The ICSI 2012 Final Program

Transcription

The Third International
Conference on Swarm
Intelligence
Technical Program & Abstracts
June 17-20, 2012
Nan Hai Hotel, Shenzhen, China
http://www.ic-si.org/
Welcome Message from General Chairs
We are warmly welcoming you, swarm intelligence researchers and practitioners from China
and all over the world, to attend The Third International Conference on Swarm Intelligence
(ICSI 2012) in Shenzhen City, which is Chinas first Special Economic Zone and a window of
Chinese reform and opening-up to the world. As a sequel of the successful Chongqing ICSI
event in 2011, Beijing ICSI event in 2010, the ICSI 2012 is the third event in its series. We
believe that you will enjoy this important and hard-to-get gathering in the community of swarm
intelligence worldwide.
Swarm Intelligence is to study the collective behavior in a decentralized system which is
made up by a population of simple individuals interacting locally with one another and with
their environment. Such systems are often found in nature, including bird flocking, ant colonies,
particles in cloud, fish schooling, bacteria foraging, animal herding, honey bees, spiders, and
sharks, to just name a few. Although there isnt typically centralized control harnessing the
behavior of the individuals, local interactions among individuals often cause a global pattern to
emerge. Modeling of these natural biological systems and social phenomenon has become one
of the most important methodologies of studying artificial intelligence from the system point
of view. The extension of swarm intelligence methods to optimizing computations, pattern
recognition, the interdisciplinary merging with robotics, control, machine learning, parallel
processing, complex systems, and etc., will generate an enormous range of research topics and
potential applications in most scientific and engineering fields.
Thanks to the hard work of the Organization Committee and the Program Committee,
the ICSI 2012 will provide you with excellent program and schedule. The informative plenary
speeches will introduce you to the frontiers of swarm intelligence research and applications,
and will help you to identify important research directions in future.
The venue of the ICSI 2012 is Nan Hai Hotel, the first 5-star hotel in Shenzhen, situated
between Hong Kong and Shenzhen. Surrounded by views of the hills of Shekou and the Pearl
River, the Nan Hai Hotel offers exquisitely furnished standard and sea view guest rooms,
spacious conference and meeting rooms as well as the Garden Square for our delegates during
our conference. You can also unwind and de-stress at the spectacular seaside pool, outdoor
tennis courts, in-house gymnasium, and other recreational facilities, etc. It is conveniently
located in the hub of an expedient transport and easily accessible from ferries running to or
from Hong Kong, Macau, and Zhuhai. There is only a 2-minute walk to nearby Shekou ferry
pier.
On the other hand, the ICSI 2012 will definitely contribute a lot to the globalization of
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The 3rd International Conference on Swarm Intelligence, June 17-20, 2012, Shenzhen, China
research and teaching in China in addition to the enhancement of the research horizons of
the conference delegates. Certainly, the participants of the ICSI 2012 can also enjoy multiple
cultures, beautiful landscapes and night scenes in Shenzhen, and the hospitality from the people
in the south of china.
On behalf of the general chairs of the ICSI 2012, I wish the ICSI 2012 will be a memorable
event for you to stay in Shenzhen, China.
Sincerely yours!
General Chairs
Ying Tan
Peking University, China
Zhen Ji
Shenzhen University, China
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Welcome Message from Program Committee Chair
The Third International Conference on Swarm Intelligence (ICSI 2012) is the third gathering
in the world, as a sequel of the ICSI 2011 (Chongqing) and ICSI 2010 (Beijing), for the
researchers working on all aspects of swarm intelligence, which provides an academic forum
for the participants to disseminate their new research findings and discuss emerging areas of
research. It also will create a stimulating environment for the participants to interact and
exchange information on future challenges and opportunities of swarm intelligence research.
The aim of this important meeting is to exhibit the state of the art research and development
in all aspects of swarm intelligence from theoretical to practical researches.
ICSI 2012 received 247 submissions and 10 invited papers from about 591 authors in 24
countries and regions (Algeria, Australia, Brazil, China, France, Hong Kong, India, Islamic
Republic of Iran, Japan, Republic of Korea, Kuwait, Macau, Malaysia, Mexico, Russian
Federation, Saudi Arabia, Singapore, South Africa, South Sudan, Chinese Taiwan, Tunisia,
Turkey, UK, USA) across six continents (Asia, Europe, North America, South America, Africa,
and Oceania). Based on rigorous reviews by the Program Committee members and reviewers,
145 high-quality papers were selected for publication in the proceedings. The papers are
organized into 27 cohesive sections covering all major topics of swarm intelligence research and
development. Besides the regular oral sessions, ICSI 2012 had one special session on Data
Fusion and Computational Intelligence.
In addition to the contributed papers, the ICSI 2012 technical program included three
plenary speeches by Xin Yao (The University of Birmingham, UK, IEEE Fellow, Vice President
of IEEE Computational Intelligence), Carlos Artemio Coello Coello (NCINVESTAV-IPNl,
Mexico, IEEE Fellow), and Guang-Bin Huang (Nanyang Technological University, Singapore).
As the Program Committee Chair of the ICSI 2012, I would like to express sincere thanks
to Shenzhen University, Peking University, and Xian Jiaotong-Liverpool University for their
sponsorship, as well as to the IEEE Computational Intelligence Society, World Federation on
Soft Computing and International Neural Network Society for their technical co-sponsorship.
Particularly, we are grateful to Springer for publishing our proceedings in the prestigious series
of Lecture Notes in Computer Science and Neurocomputing as a special issue. We appreciate
the Natural Science Foundation of China for its financial and logistic support.
On behalf of the conference organization committee, I would also like to thank the members
of the Advisory Committee for their guidance, the members of the International Program
Committee and additional reviewers for reviewing the papers, and members of the Publications
Committee for checking the accepted papers in a short period of time. Furthermore, we wish to
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express our heartfelt appreciation to the plenary speakers, session chairs, and student helpers.
In addition, there are still many more colleagues, associates, friends, and supporters who helped
us in immeasurable ways; we express our sincere gratitude to them all. Last but not the least,
we would like to thank all the speakers, authors, and participants for their great contributions
that made ICSI 2012 successful and all the hard work worthwhile.
The technical program offers an outstanding collection of recent research contributions
by top researchers. It should be of interest to both theoreticians and practitioners and is a
must-have resource for researchers interested in swarm intelligence.
We highly appreciate the three plenary speakers for delivering plenary talks. We are greatly
thankful to all the authors for their excellent contributions, to the invited session organizer
for their joint effort and enthusiasm, and to all the international program committee members
and referees for their time and expertise in the paper review process. Also, special thanks go
to Zexuan Zhu, Zhulong You, Zhongyang Zheng, Pengtao Zhang, Jiarui Zhou, Yuanchun Zhu,
Jun Wang, Xi Huang, Shaoqiu Zheng, Ke Ding and Guyue Mi for their time and outstanding
work in the organization of ICSI 2012.
We sincerely hope that all ICSI 2012 participants will enjoy attending conference sessions
and social activities, meeting research partners, and setting up new research collaborations.
Have a pleasant stay in Shenzhen and enjoy!
Cheers!
ICSI 2012 Program Committee Chair
Yuhui Shi
Xi’an Jiaotong-Liverpool University, China
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Contents
Messages
2
Welcome Message from General Chairs . . . . . . . . . . . . . . . . . .
2
Welcome Message from Program Committee Chair . . . . . . . . . . .
4
Venue
7
Sponsors
11
Committees
12
Organizing Committees
. . . . . . . . . . . . . . . . . . . . . . . . . . 12
International Program Committee Members . . . . . . . . . . . . . . . 14
Program Schedule Overview
16
Talks
17
Plenary Talk I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Plenary Talk II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Plenary Talk III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Technical Program
23
June 18, 2012(Monday) . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
June 19, 2012(Tuesday) . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Abstracts
39
Index
98
Map Overview
back endpage
6
Venue
The ICSI’2012 will be held at Nan Hai Hotel. The 5-star Nan Hai Hotel is an elegant,
396-room establishment in Shekou, ideally situated between Hong Kong and Shenzhen, gateway
to Chinas bustling business epicentre. It is conveniently located in the hub of an expedient
transport and easily accessible from ferries coming from Zhuhai or Macau; the Hong Kong
International Airport, the Railway or from the Shenzhen Baoan Airport.
Surrounded by views of the hills of Shekou and the Pearl River, the Nan Hai Hotel offers
exquisitely furnished standard and sea view guest rooms, serviced apartment units as well as
deluxe suites on the executive floors. Conference and meeting rooms are well-equipped with to
meet the needs of business travellers and meeting organisers. The Garden Square is the ideal
choice for wedding, banqueting and thematic party.
Guests can unwind and de-stress at the spectacular seaside pool, outdoor tennis courts,
in-house gymnasium or at the sauna, jacuzzi, massage and salon facilities.
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Nan Hai Hotel
Lobby
Conference Room
Restaurant
Useful Local Telephone Numbers
Country code: 86
Directory Enquiry: 114
Emergency Service: police 110, fire 119 and ambulance 999
Hotel Reservation: 755-2669-2888
Internet Access
Provide in-room braodband internet access.
Wireless access facilities in hotel lobby, restaurants and conference rooms.
8
Location Map of Basement
9
Location Map of Ground Floor
10
Sponsors
Sponsors
Shenzhen University
Peking University
Xi’an Jiaotong-Liverpool University
Financial Co-Sponsors
National Natural Science Foundation of China
Technical Co-Sponsors
IEEE
IEEE Computational Intelligence Society
World Federation on Soft Computing
International Neural Networks Society
Publishers
Lecture Notes in Computer Science
Springer
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Organizing Committees
General Chairs
Ying Tan
Peking University, Beijing, China
Zhen Ji
Shenzhen University, Shenzhen, China
Program Committee Chair
Yuhui Shi
Xi’an Jiaotong-Liverpool University, Suzhou, China
Advisory Committee Chairs
Guoliang Chen
Russell C. Eberhart
Indiana University Purdue University, IN, USA
Technical Committee Chairs
Zexuan Zhu
Qinghua Wu
University of Liverpool, Liverpool, UK
Kalyanmoy Deb
Indian Institute of Technology, Kanpur, India
Andries Engelbrecht
University of Pretoria Pretoria, South Africa
Ram Akella
University of California, Santa Cruz, USA
José Alfredo Ferreira Costa
Federal University, Brazil
Plenary Session Chairs
Martin Middendorf
University of Leipzig, Leipzig, Germany
Jun Zhang
Sun Yat-Sen University, Guangzhou, China
Special Session Chairs
Shan He
University of Birmingham, UK
Xiaodong Li
RMIT Unversity, Melboune, Australia
Publication Chairs
Radu-Emil Precup
Politehnica University of Timisoara, Timisoara, Romania
Zhishun Wang
Columbia University, New York, USA
Publicity Chairs
Eugene Santos Jr.
Thayer School of Engineering at Dartmouth, Hanover, USA
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Yew-Soon Ong
Nanyang Technological University, Singapore, Singapore
Juan Luis Fernandez
University of Oviedo, Oviedo, Spain
Martinez
Fernando Buarque
Universidade of Pernambuco, Brazil
Zhuhong You
Finance and Registration Chairs
Chao Deng
Peking University, Beijing, China
Andreas Janecek
University of Vienna, Vienna, Austria
Local Arrangement Chair
Jiarui Zhou
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International Program Committee Members
Ram Akella, University of California, USA
Payman Arabshahi, University of Washington, USA
Sabri Arik, Istanbul University, Turkey
Carmelo J. A. Bastos Filho, University of Pernambuco, Brazil
Christian Blum, Universitat Politecnica de Catalunya, Spain
Salim Bouzerdoum, University of Wollongong, Australia
Walter W. Chen, National Taipei University of Technology, Taiwan
Manuel Chica, European Centre for Soft Computing, Spain
Leandro Coelho, Pontifı́cia Universidade Católica do Parana, Brazil
Carlos A. Coello Coello, CINVESTAV-IPN, Mexico
José Alfredo Ferreira Costa, UFRN - Universidade Federal do Rio Grande do Norte, Brazil
Prithviraj Dasgupta, University of Nebraska, USA
Kalyanmoy Deb, Indian Institute of Technology, India
Kusum Deep, Indian Institute of Technology Roorkee, India
Mingcong Deng, Tokyo University of Agriculture and Technology, Japan
Yongsheng Ding, Donghua University, China
Haibin Duan, Beijing University of Aeronautics and Astronautics, China
Mark Embrechts, RPI, USA
Andries Engelbrecht, University of Pretoria, South Africa
Pan Feng, Beijing University of Technology, China
Yoshikazu Fukuyama, Fuji Electric Systems Co., Ltd. Japan
Wai-Keung Fung, University of Manitoba, Canada
Beatriz Aurora Garro Licon, CIC-IPN, Mexico
Dunwei Gong, China University of Mining and Technology, China
Ping Guo, Beijing Normal University, China
Walter Gutjahr, University of Vienna, Austria
Mo Hongwei, Harbin Engineering University, China
Jun Hu, Chinese Academy of Sciences, China
Guangbin Huang, Nanyang Technological University, Singapore
Hisao Ishibuchi, Osaka Prefecture University, Japan
Andreas Janecek, University of Vienna, Austria
Zhen Ji, Shenzhen University, China
Changan Jiang, Kagawa University, Japan
Colin Johnson, University of Kent, USA
Farrukh Khan, FAST-NUCES Islamabad, Pakistan
Arun Khosla, National Institute of Tech.Jalandhar,Punjab, India
David King, Nottingham Trent University, UK
Thanatchai Kulworawanichpong, Suranaree University of Technology, Thailand
Germano Lambert-Torres, Itajuba Federal University, Brazil
Xia Li, Shenzhen University, China
Xiaodong Li, RMIT University, Australia
Yangmin Li, University of Macau, China
Jane Liang, Zhengzhou University, China
Andrei Lihu, Politehnica University, Timisoara
Fernando B. De Lima Neto, University of Pernambuco, Brazil
Ju Liu, Shandong University, China
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Qun Liu, Chongqing University of Posts and Communications, China
Wenlian Lu, Fudan University, China
Xiaoqiang Lu, Dalian University of Technology, China
Wenjian Luo, University of Science and Technology of China, China
Jinwen Ma, Peking university, China
Xiujun Ma, Peking University, China
Juan Luis Fernandez Martinez, University of California Berkeley, USA
Bernd Meyer, Monash University, Australia
Martin Middendorf, University of Leipzig, Germany
Mahamed G. H. Omran, Gulf University for Science and Technology, Kuwait
Thomas Potok, Oak Ridge National Laboratory, USA
Radu-Emil Precup, Politehnica University of Timisoara, Romania
Yuhui Shi, Xi’an Jiaotong-Liverpool University, China
Michael Small, Hong Kong Polytechnic University, China
Ponnuthurai Suganthan, Nanyang Technological University, Singapore
Norikazu Takahashi, Kyushu University, Japan
Kay Chen Tan, National University of Singapore, Singapore
Ying Tan, Peking University, China
Peter Tino, University of Birmingham, UK
Christos Tjortjis, The University of Manchester, UK
Frans Van Den Bergh, CSIR SAC (Pretoria), South Africa
Bing Wang, University of Hull, UK
Guoyin Wang, Chongqing University of Posts and Telecommunications, China
Jiahai Wang, Sun Yat-sen University, China
Lei Wang, Tongji University, China
Ling Wang, Tsinghua University, China
Lipo Wang, Nanyang Technological University, Singapore
Qi Wang, Xi’an Institute of Optics and Precision Mechanics Of CAS, China
Hongxing Wei, Beihang University, China
Shunren Xia, Zhejiang University, China
Zuo Xingquan, Beijing University of Posts and Telecommunications, China
Ning Xiong, Mälardalen University, Sweden
Benlian Xu, Changshu Institute of Technology, China
Xinshe Yang, National Physical Laboratory, UK
Yingjie Yang, De Montfort University, UK
Gary Yen, Oklahoma State University, USA
Hoengpeng Yin, Chongqing University, China
Peng-Yeng Yin, National Chi Nan University, China
Jie Zhang, Newcastle University, UK
Jun Zhang, Waseda University, Japan
Jun Zhang, Sun Yat University, China
Junqi Zhang, Tongji University, China
Lifeng Zhang, Renmin University of China, China
Qieshi Zhang, Waseda University, Japan
Qingfu Zhang, University of Essex, UK
Yanqing Zhang, Georgia State University, USA
Dongbin Zhao, Chinese Academy of Science, China
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Program Schedule Overview
Date
Time
June 17, 2012
10:00-18:00
Events (At Nan Hai Hotel)
Registration Day
(Hotel Lobby)
June 18, 2012
08:00-08:15
Opening Ceremony
(China Merchant Hall)
08:20-12:00
Plenary Talks
(China Merchant Hall)
12:00-13:30
Lunch
(Patio Coffee Shop)
13:30-17:45
Parallel Oral Sessions
(Financial Room, Commercial Room, Coffee Shop VIP Room)
18:30-21:00
Banquet
(Multi-functions Hall, Haojing Seafood Restaurant (Times Plaza
Branch), about 300 meters away from Nanhai Hotel)
June 19, 2012
08:00-12:00
12:00-13:30
Lunch
(Patio Coffee Shop)
13:30-17:30
June 20, 2012
08:00-18:00
City Tour
(Post-conference Excursions
Hong Kong City Tour or Shenzhen City Tour)
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Plenary Talk I
Cooperatively Coevolving Particle Swarms for Large Scale Optimization
Prof. Xin Yao
The University of Birmingham, UK
Abstract
This talk presents a new cooperative coevolving particle swarm optimization (CCPSO) algorithm
in an attempt to address the issue of scaling up particle swarm optimization (PSO) algorithms
in solving large-scale optimization problems (up to 2000 real-valued variables). The proposed
CCPSO2 builds on the success of an early CCPSO that employs an effective variable grouping
technique random grouping. CCPSO2 adopts a new PSO position update rule that relies
on Cauchy and Gaussian distributions to sample new points in the search space, and a
scheme to dynamically determine the coevolving subcomponent sizes of the variables. On
high-dimensional problems (ranging from 100 to 2000 variables), the performance of CCPSO2
compared favorably against state-of-the-art evolutionary algorithms, including CMA-ES, PSO
and DE, especially on complex multi-modal problems. Our experimental results and analysis
suggest that CCPSO2 is a highly competitive optimization algorithm for solving large-scale
and complex multimodal optimization problems.
Biography
Xin Yao is a Professor (Chair) of Computer Science at the University
of Birmingham, UK. He obtained his BSc from the University of Science
and Technology of China (USTC) in Hefei, China, in 1982, MSc from
the North China Institute of Computing Technology in Beijing, China, in
1985, and PhD from USTC in Hefei, China, in 1990.
He was a postdoctoral research fellow at the Australian National
University (ANU) in Canberra in 1990-91 and at CSIRO Division of Building, Construction
and Engineering in Melbourne in 1991-92. He was a lecturer, senior lecturer and an associate
professor at the University College, the University of New South Wales (UNSW), the Australian
Defence Force Academy (ADFA) in Canberra in 1992-99. He took up a Chair of Computer
Science at the University of Birmingham, UK, on the April Fool’s Day in 1999.
Currently he is the Director of CERCIA (the Centre of Excellence for Research in Computational
Intelligence and Applications) at the University of Birmingham, UK, a Distinguished Visiting
Professor of the University of Science and Technology of China in Hefei, China, and a visiting
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professor of three other universities. He is an IEEE Fellow and a Distinguished Lecturer of
IEEE Computational Intelligence Society. He won the 2001 IEEE Donald G. Fink Prize Paper
Award and several other best paper awards. In his spare time, he does the voluntary work
as the editor-in-chief of IEEE Transactions on Evolutionary Computation, an associate editor
or editorial board member of several other journals, and the editor of the World Scientific
book series on ”Advances in Natural Computation”. He has been invited to give more than 60
invited keynote and plenary speeches at conferences and workshops in 16 different countries.
He is a Cheung Kong Scholar (Changjiang Chair Professor) of the Ministry of Education of
the People’s Republic of China.
His research has been well supported by research councils, government organisations and
industry.
His major research interests include evolutionary computation, neural network
ensembles, and their applications. He has more than 300 refereed publications.
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Plenary Talk II
Multi-Objective Particle Swarm Optimizers: Past, Present and Future
Prof. Carlos Artemio Coello Coello
CINVESTAV-IPNl, Mexico
Abstract
This talk will provide a review of the research that has been conducted on the use of particle
swarm optimization for solving multi-objective problems. The talk will emphasize the main
algorithmic developments that have been reported in the specialized literature, and will include
a brief analysis of some of the most representative multi-objective particle swarm optimizers
(MOPSOs) that have been proposed so far. The talk will also include some of the real-world
applications of these MOPSOs and will conclude with some of the potential paths for future
research in this area.
Biography
Carlos Artemio Coello Coello received a BSc in Civil Engineering from
the Universidad Autonoma de Chiapas in Mexico in 1991 (graduating
Summa Cum Laude).
Then, he was awarded a scholarship from the
Mexican government to pursue graduate studies in Computer Science
at Tulane University (in the USA). He received a MSc and a PhD in
Computer Science in 1993 and 1996, respectively. His PhD thesis was one
of the first in the field now called “evolutionary multiobjective optimization”.
Dr. Coello has been a Senior Research Fellow in the Plymouth Engineering Design Centre
(in England) and a Visiting Professor at DePauw University (in the USA). He is currently
full professor (Investigador Cinvestav 3F) and Chair of the Computer Science Department at
CINVESTAV-IPN in Mexico City, Mexico. He has published over 250 papers in international
peer-reviewed journals and conferences. He has also co-authored the book “Evolutionary
Algorithms for Solving Multi-Objective Problems” which is now in its second edition (Springer,
New York, 2007) and has co-edited the book “Applications of Multi-Objective Evolutionary
Algorithms” (World Scientific, 2004). He has delivered invited talks, keynote speeches and
tutorials at international conferences held in Spain, USA, Canada, Switzerland, UK, Chile,
Colombia, Brazil, Uruguay, Argentina, India, Italy, China and Mexico.
Dr. Coello has served as a technical reviewer for over 60 international journals and for
more than 100 international conferences and actually serves as associate editor of the journals
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“IEEE Transactions on Evolutionary Computation”, “Evolutionary Computation”, “Journal of
Heuristics”, “Soft Computing”, “Pattern Analysis and Applications”, “Memetic Computing”
and “Computational Optimization and Applications”, and as a member of the editorial boards
of the journals “Engineering Optimization”, and the “International Journal of Computational
Intelligence Research”. He is member of the Mexican Academy of Science, the Association
for Computing Machinery, and of Sigma Xi, The Scientific Research Society.
He is also
amember of the National System of Researchers (Level 3). As of January, 2011, he is also
an IEEE Fellow for his “contributions to multi-objective optimization and constraint-handling
techniques”. He received the 2007 National Research Award from the Mexican Academy of
Science in the area of exact sciences. His work currently reports over 5,400 citations (his
h-index is 40). His current research interests are: evolutionary multiobjective optimization
and constraint-handling techniques for evolutionary algorithms.
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Plenary Talk III
Extreme Learning Machine: Towards Tuning-Free Learning
Prof. Guang-Bin Huang
Nanyang Technological University, Singapore
Abstract
Neural networks (NN) and support vector machines (SVM) play key roles in machine learning
and data analysis. However, it is known that these popular learning techniques face some
challenging issues such as: intensive human intervene, slow learning speed, poor learning
scalability. This talk will introduce a new learning technique referred to as Extreme Learning
Machine (ELM). ELM not only learns up to tens of thousands faster than NN and SVMs,
but also provides unified implementation for regression, binary and multi-class applications.
ELM not only produces good results for sparse datasets but also is efficient for large size
of applications. From both theoretical and practical points of view, NN and SVM/LS-SVM
only produce suboptimal solutions to ELM. ELM is efficient to time series, online sequential,
incremental applications. This talk will also study the feasibility of efficient marriage between
Extreme Learning Machines and Swarm Intelligence.
Biography
Guang-Bin Huang received the B.Sc degree in applied mathematics
and M.Eng degree in computer engineering from Northeastern University,
P. R. China, in 1991 and 1994, respectively, and Ph.D degree in electrical
engineering from Nanyang Technological University, Singapore in 1999.
During undergraduate period, he also concurrently studied in Applied
Mathematics department and Wireless Communication department of
Northeastern University, P. R. China.
From June 1998 to May 2001, he worked as Research Fellow in Singapore Institute of
Manufacturing Technology (formerly known as Gintic Institute of Manufacturing Technology)
where he has led/implemented several key industrial projects (e.g., Chief architect/designer
and technical leader of Singapore Changi Airport Cargo Terminal Upgrading Project, etc).
From May 2001, he has been working as an Assistant Professor and Associate Professor
(with tenure) in the School of Electrical and Electronic Engineering, Nanyang Technological
University. His current research interests include machine learning, computational intelligence,
extreme learning machine, pattern recognition, games, and remanufacturing. He has published
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15 full-length papers in IEEE Transactions and received 1400 SCI citations over his work.
He was Program Chair of IEEE TENCON2009 (IEEE Region 10 flagship conference with
550+ registered participants). He serves as an Associate Editor of Neurocomputing and IEEE
Transactions on Systems, Man and Cybernetics - Part B. He is a senior member of IEEE.
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Technical Program
June 18, 2012(Monday)
08:00-08:15
Opening Ceremony
China Merchant Hall
Plenary Talk I
08:20-09:25
Speaker: Prof. Xin Yao
China Merchant Hall
Chair: Prof. Zhen Ji
Main Entrance of
09:25-09:45
Tea/Coffee Break
China Merchant Hall
Plenary Talk II
09:45-10:50
Speaker: Prof. Carlos Artemio
China Merchant Hall
Coello Coello
Chair: Prof. Yuhui Shi
Plenary Talk III
10:55-12:00
Speaker: Prof. Guang-Bin Huang
China Merchant Hall
Chair: Prof. Xin Yao
12:00-13:30
Lunch
Patio Coffee Shop
13:30-15:30
Oral Sessions
Conference Rooms
Financial Room
Commercial Room
Coffee Shop VIP Room
Particle Swarm
Applications of PSO
Novel Swarm-based
Optimization
Algorithms
Optimization Algorithms
15:30-15:45
Tea/Coffea Break
Corridor
15:45-17:45
Oral Sessions
Conference Rooms
Financial Room
Commercial Room
Multi-robot, Swarm-robot
Swarm Intelligence based
Ant and Bee Colony
and Multi-agent Systems
Algorithms
18:30-21:00
Banquet
Haojing Seafood Restaurant
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June 19, 2012(Tuesday)
08:00-10:00
Oral Sessions
Conference Rooms
Financial Room
Commercial Room
Differential Evolution and
Hybrid Algorithms
Multi-Objective
Genetic Algorithms
10:00-10:15
Tea/Coffea Break
Corridor
10:15-12:15
Oral Sessions
Conference Rooms
Financial Room
Commercial Room
Machine Learning and Data
Machine Learning and
Special Session on Data
Mining Methods (I)
Data Mining Methods (II)
Fusion and Computational
Intelligence
12:00-13:30
Lunch
Patio Coffee Shop
13:30-15:15
Oral Sessions
Conference Rooms
Financial Room
Commercial Room
Scheduling and Path
Visual Simulation and
Biometrics and Information
Planning
Parallel Implementation
Security
15:15-15:30
Tea/Coffea Break
Corridor
15:30-17:30
Oral Sessions
Conference Rooms
Financial Room
Commercial Room
Other Applications
Mathematics
Artificial Immune System
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Oral Sessions
June 18, 2012(Monday)
Session
Particle Swarm Optimization
Chair C. J. A. Bastos Filho
Co-Chair Penchen Chou
Date/Time
13:30 - 13:45
June 18, 2012(Monday) 13:30-15:15
Venue Financial Room
Grey-based Particle Swarm Optimization Algorithm
P39
Ming-Feng Yeh, Cheng Wen and Min-Shyang Leu
13:45 - 14:00
Exponential Inertia Weight for Particle Swarm Optimization
P39
T.O. Ting, Yuhui Shi, Shi Cheng and Sanghyuk Lee
14:00 - 14:15
A Coevolutionary Memetic Particle Swarm Optimizer
P40
Jiarui Zhou, Zhen Ji, Zexuan Zhu and Siping Chen
14:15 - 14:30
Improved Particle Swarm Optimization with Wavelet-based Mutation
P40
Operation
Yubo Tian, Donghui Gao and Xiaolong Li
14:30 - 14:45
Elastic Boundary for Particle Swarm Optimization
P41
Yuhong Chi, Fuchun Sun, Langfan Jiang, Chunming Yu and Ping
Zhang
14:45 - 15:00
High-dimension Optimization Problems Using Specified Particle Swarm
P41
Optimization
Penchen Chou
15:00 - 15:15
A Mechanism Based on Artificial Bee Colony to Generate Diversity in
P42
L. N. Vitorino, S. F. Ribeiro and C. J. A. Bastos Filho
Session
Applications of PSO Algorithms
Chair M.W. Siti
Co-Chair Walter Chen
Date/Time
13:30 - 13:45
June 18, 2012(Monday) 13:30-15:15
Venue Commercial Room
Optimization Locations of Wind Turbines with the Particle Swarm
Optimization
Ming-Tang Tsai and Szu-Wzi Wu
25
P42
13:45 - 14:00
Training ANFIS Parameters with A Quantum-Behaved Particle Swarm
P43
Optimization Algorithm
Xiufang Lin, Jun Sun, Vasile Palade, Wei Fang, Xiaojun Wu and
Wenbo Xu
14:00 - 14:15
Research on Improved Model of Loans Portfolio Optimization Based
P43
on Adaptive Particle Swarm Optimization Algorithm
Ying Sun and Yuelin Gao
14:15 - 14:30
Particle Swarm Optimization for Power System State Estimation
P43
D.H. Tungadio, B.P. Numbi, M.W. Siti and A.A. Jimoh
14:30 - 14:45
DPSO based on Min-max Approach and Clamping Strategy for the
P44
Evacuation Vehicle Assignment Problem
Marina Yusoff, Junaidah Ariffin and Azlinah Mohamed
14:45 - 15:00
Scripting STABL with PSO for Analysis of Slope Stability
P44
Zheping Shen, Jianan Wang, Walter Chen and Fuan Tsai
15:00 - 15:15
Feature Selection Algorithm Based on Bare Bones Particle Swarm
P45
Optimization
Yong Zhang, Dunwei Gong, Ying Hu and Wanqiu Zhang
Session
Novel
Swarm-based
Optimization
Algorithms
Chair Mohammad Reza Amin
Naseri
Co-Chair Lei Wang
Date/Time
13:30 - 13:50
June 18, 2012(Monday) 13:30-15:30
Venue Coffee Shop VIP Room
Unconscious Search - A New Structured Search Algorithm for Solving
P45
Continuous Engineering Optimization Problems Based on the Theory
of Psychoanalysis
Ehsan Ardjmand and Mohammad Reza Amin Naseri
13:50 - 14:10
Brain Storm Optimization Algorithm with Modified Step-size and
P46
Individual Generation
Dadian Zhou, Yuhui Shi and Shi Cheng
14:10 - 14:30
Group Search Optimizer For Power System Economic Dispatch
Huilian Liao, Haoyong Chen, Qinghua Wu, Masoud Bazargan and
Zhen Ji
26
P46
14:30 - 14:50
Cloud Droplets Evolutionary Algorithm on Reciprocity Mechanism for
P47
Function Optimization
Lei Wang, Wei Li, Rong Fei and Xinghong Hei
14:50 - 15:10
A Filter Fan Based Algorithm for Slab Rehandling Problem in MPA of
P47
Steel Industry
Xu Cheng and Lixin Tang
15:10 - 15:30
A Quantum-inspired Bacterial Swarming Optimization Algorithm for
P48
Discrete Optimization Problems
Jinlong Cao and Hongyuan Gao
Session
Date/Time
15:45 - 16:00
Multi-robot,
Swarm-robot
and
Chair Hongxin Wei
Multi-agent Systems
Co-Chair Zhiguo Shi
June 18, 2012(Monday) 15:45-17:45
Priority Based Multi Robot Task Assignment
P48
Rahul Goyal, Tushar Sharma and Ritu Tiwari
16:00 - 16:15
A Survey of Swarm Robotics System
P49
Zhiguo Shi, Jun Tu, Qiao Zhang, Lei Liu and Junming Wei
16:15 - 16:30
Levels of Realism for Cooperative Multi-agent Reinforcement Learning
P49
Bryan Cunningham and Yong Cao
16:30 - 16:45
Research of Tourism Service System Base on Multi-Agent Negotiation
P50
Youqun Shi, Cheng Tang, Henggao Wu and Xinyu Liu
16:45 - 17:00
Distributed Model Predictive Control of the Multi-agent Systems with
P50
Communication Distance Constraints
Shanbi Wei, Yi Chai, Hongpeng Yin and Penghua Li
17:00 - 17:15
Research on HumanRobot Collaboration in Rescue Robotics
P51
Haibo Tong, Rubo Zhang and Guanqun Liu
17:15 - 17:30
Co-evolution Framework of Swarm Self-assembly Robots
P51
Haiyuan Li, Hongxing Wei, Jiangyang Xiao and Tianmiao Wang
17:30 - 17:45
Superadditive Effect of Multi-robot coordination in the Exploration of
Unknown Environments via Stigmergy
T”uze Kuyucu, Ivan Tanev and Katsunori Shimohara
27
P51
Session
Swarm Intelligence based Algorithms
Chair Komla A Folly
Co-Chair Ben Niu
Date/Time
15:45 - 16:00
June 18, 2012(Monday) 15:45-17:30
Population-Based Incremental with Adaptive Learning Rate Strategy
P52
Komla A Folly
16:00 - 16:15
A SI-based Algorithm for Structural Damage Detection
P52
Ling Yu, Peng Xu and Xi Chen
16:15 - 16:30
Swarm Intelligence in Cloud Environment
P53
Anirban Kundu and Chunlin Ji
16:30 - 16:45
Swarm Intelligence Supported e-Remanufacturing
P53
Bo Xing, Wenjing Gao, Fulufhelo V. Nelwamando, Kimberly Battle
and Tshilidzi Marwala
16:45 - 17:00
Bacterial-inspired Algorithms for Solving Constrained Optimization
P53
Problems
Ben Niu and Jingwen Wang
17:00 - 17:15
A New Model to Imitate the Foraging Behavior of Physarum
P54
Polycephalum in Nutrient-poor Substrate
Yuheng Wu, Zili Zhang, Yong Deng, Huan Zhou and Tao Qian
17:15 - 17:30
Mining the Role-oriented Process Models Based on Genetic Algorithm
P54
Weidong Zhao, Qinhe Lin, Yue Shi and Xiaochun Fang
Session
Ant and Bee Colony Optimization
Chair Michiharu Maeda
Algorithms
Date/Time
15:45 - 16:05
June 18, 2012(Monday) 15:45-17:45
Ant Colony Algorithm for Surgery Scheduling Problem
P55
Jiao Yin and Wei Xiang
16:05 - 16:25
A Method for Avoiding the Feedback Searching Bias in Ant Colony
P55
Optimization
Bolun Chen and Ling Chen
16:25 - 16:45
Node-based
Pheromone
Updating
Strategy
Optimization with Pheromone Marks
Xiangyang Deng, Limin Zhang and Lan Luo
28
for
Ant
Colony
P56
16:45 - 17:05
A Web-Service for Automated Software Refactoring Using Artificial
P56
Bee Colony Optimization
Ekin Koc, Nur Ersoy, Zelal Seda Camlidere and Hurevren Kilic
17:05 - 17:25
Reduction of Artificial Bee Colony Algorithm for Global Optimization
P57
Michiharu Maeda and Shinya Tsuda
17:25 - 17:45
A Novel Simple Candidate Set Method for Symmetric TSP and Its
Application in MAX-MIN Ant System
Miao Deng, Jihong Zhang, Yongsheng Liang, Guangming Lin and Wei
Liu
29
P57
June 19, 2012(Tuesday)
Session
Differential Evolution and Genetic
Chair Chia-Chun Hsu
Algorithms
Date/Time
08:00 - 08:15
June 19, 2012(Tuesday) 08:00-09:45
Self-configuring Genetic Algorithm with Modified Uniform Crossover
P58
Operator
Eugene Semenkin and Maria Semenkina
08:15 - 08:30
A Genetic Algorithm for the Maximum Edge-disjoint Paths Problem
P58
Chia-Chun Hsu and Hsun-Jung Cho
08:30 - 08:45
Ensemble
of
Clearing
Differential
Evolution
for
Multi-modal
P59
Optimization
Boyang Qu, Jing Liang, Ponnuthurai Nagaratnam Suganthan and
Tiejun Chen
08:45 - 09:00
Fast Mixed Strategy Differential Evolution Using Effective Mutant
P59
Vector Pool
Hao Liu, Han Huang, Yingjun Wu and Zhenhua Huang
09:00 - 09:15
Differential Annealing for Global Optimization
P60
Yongwei Zhang, Lei Wang and Qidi Wu
09:15 - 09:30
Image Retrieval Based on GA Integrated Color Vector Quantization
P60
and Curvelet Transform
Yungang Zhang, Tianwei Xu and Wei Gao
09:30 - 09:45
A Novel Fitness Allocation Algorithm for Maintaining a Constant
P61
Selective Pressure During GA Procedure
Li Feng Zhang, Chen Xi Zhou, Rong He, Yuan Xu and Meng Ling Yan
Session
Hybrid Algorithms
Chair Zexuan Zhu
Date/Time
June 19, 2012(Tuesday) 08:00-10:00
08:00 - 08:15
Memetic
Three-Dimensional
Gabor
Feature
Extraction
Hyperspectral Imagery Classification
Zexuan Zhu, Linlin Shen, Yiwen Sun, Shan He and Zhen Ji
30
for
P61
08:15 - 08:30
A Novel Two-Level Hybrid Algorithm for Multiple Traveling Salesman
P62
Problems
Qingsheng Yu, Dong Wang, Dongmei Lin, Ya Li and Chen Wu
08:30 - 08:45
Hybrid
Particle
Swarm
Optimization
for
Preemptive
P62
Resource-constrained Project Scheduling
Yongyi Shou, Ying Li and Changtao Lai
08:45 - 09:00
Discovery Scientific Laws by Hybrid Evolutionary Model
P63
Fei Tang, Sanfeng Chen, Xu Tan, Tao Hu, Guangming Lin and Zuo
Kang
09:00 - 09:15
A Hybrid Fireworks Optimization Method with Differential Evolution
P63
Operators
Yujun Zheng, Xinli Xu and Haifeng Ling
09:15 - 09:30
Evolving RBF Neural Networks for Rainfall Prediction Using Hybrid
P64
Particle Swarm Optimization and Genetic Algorithm
Jiansheng Wu, Jin Long and Mingzhe Liu
09:30 - 09:45
Evolving Neural Network Using Hybrid Genetic Algorithm and
P64
Simulated Annealing for Rainfall-runoff Forecasting
Hong Ding, Jiansheng Wu and Xianghui Li
09:45 - 10:00
Evolutionary Three-Stage Approach for Designing of Neural Networks
P65
Ensembles for Classification Problems
Vladimir Bukhtoyarov and Eugene Semenkin
Session
Multi-Objective
Optimization
Chair Yuhui Shi
Algorithms
Date/Time
08:00 - 08:15
June 19, 2012(Tuesday) 08:00-09:45
On the Performance Metrics of Multiobjective Optimization
P66
Shi Cheng, Yuhui Shi and Quande Qin
08:15 - 08:30
Brain Storm Optimization Algorithm for Multi-objective Optimization
P66
Problems
Jingqian Xue, Yali Wu, Yuhui Shi and Shi Cheng
08:30 - 08:45
Modified Multi-objective Particle Swarm Optimization Algorithm for
Multi-objective Optimization Problems
Ying Qiao
31
P67
08:45 - 09:00
A Multi-Objective Mapping Strategy forApplication Specific Emesh
P67
Network-on-Chip NoC
Bixia Zhang, Huaxi Gu, Sulei Tian and Bin Li
09:00 - 09:15
Binary Nearest Neighbor Classification of Predicting Pareto Dominance
P68
in Multi-objective Optimization
Guanqi Guo, Cheng Yin, Taishan Yan and Wenbin Li
09:15 - 09:30
Multi-objective Evolutionary Algorithm Based on Layer Strategy
P68
Sen Zhao, Zhifeng Hao, Shusen Liu, Weidi Xu and Han Huang
09:30 - 09:45
An Improved MOPSO with a Crowding Distance Based External
P69
Archive Maintenance Strategy
Weixing Li, Qian Zhou, Yu Zhu and Feng Pan
Session
Date/Time
10:15 - 10:30
Machine Learning and Data Mining
Chair Naser Mozayani
Methods (I)
Co-Chair Amira Aloui
June 19, 2012(Tuesday) 10:15-12:00
An Automatic Learning System to Derive Multipole and Local
P69
Expansions for the Fast Multipole Method
Seyed Naser Razavi, Nicolas Gaud, Abderrafiâa Koukam and Naser
Mozayani
10:30 - 10:45
Automatic Scoring on English Passage Reading Quality
P70
Junbo Zhang, Fuping Pan and Yongyong Yan
10:45 - 11:00
An Expandable Recommendation System on IPTV
P70
Jie Xiao and Liang He
11:00 - 11:15
Intrinsic
Protein
Distribution
on
Manifolds
Embedded
in
P70
A Novel Approach to Modelling Protein-Protein Interaction Networks
P71
Low-dimensional Space
Wei-Chen Cheng
11:15 - 11:30
Zhuhong You, Yingke Lei, Zhen Ji and Zexuan Zhu
11:30 - 11:45
Flock by Leader A Novel Machine Learning Biologically Inspired
P71
Clustering Algorithm
Abdelghani Bellaachia and Anasse Bari
11:45 - 12:00
Cluster KDD: A Visual Clustering and Knowledge Discovery Platform
based on Concept Lattice
Amel Grissa Touzi, Amira Aloui and Rim Mahouachi
32
P72
Session
Date/Time
10:15 - 10:30
Machine Learning and Data Mining
Chair R.A. Vázquez
Methods (II)
Co-Chair T.O. Ting
June 19, 2012(Tuesday) 10:15-12:15
Design and Implementation of an Intelligent Automatic Question
P72
Answering System Based on Data Mining
Zhe Qu and Qin Wang
10:30 - 10:45
Ontology-based Hazard Information Extraction from Chinese Food
P72
Complaint Documents
Xiquan Yang, Rui Gao, Zhengfu Han and Xin Sui
10:45 - 11:00
Design Similarity Measure and Application to Fault Detection of
P73
Lateral Directional Mode Flight System
WookJe Park, Sangmin Lee, Sanghyuk Lee and T.O. Ting
11:00 - 11:15
The
Effects
of
Customer
Perceived
Disposal
Hardship
on
P73
Post-Consumer Product Remanufacturing A Multi-Agent Perspective
Bo Xing, Wen-jing Gao, Fulufhelo V Nelwamondo, Kimberly Battle
11:15 - 11:30
Research on Domain-Specific Features Clustering Based Spectral
P74
Clustering
Xiquan Yang, Meijia Wang, Lin Fang, Lin Yue and Yinghua Lv
11:30 - 11:45
Knowledge Annotation Framework Oriented Geospatial Semantic Web
P74
Service Management
Rupeng Liang, Hongwei Li, Jian Chen, Leilei Ma and Hu Chen
11:45 - 12:00
Optimizing Supplier Selection with Disruptions by Chance-Constrained
P75
Programming
Wenjuan Zang, Yankui Liu and Zhenhong Li
12:00 - 12:15
Tuning the Parameters of an Integrate and Fire Neuron via a Genetic
Algorithm for Solving Pattern Recognition Problems
A. Cachón and R.A. Vázquez
33
P75
Session
Special Session on Data Fusion and
Chair Benlian Xu
Computational Intelligence
Date/Time
10:15 - 10:35
June 19, 2012(Tuesday) 10:15-12:15
Quantized Steady-state Kalman Filter in a Wireless Sensor Network
P76
Changcheng Wang, Guoqing Qi, Yinya Li and Andong Sheng
10:35 - 10:55
A Multiple Shape-target Tracking Algorithm by Using MCMC
P76
Sampling
Weifeng Liu, Zhong Chai and Chenglin Wen
10:55 - 11:15
Modified UDP-Based Semi-Supervised Learning for Fruit Internal
P77
Quality Detection
Peiyi Zhu, Benlian Xu and Jue Gao
11:15 - 11:35
Research Progress of a Novel Hybrid 3G-VHF Communication System
P77
over Maritime Buoys
Xiaoying Wang, Yingge Chen and Benlian Xu
11:35 - 11:55
Cell Automatic Tracking Technique with Particle Filter
P77
Mingli Lu, Benlian Xu and Andong Sheng
11:55 - 12:15
Ocean Buoy Communication Node Selection Strategy with Intelligent
P78
Ant Behavior
Benlian Xu, Qinglan Chen, Wan Shi and Xiaoying Wang
Session
Scheduling and Path Planning
Chair Hongwei Mo
Co-Chair Junghoon Lee
Date/Time
13:30 - 13:40
June 19, 2012(Tuesday) 13:30-15:10
Distributed Rate Allocation for Multi-path Routing Based on Network
P78
Utility Maximization
Youjun Bu, Wei He, Kunpeng Jiang and Binqiang Wang
13:40 - 13:50
Integration of Battery Charging to Tour Schedule Generation for An
P79
EV-based Rent-a-car Business
Junghoon Lee, Hye-Jin Kim and Gyung-Leen Park
13:50 - 14:00
A Scalable Algorithm for Finding Delay-constraint Least-cost
End-to-end Path
Yue Han, Zengji Liu, Mingwu Yao and Jungang Yang
34
P79
14:00 - 14:10
Resolving Single Depot Vehicle Routing Problem with Artificial Fish
P79
Swarm Algorithm
Zhi Li, Haixiang Guo, Longhui Liu, Juan Yang and Peng Yuan
14:10 - 14:20
Research of Biogeography Particle Swarm Optimization for Robot Path
P80
Planning
Hongwei Mo and Lifang Xu
14:20 - 14:30
Path Planning Based on Voronoi Diagram and Biogeography-based
P80
Optimization
Ning Huang, Gang Liu and Bing He
14:30 - 14:40
A New Probability Model for Insuring Critical Path Problem with
P81
Heuristic Algorithm
Zhenhong Li, Yankui Liu and Guoqing Yang
14:40 - 14:50
Memetic Differential Evolution for Vehicle Routing Problem with Time
P81
Windows
Wanfeng Liu, Xu Wang and Xia Li
14:50 - 15:00
The Application of a Hybrid Algorithm to the Submersible
P82
Path-Planning
Chongyang Lv, Fei Yu, Na Yang, Jin Feng and Meikui Zou
15:00 - 15:10
An Improved Bean Optimization Algorithm for Solving TSP
P82
Xiaoming Zhang, Kang Jiang, Hailei Wang, Wenbo Li and Bingyu Sun
Session
Visual
Simulation
and
Parallel
Chair Qing He
Implementation
Date/Time
13:30 - 13:45
June 19, 2012(Tuesday) 13:30-15:15
Application of Visual Simulation in Building Marine Engine Room
P83
Simulator
Yelan He and Hui Chen
13:45 - 14:00
Development of Visual Action Design Environment for Intelligent Toy
P83
Robot
Jianqing Mo, Hanwu He and Hong Zhang
14:00 - 14:15
A Robust Adaptive Filter Estimation Algorithm for Vision-based
Cooperative Motions of Unmanned Aerial Vehicle
Chaoxu Li, Zhong Liu, Zhihua Gao and Xuesong Li
35
P83
14:15 - 14:30
Design and Implement of a CUDA Based SPH Particle System Editor
P84
Xianjun Chen and Yongsong Zhan
14:30 - 14:45
Implementations of Main Algorithms for Generalized Eigenproblem on
P84
GPU Accelerator
Yonghua Zhao, Jian Zhang and Xuebin Chi
14:45 - 15:00
Parallel Max-Min Ant System Using MapReduce
P85
Qing Tan, Qing He and Zhongzhi Shi
15:00 - 15:15
Parallel Implementation of Ant-based Clustering Algorithm Based on
P85
Hadoop
Yan Yang, Xianhua Ni, Hongjun Wang and Yiteng Zhao
Session
Biometrics and Information Security
Chair Hong Fu
Date/Time
June 19, 2012(Tuesday) 13:30-15:10
13:30 - 13:50
Dynamic ROI Extraction Algorithm for Palmprints
P86
K. Hemantha Kumar, Munaga.V.N.K. Prasad and Arun Agarwal
13:50 - 14:10
Video-base People Counting and Gender Recognition
P86
Yuen Sum Wong, Cho Wing Tam, Siu Mo Lee, Chuen Pan Chan and
Hong Fu
14:10 - 14:30
Facial Expression Recognition Based on Cortex-Like Mechanisms
P86
Heng Zhao, Xiaoping Wang and Qiang Zhang
14:30 - 14:50
A ClientServer Based Mechanism to Prevent ARP Spoofing Attacks
P87
Haider Salim, Zhitang Li, Hao Tu and Zhengbiao Guo
14:50 - 15:10
A Novel Focused Crawler Based on Breadcrumb Navigation
P87
Lizhi Ying, Xinhao Zhou, Jian Yuan and Yongfeng Huang
Session
Other Applications
Chair Fulufhelo V. Nelwamondo
Co-Chair
Adamu
Murtala
Zungeru
Date/Time
15:30 - 15:45
June 19, 2012(Tuesday) 15:30-17:30
TAC-RMTO Trading Agent Competition in Remanufacture-to-Order
Bo Xing, Wenjing Gao, Fulufhelo V. Nelwamondo, Kimberly Battle
36
P88
15:45 - 16:00
E-HASH An Energy-efficient Hybrid Storage System Composed of One
P88
SSD and Multiple HDDs
Jiao Hui, Xiongzi Ge, Xiaoxia Huang, Yi Liu and Qiangjun Ran
16:00 - 16:15
Fault Diagnosis and Optimization for Agent Based on the D-S Evidence
P89
Theory
Jianfang Wang, Qiuling Zhang and Huilai Zhi
16:15 - 16:30
Situation Cognitive in Adjustable Autonomy System Theory and
P89
Application
Rubo Zhang and Lili Yin
16:30 - 16:45
Performance of Termite-hill Routing Algorithm on Sink Mobility in
P90
Wireless Sensor Networks
Adamu Murtala Zungeru, Li-Minn Ang and Kah Phooi Seng
16:45 - 17:00
Distributed compressed Sensing based on Bipartite graph in wireless
P91
sensor networks
Zhemin Zhuang, Chuliang Wei and Fenlan Li
17:00 - 17:15
Identity Manage Interoperation Based on OpenID
P91
Shaofeng Yu, Dongmei Li and Jianyong Chen
17:15 - 17:30
Nonlinear Calibration for N Thermocouple Sensor
P91
Xiaobin Li, Haiyan Sun, Naijie Xia and Jianhua Wang
Session
Mathematics
Chair Cheng-Yuan Liou
Date/Time
June 19, 2012(Tuesday) 15:30-17:15
15:30 - 15:45
The Existence of Nonoscillatory of a Third-order Quasilinear Ordinary
P92
Differential Equation
Jinyan Wang
15:45 - 16:00
Connective Stability Analysis for a Class of Large-scale Systems Based
P92
on the Inclusion Principle
Xuebo Chen, Xufei Lu, Xinyu Ouyang and Xiao Xiao
16:00 - 16:15
Calculations of Amounts of Joint Reserve of Airplanes in Civil Aviation
P93
Systems
Zhe Yin, Yunfei Guo, Feng Lin, Di Gao and Maosheng Lai
16:15 - 16:30
About Eigenvalues from Embedding Data Complex in Low Dimension
Jiun-Wei Liou and Cheng-Yuan Liou
37
P93
16:30 - 16:45
Rotation of Matrix and DUET for Underdetermined BSS
P93
Gongxian Sun, Feng Gao and Ming Xiao
16:45 - 17:00
A Differentiating Evolutionary Computation Approach for the
P94
Multidimensional Knapsack Problem
Meysam Mohagheghi Fard, Yoon-Teck Bau and Chien-Le Goh
17:00 - 17:15
The Evaluation of Data Uncertainty and Entropy Analysis for Multiple
P94
Events
Sanghyuk Lee and T.O. Ting
Session
Chair Ying Tan
Date/Time
June 19, 2012(Tuesday) 15:30-17:10
15:30 - 15:50
An Improved Artificial Immune Recognition System Based on the
P95
Average Scatter Matrix Trace Criterion
Xiaoyang Fu and Shuqing Zhang
15:50 - 16:10
A Danger Feature Based Negative Selection Algorithm
P95
Pengtao Zhang and Ying Tan
16:10 - 16:30
Alpha Matting Using Artificial Immune Network
P96
Zhifeng Hao, Jianming Liu, Xueming Yan, Wen Wen and and Ruichu
Cai
16:30 - 16:50
Particle Swarm Optimize Fuzzy Logic Memberships of AC-drive
P96
Nasseer K. Bachache and Jinyu Wen
16:50 - 17:10
Multistep Fuzzy Classifier Forming with Cooperative-Competitive
Coevolutionary Algorithm
Roman Sergienko and Eugene Semenkin
38
P97
Abstracts
June 18, 2012(Monday) 13:30-15:15 Financial Room
Grey-based Particle Swarm Optimization Algorithm
Ming-Feng Yeh, Cheng Wen and Min-Shyang Leu
Department of Electrical Engineering, Lunghwa University of Science and Technology,
Abstract. In order to apply grey relational analysis to the evolutionary process, a modified
grey relational analysis is introduced in this study. Then, with the help of such a grey relational
analysis, this study also proposed a grey-based particle swarm optimization algorithm in which
both inertia weight and acceleration coefficients are varying over the generations. In each
generation, every particle has its own algorithm parameters and those parameters may differ for
different particles. The proposed PSO algorithm is applied to solve the optimization problems
of twelve test functions for illustration. Simulation results are compared with the other three
variants of PSO to demonstrate the search performance of the proposed algorithm.
Exponential Inertia Weight for Particle Swarm Optimization
T.O. Ting1 , Yuhui Shi1 , Shi Cheng2 and Sanghyuk Lee1
1
Dept. Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University,
China.
2
Dept. Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK
Abstract. The exponential inertia weight is proposed in this work aiming to improve the
search quality of Particle Swarm Optimization (PSO) algorithm. This idea is based on the
adaptive crossover rate used in Differential Evolution (DE) algorithm. The same formula is
adopted and applied to inertia weight, w. We further investigate the characteristics of the
adaptive w graphically and careful analysis showed that there exists two important parameters
in the equation for adaptive w; one acting as the local attractor and the other as the global
attractor. The 23 benchmark problems are adopted as test bed in this study; consisting of
both high and low dimensional problems. Simulation results showed that the proposed method
achieved significant improvement compared to the linearly decreasing method technique that
is used widely in literature.
39
A Coevolutionary Memetic Particle Swarm Optimizer
Jiarui Zhou1,2 , Zhen Ji1 , Zexuan Zhu1 and Siping Chen1,2
1
College of Biomedical Engineering and Instrument Science, Zhejiang University, China
2
Shenzhen City Key Laboratory of Embedded System Design, College of Computer
Science and Software Engineering, Shenzhen University, China
Abstract. This paper presents a coevolutionary memetic particle swarm optimizer (CMPSO)
for the global optimization of numerical functions. CMPSO simplifies the update rules of
the global evolution and utilizes five different effective local search strategies for individual
improvement. The combination of the local search strategy and its corresponding computational
budget is defined as coevolutionary meme (CM). CMPSO co-evolves both CMs and a single
particle position recording the historical best solution that is optimized by the CMs in each
generation. The experimental results on 7 unimodal and 22 multimodal benchmark functions
demonstrate that CMPSO obtains better performance than other representative state-of-the-art
PSO variances. Particularly, CMPSO is shown to have higher convergence speed.
Improved Particle Swarm Optimization with Wavelet-based
Mutation Operation
Yubo Tian, Donghui Gao and Xiaolong Li
School of Electronics and Information, Jiangsu University of Science and Technology,
China
Abstract. An improved wavelet-based mutation particle swarm optimization (IWMPSO)
algorithm is proposed in this paper in order to overcome the classic PSO’s drawbacks such
as the premature convergence and the low convergence speed. The IWMPSO introduces a
wavelet-based mutation operator first and then the mutated particle replaces a selected particle
with a small probability. The numerical experimental results on benchmark test functions
show that the performance of the IWMPSO algorithm is superior to that of the other PSOs
in references in terms of the convergence precision, convergence rate and stability. Moreover,
a pattern synthesis of linear antennas array is implemented successfully using the algorithm.
It further demonstrates the effectiveness of the IWMPSO algorithm.
40
Elastic Boundary for Particle Swarm Optimization
Yuhong Chi1,2 , Fuchun Sun1 , Langfan Jiang2 , Chunming Yu2 and Ping Zhang3
1
Tsinghua National Laboratory for Information Science and Technology, Department of
Computer Science and Technology, Tsinghua University, China
Abstract.
2
Unit 65053, PLA, China
3
Unit 65044, PLA, China
Standard particle swarm optimization (PSO) introduced in 2007, here called
2007-sPSO, is chosen as a starting algorithm in this paper. To solve the problems of the
swarm’s velocity slowing down towards zero and stagnant phenomena in the later evolutionary
process of 2007-sPSO, elastic boundary for PSO (EBPSO) is proposed, where search space
boundary is not fixed, but adapted to the condition whether the swarm is flying inside the
current elastic search space or not. When some particles are stagnant, they are activated
to speed up in the range of the current elastic boundary, and personal cognition is cleared.
Experimental results show that EBPSO improves the optimization performance of 2007-sPSO,
and performs better than comparison algorithms.
High-dimension Optimization Problems Using Specified Particle
Swarm Optimization
Penchen Chou
Department of Electrical Engineering, DaYeh University, Taiwan
Abstract. Particle Swarm Optimization (PSO), proposed by Dr. J. Kennedy and Professor
R. Eberhart in 1995, attracts many attentions to solve for a lot of real uni-modal/multi-modal
optimization problems. Due to its simplicity of parameter-setting and computational efficiency,
PSO becomes one of the most popular algorithms for optimization search. Since 1995, many
researchers provide different algorithms to set parameters for convergence, explosion and
exploitation potential of PSO. Most of the proposed methods are to find a general PSO
(called Standard PSO, SPSO) for most of the benchmark problems. However, those may
not be suitable to a specified problem, for example, Shaffer or Rosenbrock problems; especially
the dimension of the problem is high. On the contrary, with to the difficult problem such as,
Rosenbrock, a more proper specified PSO is needed for this high-dimension problem. Therefore,
for each problem after more understanding the characteristic of the problem, a SPecified PSO
(SPPSO) is proposed. Apply this idea to 5 benchmark problems, such as sphere, quatric,
Rosenbrock, Griewank, and Rastrigin functions, four different SPPSO algorithms are proposed
with good results in the end.
41
A Mechanism Based on Artificial Bee Colony to Generate
Diversity in Particle Swarm Optimization
L. N. Vitorino, S. F. Ribeiro and C. J. A. Bastos Filho
University of Pernambuco, Brazil
Abstract. Particle Swarm Optimization (PSO) presents a fast convergence in continuous
problems, but it can not maintain diversity along the entire search process. On the other hand,
Artificial Bee Colony (ABC) presents the capability to generate diversity when the guide bees
are in the exploration mode. We propose in this paper to introduce a mechanism based on
the ABC to generate diversity in the PSO. The swarm switches its behavior depending on the
evolutionary state. We used the Adaptive PSO variation since it presents better adaptation
capabilities and it also has a mechanism to evaluate the evolutionary state. We evaluated
our proposal for all benchmark functions recently proposed in 2010 and our proposal achieved
better performance in most of the cases.
Applications of PSO Algorithms
June 18, 2012(Monday) 13:30-15:15 Commercial Room
Optimization Locations of Wind Turbines with the Particle
Swarm Optimization
Ming-Tang Tsai and Szu-Wzi Wu
Department of Electrical Engineering, Cheng-Shiu University, Taiwan
Abstract. In this paper, a new algorithm is presented for the locations of wind turbine in
the distribution systems. Technical constraints such as feeder capacity limits, bus voltage,
and load balance are considered. The Particle Swarm Optimization(PSO) is applied to solve
this problem. To enhance the performance of the new algorithm, a load flow program with
Equivalent Current Injection (ECI) is used to analyze the load flow of distribution systems.
Based on ECI load flow model, a constant Jacobian matrix is determined to improve the existing
power-based model by using the Norton Equivalent Theorem. Example of IEEE 69-bus system
is adopted to illustrate the efficiency and feasible of the proposed algorithm. Test results show
that with proper site selections of wind turbines can be used to reduce system losses and
maintain the voltage profile.
42
Training ANFIS Parameters with A Quantum-Behaved Particle
Swarm Optimization Algorithm
Xiufang Lin1 , Jun Sun1 , Vasile Palade2 , Wei Fang1 , Xiaojun Wu1 and Wenbo Xu1
1
Key Laboratory of Advanced Control for Light Industry (Ministry of China), China
2
Department of Computer Science, University of Oxford, United Kingdom
Abstract. This paper proposes a novel method for training the parameters of an adaptive
network based fuzzy inference system (ANFIS). Different from previous approaches, which
emphasized on the use of gradient descent (GD) methods, we employ a method based on.
Quantum-behaved Particle Swarm Optimization (QPSO) for training the parameters of an
ANFIS. The ANFIS trained by the proposed method is applied to nonlinear system modeling
and chaotic prediction. The simulation results show that the ANFIS-QPSO method performs
much better than the original ANFIS and the ANFIS-PSO method.
Research on Improved Model of Loans Portfolio Optimization
Based on Adaptive Particle Swarm Optimization Algorithm
Ying Sun and Yuelin Gao
Research Institute of Information and System Science, Beifang University of
Nationalities, China
Abstract. The paper establishes a decision-making model of the commercial bank’s loans
portfolio optimization based on complex risk weight in view of the loan enterprise’s credit
graduation situation and so on. It is more similar with the actual operation. In order to
solve this model that is a non-linear 0-1 fractional integer programming question, we present a
adaptive particle swarm optimization (APSO) algorithm. It is shown with the numerical result
that this algorithm is effective for solving commercial bank’s loans portfolio decision-making
problem. The algorithm can solve the middle-scale question and the given model is reasonable.
Particle Swarm Optimization for Power System State Estimation
D.H. Tungadio, B.P. Numbi, M.W. Siti and A.A. Jimoh
Department of Electrical Engineering, Tshwane University of Technology, South Africa
Abstract. The electrical network measurements are usually sent to the control centers using
specific communication protocols. However, these measurements contain uncertainties due to
the meters and communication errors (noise), incomplete metering or unavailability of some
of these measurements. The aim of state estimation is to estimate the state variables of the
power system by minimizing all measurement errors available at the control center. In the past,
43
many traditional algorithms, based on gradient approach, have been used for this purpose.
This paper discusses the application of an artificial intelligence (AI) algorithm, the particle
swarm optimization (PSO), to solve the state estimation problem within a power system.
Two objective functions are formulated: the weighted least square (WLS) and weighted least
absolute value (WLAV). The effectiveness of PSO over another AI optimization algorithm,
genetic algorithm (GA), is shown by comparing both two solutions to the true state variable
values obtained using Newton-Raphson (NR) algorithm.
DPSO based on Min-max Approach and Clamping Strategy for
the Evacuation Vehicle Assignment Problem
Marina Yusoff1 , Junaidah Ariffin2 and Azlinah Mohamed1
1
Intelligent System Research Group, Faculty of Computer and Mathematical Sciences,
Universiti Teknologi Mara, Malaysia
2
Flood-Marine Excellence Centre, Faculty of Civil Engineering Universiti Teknologi
Mara, Malaysia
Abstract. During flood evacuation processes, the most challenging task is to move people to
safer locations. Uneven distributions of transport, untimely assistance and poor coordination at
the operation level have always been the major problem in the evacuation process. Currently,
lack of research focuses on assignment of vehicles prior to the evacuation routing process.
This paper proposes an improved discrete particle swarm optimization (DPSO) algorithm
to solve the evacuation vehicle assignment problem (EVAP). Discrete particle position is
proposed to support the implementation of the DPSO. Particle positions for EVAP are initially
calculated based on a min-max approach. This algorithm was computationally experimented
with different numbers of potentially flooded area and compared with DPSO with average
approach and genetic algorithm. The findings show that DPSO with the min-max approach
embedded with a new velocity clamping procedure offers good performance in maximizing the
number of people to vehicles from vehicle location to multiple numbers of potential flooded
areas.
Scripting STABL with PSO for Analysis of Slope Stability
Zheping Shen1 , Jianan Wang1 , Walter Chen1 and Fuan Tsai2
1
Dept. of Civil Engineering, LiDAR Center for High Performance Visualization and
Disaster Research, National Taipei University of Technology, Taiwan
2
Center for Space and Remote Sensing Research, National Central University, Taiwan
Abstract. Landslide is a recurring problem in Taiwan as well as other parts of the world,
44
which kills many persons every year. The more developed the societies, the more vulnerable
they are to the occurrences of natural disasters. That is why the analysis of slope stability
continues to receive high attention amongst researchers. In this study, the authors aimed to
use the standard landslide analysis program (STABL) and turned it into a computation engine
for PSO. A scripting program and a batch file were written to control the execution of STABL.
The results of STABL were automatically analyzed by the scripting program to produce next
generation’s input data files. After testing the system on a standard soil slope, the result
showed very promising potentials. Not only did the system converge to a solution, but it also
generated the best solution ever in the literature.
Feature Selection Algorithm Based on Bare Bones Particle
Swarm Optimization
Yong Zhang, Dunwei Gong, Ying Hu and Wanqiu Zhang
School of Information and Electrical Engineering, China University of Mining and
Technology, China
Abstract.
Feature selection is a useful pre-processing technique for solving classification
problems. As an almost parameter-free optimization algorithm, the bare bones particle swarm
optimization (BPSO) has been applied to the topic of optimization in continuous or integer
space, but it has not been applied to feature selection problems with binary variables. In
this paper, we propose a new method to find the related feature subset based on the BPSO,
called the binary BPSO. In this algorithm, a reinforced memory strategy is designed to update
the local leader of a particle for avoiding the degradation of out-standing gene, and the
1-nearest neighbor method is adopted as a classifier to evaluate classification accuracy. Some
international standard data sets are used to evaluate the proposed algorithm. The experiment
shows that the proposed method is competitive in terms of both classification accuracy and
computational performance.
Novel Swarm-based Optimization Algorithms
June 18, 2012(Monday) 13:30-15:30 Coffee Shop VIP Room
Unconscious Search - A New Structured Search Algorithm for
Solving Continuous Engineering Optimization Problems Based
on the Theory of Psychoanalysis
Ehsan Ardjmand and Mohammad Reza Amin Naseri
Department of Industrial Engineering, Tarbiat Modares University, Iran
45
Abstract. Many metaheuristic methods are based on the ability of systems in Nature to
optimize on aspects of their performance. One such system is the human brain with its capacity
for optimizing towards a general state of mental balance. The Theory of Psychoanalysis
propounded by Sigmund Freud is generally recognized as an account of the mechanisms involved
in psychological processes. It is possible to draw an analogy between the practice of psychoanalysis
and the treatment of optimization problems. The proposed new Unconscious Search (US)
method shares in some features with the procedure attempted in psychoanalysis to elicit the
suppressed contents of the subject’s mind. One bounded and several unbounded benchmark
problems have been solved using the proposed algorithm; the results were satisfactory when
compared against earlier results obtained using other known methods.
Brain Storm Optimization Algorithm with Modified Step-size
and Individual Generation
Dadian Zhou1 , Yuhui Shi1 and Shi Cheng1,2
1
Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool
University, China
2
Department of Electrical Engineering and Electronics, University of Liverpool,
Liverpool, UK
Abstract. Brain Storm Optimization algorithm is inspired from the humans’ brainstorming
process. It simulates the problem-solving process of a group of people. In this paper, the
original BSO algorithm is modified by amending the original BSO. First the step-size is adapted
according to the dynamic range of individuals on each dimension. Second, the new individuals
are generated in a batch-mode and then selected into the next generation. Experiments are
conducted to demonstrate the performance of the modified BSO by testing on ten benchmark
functions. The experimental results show that the modified BSO algorithm performs better
than the original BSO.
Group Search Optimizer For Power System Economic Dispatch
Huilian Liao1 , Haoyong Chen1 , Qinghua Wu2 , Masoud Bazargan3 and Zhen Ji4
1
South China University of Technology, China
2
University of Liverpool, Liverpool, U.K.
3
ALSTOM Grid UK Limited
4
Shenzhen University, China
Abstract. This paper presents the application of a group search optimizer (GSO) to solve a
power system economic dispatch problem, which is to reduce the fuel cost and transmission
46
line loss in the power system. GSO is inspired by animal searching behavior and group living
theory. The framework of GSO is mainly based on the cooperation of producer, scroungers
and rangers, which play different roles during the search. GSO has been successfully applied
to solve a wider range of benchmark functions . This paper investigates the application of
GSO to resolve the power system economic dispatch problem with consideration of minimizing
the objectives of fuel cost and transmission line loss. The performance of GSO has been
compared with that of genetic algorithm (GA) and particle swarming optimizer (PSO), and
the simulation results have demonstrated that GSO outperforms the other two algorithms.
The application is also extended to determine the optimal locations and control parameters of
flexible AC transmission system (FACTS) devices to achieve the objective. Simulation studies
have been carried out on a standard test system and better results have been obtained by GSO.
Cloud Droplets Evolutionary Algorithm on Reciprocity
Mechanism for Function Optimization
Lei Wang, Wei Li, Rong Fei and Xinghong Hei
Faculty of Computer Science and Engineering, Xi’an University of Technology, China
Abstract. For the problems of solving difficult problems in evolutionary algorithms such
as easily falling into local optimum, premature convergence because of selective pressure, a
complex and larger calculation and a lower accuracy of the solution, this paper proposes cloud
droplets evolutionary model on reciprocity mechanism (CDER). The main idea of CDER is to
simulate the phase transition of the cloud in nature which has vapor state, liquid state and solid
state, and to combine the basic ideas of evolutionary computation to realize the population
evolution. The condensation growth and collision growth of cloud droplets correspond to the
competitive evolution and reciprocal evolution of species in nature. Experiments on solving the
function optimization problems show that this model can enhance the individual competition
and survival ability, guarantee the population diversity, accelerate the convergence speed and
improve the solution precision through the iterative process of competition mechanism and
reciprocity mechanism.
A Filter Fan Based Algorithm for Slab Rehandling Problem in
MPA of Steel Industry
Xu Cheng and Lixin Tang
The Logistics Institute, Northeastern University, China
Abstract. Slab Rehandling Problem is a new style of warehousing problem stemmed from
Material Preparation Area in slab yard in steel industry which is essential to the operations
47
efficiency of the slab yard and also coordination between continuous-casting and hot-rolling
stages. SRP is to reassign storage locations of slabs in MPA under the constraint of no
further shuffles during the retrieving process, with the objective of minimizing the number of
rehandles in reassigning process and maximizing the empty stacks cleared up. Few literatures
studied exactly the same problem as SRP. For its combinatorial and dynamic nature, a basic
heuristic and a cut strategy-embeded filter & fan algorithm are proposed to solve it separately.
Experiments on real data collected from steel industry proved the effectiveness and efficiency
of the algorithm proposed. A lower bound of the problem is also proposed as a measurement
of the algorithm proposed.
A Quantum-inspired Bacterial Swarming Optimization
Algorithm for Discrete Optimization Problems
Jinlong Cao1 and Hongyuan Gao2
1
School of Information and Communication Engineering, Beijing University of Posts and
Telecommunications, China
2
College of Information and Communication Engineering, Harbin Engineering
University, China
Abstract. In order to solve discrete optimization problem, this paper proposes a quantum-inspired
bacterial swarming optimization (QBSO) algorithm based on bacterial foraging optimization
(BFO). The proposed QBSO algorithm applies the quantum computing theory to bacterial
foraging optimization, and thus has the advantages of both quantum computing theory and
bacterial foraging optimization. Also, we use the swarming pattern of birds in block introduced
in particle swarm optimization (PSO). Then we evaluate the efficiency of the proposed QBSO
algorithm through four classical benchmark functions. Simulation results show that the designed
algorithm is superior to some previous intelligence algorithms in both convergence rate and
convergence accuracy.
Multi-robot, Swarm-robot and Multi-agent Systems
June 18, 2012(Monday) 15:45-17:45 Financial Room
Priority Based Multi Robot Task Assignment
Rahul Goyal, Tushar Sharma and Ritu Tiwari
Indian Institute of Information Technology and Management, India
Abstract. In this paper, we designed and developed three new model related to task assignment
in robotics. The aim is to allocate all the robots to all the available tasks such that all tasks are
48
finished with minimum total cost and minimum time taken. The first model is completely based
on priority but waits for robots and allocates task only to the most suitable robots (highest bid
robots). The second model completes the task in such a way that it allocates best robots that
are currently available to the task without waiting for the most suitable robots to be free. The
third and final model uses heuristics based approach with auction algorithm to identify the
non-performing slow robots and eliminate them from the list of available robots. This helps in
increasing the efficiency of the whole system and helps to reduce the total cost of performance
for the system. The work aims at providing a best suitable algorithm for completing all the
tasks with minimum overhead and maintaining a specific order for the completion of the tasks.
Further it also rejects the slow working robots so that the total time taken for completion can
be reduced.
A Survey of Swarm Robotics System
Zhiguo Shi1 , Jun Tu1 , Qiao Zhang1 , Lei Liu1 and Junming Wei2
1
School of Computer and Communication Engineering, University of Science and
Technology Beijing, China
2
College of Engineering and Computer Science, Australian National University,
Australia
Abstract. Swarm robotics system has been a particularly active topic of robotics in recent
years due to the increasing deepening of research on robotics technology and application.
This paper gives a survey of swarm robotics system research from such aspects as theoretical
basis and physical research, simulation platform, distributed control information fusion and
communications system. Some problems that need to be solved about swarm robotics system
research in IOT (Internet of Things) environment are also raised, such as co-adaptation,
distributed control and self-organization, resource scheduling management. Finally, the ant
colony algorithm and particle swarm optimization are applied to the swarm robotics system.
Levels of Realism for Cooperative Multi-agent Reinforcement
Learning
Bryan Cunningham and Yong Cao
Department of Computer Science, Virginia Polytechnic Institute and State University,
USA
Abstract.
Training agents in a virtual crowd to achieve a task can be accomplished by
allowing the agents to learn by trial-and-error and by sharing information with other agents.
Since sharing enables agents to potentially reach optimal behavior more quickly, what type of
49
sharing is best to use to achieve the quickest learning times? This paper categorizes sharing into
three categories: realistic, unrealistic, and no sharing. Realistic sharing is defined as sharing
that takes place amongst agents within close proximity and unrealistic sharing allows agents
to share regardless of physical location. This paper demonstrates that all sharing methods
converge to similar policies and that the differences between the methods are determined by
analyzing the learning rates, communication frequencies, and total run times. Results show
that the unrealistic-centralized sharing method – where agents update a common learning
module – is the most effective of the sharing methods tested.
Research of Tourism Service System Base on Multi-Agent
Negotiation
Youqun Shi, Cheng Tang, Henggao Wu and Xinyu Liu
School of Computer Science and Technology, Donghua University, China
Abstract. In a multi-Agent system, agents should keep negotiation and cooperation to get
a resolution when dealing with an issue. Consequently, negotiation becomes a key point to
run the system successfully. This paper firstly proposed an Agent structure model suitable for
this field according to the traits of tourism service system. According to the characteristics
of the Agent negotiate mechanism, made some researches about multi-issue problems, utility
function, negotiation protocol, negotiation strategy and so on during negotiation process, and
then designed a negotiation model for tourism field, and used the test data to show that the
availability of this model.
Distributed Model Predictive Control of the Multi-agent Systems
with Communication Distance Constraints
Shanbi Wei, Yi Chai, Hongpeng Yin and Penghua Li
College of Automation, Chongqing University, China
Abstract.
This paper addresses a distributed model predictive control (DMPC) scheme
for multi-agent systems with communication distance constraints. Firstly, the communication
distance constraints are dealt as non-coupling constraints by using the time varying compatibility
constraints and the assumed state trajectory.
Obviously, the control performance for all
system is influenced by the time-varying compatibility constraints. Secondly, the deviation
punishment is involved in the local cost function of each agent to penalize the deviation of the
computed state trajectory from the assumed state. The value of the time-varying compatibility
constraints is set according to the deviation of previous sample time. The closed-loop stability
is guaranteed with a large weight for deviation punishment. A numerical example is given to
50
illustrate the effectiveness of the proposed scheme.
Research on HumanRobot Collaboration in Rescue Robotics
Haibo Tong, Rubo Zhang and Guanqun Liu
College of Computer Science and Technology, Harbin Engineering University, China
Abstract. This paper presents a human-robot collaboration framework which describes a
comprehensive structure of rescue robots which are expected to collaborate with human in
urban search and rescue (USAR). We develop a victim’s autonomous search and rescue robotic
system based on biomimetic sensing technology and discuss the information flow model and
control mode transition between human and robot.
The intelligence control architecture
used for human-robot collaboration is also proposed. Experiments indicate that this system
makes the collaboration convenient and the rescue robotics utilizing the intelligence control
architecture search and discover victims promptly and efficiently.
Co-evolution Framework of Swarm Self-assembly Robots
Haiyuan Li, Hongxing Wei, Jiangyang Xiao and Tianmiao Wang
School of Mechanical Engineering and Automation, Beihang University, China
Abstract. In this paper, we present a co-evolution framework of configuration and control for
swarm self-assembly robots, Sambots, in changing environments. The framework can generate
different patterns composed of a set of Sambot robots to adapt to the uncertainties in complex
environments. Sambot robots are able to autonomously aggregate and disaggregate into a
multi-robot organism. To obtain the optimal pattern for the organism, the configuration and
control of locomoting co-evolve by means of genetic programming. By self-adaptive tasks, we
imply a unified locomotion control model based on Central Pattern Generators (CPGs). In
addition, taking modular assembly modes into consideration, a mixed genotype is used, which
encodes the configuration and control. Specialized genetic operators are designed to maintain
the evolution in the simulation environment. By using an orderly method of evaluation, we
can select some resulting patterns of better performance. Simulation experiments demonstrate
that the proposed system is effective and robust in simultaneously constructing the adaptive
structure and locomotion pattern.
Superadditive Effect of Multi-robot coordination in the
Exploration of Unknown Environments via Stigmergy
T”uze Kuyucu, Ivan Tanev and Katsunori Shimohara
Information Systems Design, Doshisha University, Japan
51
Abstract. We propose a simple yet efficient way of coordinating multiple homogeneous robots
in the exploration of unknown environments. A guided probabilistic exploration of an unknown
environment is achieved via combining random movement with pheromone-based stigmergic
guidance.
The emergent strategy is shown to provide a scalable solution to multi-robot
coordination for the area exploration task, with a faster than linear speed-up with the addition
of new robots. We utilize an approach to evaluating the desired exploration behaviour that
emphasizes ”surveying” rather than ”scanning” the environment. We analyse the emergent
exploration strategies, and demonstrate their effectiveness in higher complexity environments.
Swarm Intelligence based Algorithms
June 18, 2012(Monday) 15:45-17:30 Commercial Room
Population-Based Incremental with Adaptive Learning Rate
Strategy
Komla A Folly
Department of Electrical Engineering, University of Cape Town, South Africa
Abstract. Population-Based Incremental Learning (PBIL) is a relatively new class of Evolutionary
Algorithms (EA) that has been recently applied to a range of optimization problems in engineering
with promising results. PBIL combines aspects of Genetic Algorithm with competitive learning.
The learning rate in the standard PBIL is generally fixed which makes it difficult for the
algorithm to explore the search space effectively. In this paper, a PBIL with Adapting learning
rate is proposed. The Adaptive PBIL (APBIL) is able to thoroughly explore the search space
at the start of the run and maintain the diversity longer than the standard PBIL. To show its
effectiveness, the proposed algorithm is applied to the problem of optimizing the parameters
of a power system controller. Simulation results show that APBIL based controller performs
better than the standard PBIL based controller.
A SI-based Algorithm for Structural Damage Detection
Ling Yu1,2 , Peng Xu1 and Xi Chen1
1
2
Department of Mechanics and Civil Engineering, Jinan University, China
MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, China
Abstract. As a challenging task in the structural health monitoring (SHM) field, structural
damage detection, one of most important issues of the SHM system, is mathematically converted
into a constrained optimization problem, which is then hopefully solved by a swarm intelligence
(SI) based algorithm proposed in this paper. The performance of the proposed algorithm is
52
experimentally evaluated by the measured data of four damage patterns of a building model
of 3-storey steel frame structure made in laboratory. Some illustrated results show that the
proposed method is very suitable for the structural multi-damage identification, which also
show that the SI-based algorithm for structural damage detection can provide an effective and
robust tool in the SHM field.
Swarm Intelligence in Cloud Environment
Anirban Kundu and Chunlin Ji
Kuang-Chi Institute of Advanced Technology, China
Abstract. In this paper, the major goal is to show the swarm intelligence power in cloud based
scenario. Heterogeneous environment has been configured at server-side network of cloud.
Swarm intelligence has been adopted for enhancing performance of overall system network.
Specific location at server-side of network is going to be selected by swarm intelligence for
accessing desired elements. Flexibility, robustness and self-organization have been considered
as main features of swarm intelligence.
Swarm Intelligence Supported e-Remanufacturing
Bo Xing, Wenjing Gao, Fulufhelo V. Nelwamando, Kimberly Battle and Tshilidzi
Marwala
Faculty of Engineering and the Built Environment, University of Johannesburg, South
Africa
Abstract. e-Remanufacturing has nowadays become a superior option for product recovery
management system. So far, many different approaches have been followed in order to increase
the efficiency of remanufacturing process. Swarm intelligence (SI), a relatively new bio-inspired
family of methods, seeks inspiration in the behavior of swarms of insects or other animals. After
applied in other fields with success, SI started to gather the interest of researchers working in
the field of remanufacturing. In this paper we provide a survey of SI methods that have been
used in e-remanufacturing.
Bacterial-inspired Algorithms for Solving Constrained
Optimization Problems
Ben Niu1,2,3 and Jingwen Wang2
1
2
College of Management, Shenzhen University, China
e-Business Technology Institute, The University of Hongkong, Hongkong, China
3
Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, China
53
Abstract. Bio-inspired optimization techniques using analogy of swarming principles and
social behavior in nature have been adopted to solve a variety of problems. In this paper
Bacterial foraging optimization (BFO) is employed to achieve high-quality solutions to the
constrained optimization problems. To further improve the performance of the original BFO,
we also come up with two modified BFOs, i.e. BFO with linear decreasing chemotaxis step
(BFO-LDC) and BFO with non-linear decreasing chemotaxis step (BFO-NDC). To illustrate
the efficiency of the proposed algorithms (BFO-LDC and BFO-NDC), eleven different constrained
benchmark problems were selected as testing functions to compare with original BFO. The
experimental results demonstrated that the improved BFOs are efficient and general approach
to solve most nonlinear optimization problems with inequity constrains.
A New Model to Imitate the Foraging Behavior of Physarum
Polycephalum in Nutrient-poor Substrate
Yuheng Wu1 , Zili Zhang1,2 , Yong Deng1 , Huan Zhou1 and Tao Qian1
1
Faculty of Computer and Information Science, Southwest University, China
2
School of Information Technology, Deakin University, Australia
Abstract. Researches on Physarum Polycephalum show that methods inspired by the primitive
unicellular organism can solve some complex problems in graph theory and construct effective
network. Current models simulating the intelligent behavior of Physarum are mainly based
on Poiseuille Law and Kerhoff Law, reaction-diffusion and Cellular Automaton. In this paper,
based on an assumption that Physarum forages for food along the gradient of chmo-attractants
in nutrient-poor substrate, a new model is proposed to imitate its intelligent foraging behavior.
The key point of the model is that the growth of Physarum is determined by the simple
particle concentration field relating the distance to food source and the shape of food source in
nutrient-poor substrate. To verify this model, numerical experiments are conducted according
to [19]. Results in spanning tree construction by this model is almost the same as those of
Physarum and Oregonator Model. The proposed model can also imitate Physarum to avoid
repellents. Furthermore, the Euclidean Spanning tree built by this model is similar to its
corresponding Minimal Euclidean Spanning tree.
Mining the Role-oriented Process Models Based on Genetic
Algorithm
Weidong Zhao, Qinhe Lin, Yue Shi and Xiaochun Fang
Software School, Fudan University, China
Abstract. Traditional role-oriented process modeling seems to be subjective in identifying
54
roles. To solve the problem, the similarity of activities is used in this paper. Sub-processes
with high similarity are recognized as the process undertaken by a certain role.
In this
way, a relatively objective role identification approach is proposed, which determines the
interaction between roles and establishes the role-activity diagram. Furthermore, by analyzing
the interaction between roles, genetic algorithm is used to introduce multiple factors to optimize
the identification. Therefore, an optimized role-oriented process modeling approach is established
and an example is presented to show the feasibility of this approach.
Ant and Bee Colony Optimization Algorithms
June 18, 2012(Monday) 15:45-17:45 Coffee Shop VIP Room
Ant Colony Algorithm for Surgery Scheduling Problem
Jiao Yin and Wei Xiang
Faculty of Mechanical Engineering and Mechanics, Ninbo University, China
Abstract. Considering the complete process of surgery including the preoperative and postoperative
stages, multiple resource constraints involved and the integration of surgical upstream and
downstream resources, surgery scheduling was described as an extended multi-resource constrained
flexible job-shop scheduling problem and an optimization approach was proposed based on an
improved ant colony algorithm. A resource selection rule and strategy of overtime judging and
adjusting was designed, and the scheduling process with the ant colony algorithm was realized.
The case study shows that the improved ant colony algorithm proposed in this paper achieved
good results in shortening total time and allocating resources for surgery scheduling.
A Method for Avoiding the Feedback Searching Bias in Ant
Colony Optimization
Bolun Chen1 and Ling Chen1,2
1
2
Department of Computer Science, Yangzhou University, China
State Key Lab of Novel Software Tech, Nanjing University, China
Abstract. One of the obstacles in applying ant colony optimization (ACO) to the combinatorial
optimization is that the search process is sometimes biased by algorithm features such as the
pheromone model and the solution construction process. Due to such searching bias, ant colony
optimization cannot converge to the optimal solution for some problems. In this paper, we
define a new type of searching bias in ACO named feedback bias taking the k-cardinality tree
problem as the test instance. We also present a method for avoiding the feedback searching
bias. Convergence analysis of our method is also given. Experimental results confirm the
55
correctness of our analysis and show that our method can effectively avoid the searching bias
and can ensure the convergence for the problem.
Node-based Pheromone Updating Strategy for Ant Colony
Optimization with Pheromone Marks
Xiangyang Deng1 , Limin Zhang2 and Lan Luo3
1
Department of Electric and Information Engineering, Naval Aeronautical and
Astronautical University, China
2
Department of Scientific Research, Naval Aeronautical and Astronautical, China
3
Basic Teaching Department, Yantai Vocational College, China
Abstract. An improved ant colony optimization algorithm is proposed for the non-ergodic
optimal problems. The algorithm called PM-ACO has a pheromone trace of scatter points
called pheromone marks, and has a global pheromone updating strategy in each iteration,
which has two components. One is updating the pheromones of the nodes which belong to
the best-so-far tour, and the other is updating the pheromones of node groups which consist
of the best-ranked nodes and the k-nearest-neighbor (KNN) nodes to one of the best-ranked
nodes. The later component is studied particularly, and some modifications are presented
which include an r-best-node updating rule and a relevant-node depositing rule. Experimental
tests show that PM-ACO can solve the shortest path problem effectively and has a good
performance compared with ACS.
A Web-Service for Automated Software Refactoring Using
Artificial Bee Colony Optimization
Ekin Koc1 , Nur Ersoy2 , Zelal Seda Camlidere1 and Hurevren Kilic3
1
Department of Computer Engineering, Atilim University, Turkey
2
3
Innova, Middle East Technical University, Turkey
Computer Engineering Department, Gediz University, Turkey
Abstract. Automated software refactoring is one of the hard combinatorial optimization
problems of search-based software engineering domain. The idea is to enhance the quality
of the existing software under the guidance of software quality metrics through applicable
refactoring actions. In this study, we designed and implemented a web-service that uses discrete
version of Artificial Bee Colony (ABC) optimization approach in order to enhance bytecode
compiled Java programming language codes, automatically. The introduced service supports 20
different refactoring actions that realize intelligent ABC searches on design landscape defined
by an adhoc quality model being an aggregation of 24 object-oriented software metrics.
56
Reduction of Artificial Bee Colony Algorithm for Global
Optimization
Michiharu Maeda and Shinya Tsuda
Fukuoka Institute of Technology, Japan
Abstract.
This paper presents a reduction of artificial bee colony algorithm for global
optimization. Artificial bee colony algorithm is an optimization technique which refers to the
behavior of honeybee swarms, and a multi-point search approach which finds a best solution
using multiple bees. For avoiding local minimums, a number of bees are initially prepared and
their positions are updated by artificial bee colony algorithm. Bees sequentially reduce to reach
a predetermined number of them grounded in the evaluation value and artificial bee colony
algorithm continues until the termination condition is met. In order to show the effectiveness
of the proposed algorithm, we examine the best value by using test functions compared to
existing algorithms. Furthermore the influence of best value on the initial number of bees for
our algorithm is discussed.
A Novel Simple Candidate Set Method for Symmetric TSP and
Its Application in MAX-MIN Ant System
Miao Deng1 , Jihong Zhang2 , Yongsheng Liang2 , Guangming Lin2 and Wei Liu2
1
2
School of Information Engineering, Shenzhen University, China
Shenzhen Key Lab of Visual Media Processing and Transmission, Shenzhen Institute of
Information Technology, China
Abstract. Traveling Salesman Problem (TSP) is a kind of typical NP problem and has been
extensively researched in combinatorial optimization. For solving it more effectively, candidate
set is used in many algorithms in order to limit the selecting range when choosing next city to
move, such as in Ant Systems, or to initialize a local optimum solution, such as in Lin-Kernighan
Heuristic (LKH) algorithm. A novel simple method for generating candidate set is proposed
in this paper and applied into MAX-MIN Ant System (MMAS) for symmetric TSP problem.
Experimental results show that it has better performance than other Ant Systems including
MMAS. Moreover, this method can be used in other algorithms for symmetric TSP problem.
Differential Evolution and Genetic Algorithms
June 19, 2012(Tuesday) 08:00-09:45 Financial Room
57
Self-configuring Genetic Algorithm with Modified Uniform
Crossover Operator
Eugene Semenkin and Maria Semenkina
Departement of System Analysis and Operation Research, Siberian State Aerospace
University, Russian Federation
Abstract.
For genetic algorithms, new variants of the uniform crossover operator that
introduce selective pressure on the recombination stage are proposed. Operator probabilistic
rates based approach to genetic algorithms self-configuration is suggested. The usefulness of
the proposed modifications is demonstrated on benchmark tests and real world problems.
A Genetic Algorithm for the Maximum Edge-disjoint Paths
Problem
Chia-Chun Hsu1,2 and Hsun-Jung Cho1
1
Department of Transportation Technology and Management, National Chiao Tung
University, Taiwan
2
Department of Industrial and Systems Engineering, North Carolina State University,
Raleigh, USA
Abstract. Optimization problems concerning edge-disjoint paths have attracted considerable
attention for decades. These problems have a lot of applications in the areas of call admission
control, real-time communication, VLSI (Very-large-scale integration) layout and reconfiguration,
packing, etc. The maximum edge-disjoint paths problem (MEDP) seems to lie in the heart of
these problems. Given an undirected graph G = (V, E) and a set T = (si , ti )| = 1, . . . , Iandsi 6= ti ∈ V
of I connection requests, MEDP is an NP-hard problem which determines the maximum
number of accepted requests that can be routed by mutually edge-disjoint (si , ti ) paths. We
propose a genetic algorithm (GA) to solve the problem. In comparison to the multi-start simple
greedy algorithm (MSGA) and the ant colony optimization method (ACO), the proposed GA
method performs better in most of the instances in terms of solution quality and time.
58
Ensemble of Clearing Differential Evolution for Multi-modal
Optimization
Boyang Qu1,2 , Jing Liang3 , Ponnuthurai Nagaratnam Suganthan2 and Tiejun Chen3
1
School of Electric and Information Engineering, Zhongyuan University of Technology,
China
2
School of Electrical and Electronic Engineering, Nanyang Technological University,
Singapore
3
School of Electrical Engineering, Zhengzhou University, China
Abstract. Multi-modal Optimization refers to finding multiple global and local optima of
a function in one single run, so that the user can have a better knowledge about different
optimal solutions. Multiple global/local peaks generate extra difficulties for the optimization
algorithms. Many niching techniques have been developed in literature to tackle multi-modal
optimization problems. Clearing is one of the simplest and most effective methods in solving
multi-modal optimization problems. In this work, an Ensemble of Clearing Differential Evolution
(ECLDE) algorithm is proposed to handle multi-modal problems. In this algorithm, the
population is evenly divided into 3 subpopulations and each of the subpopulations is assigned
a set of niching parameters (clearing radius). The algorithms is tested on 12 benchmark
multi-modal optimization problems and compared with the Clearing Differential Evolution
(CLDE) with single clearing radius as well as a number of commonly used niching algorithms.
As shown in the experimental results, the proposed algorithm is able to generate satisfactory
performance over the benchmark functions.
Fast Mixed Strategy Differential Evolution Using Effective
Mutant Vector Pool
1
Hao Liu , Han Huang1,2 , Yingjun Wu1 and Zhenhua Huang1
1
School of Software Engineering South China University of Technology Guangzhou,
China
2
Department of Management Sciences, College of Business City University of Hong
Kong, Hong Kong
Abstract. The mutant vector has significant influence on the performance of Differential
Evolution (DE). Different mutant vector always generates different result, one outstanding
mutant vector for a specify problem perhaps achieve unbearable bad result for another question.
There still no one perfect mutant vector can solve all problems excellently. In this situation,
mixed strategy method is proposed to improve the performance of DE by combining multi-effective
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mutant vectors together. This paper proposes a fast mixed strategy DE (FMDE). The new
method uses two best mutant vectors selected from the mutant vector pool and applies a fast
mixed method to generate better result without increase computing expense. The FMDE
is evaluated by 27 benchmarks selected from Congress on Evolutionary Computation (CEC)
competition. The experiment result shows FMDE is competitive, stable and comprehensive.
Differential Annealing for Global Optimization
Yongwei Zhang, Lei Wang and Qidi Wu
College of Electronics and Information Engineering, Tongji University, China
Abstract.
This paper propose a hybrid stochastic approach called differential annealing
algorithm. The algorithm integrated the advantages of differential evolution and simulated
annealing. It can be considered as a swarm-based simulated annealing with differential operator
or differential evolution with the Boltzmann-type selection operator. The proposed algorithm
is tested on benchmark functions, along with simulated annealing and differential evolution.
Results show that differential annealing outperforms the comparative group under the same
amount of function evaluations.
Image Retrieval Based on GA Integrated Color Vector
Quantization and Curvelet Transform
Yungang Zhang1,2 , Tianwei Xu1 and Wei Gao3
1
2
School of information Science, Yunnan Normal University, China
Department of Computer Science & Software Engineering, Xi’an JiaoTong-Liverpool
University, China
3
Department of Mathematics, Soochow Univeristy, China
Abstract. Color and shape information have been two important image descriptors in Content
Based Image Retrieval (CBIR) systems.
The focus of this research is to find a method
representing images with color and shape information in the way of human visual perception.
The image retrieval approach proposed here depends on the color and shape features extracted
by color Vector Quantization (VQ) and the Digital Curvelet Transform (DCT), respectively.
The extracted color and shape features were combined and weighted by Genetic Algorithm
(GA), then used for image similarity measurement. Experimental results show that the GA
combined features can bring about improved image retrieval performance.
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A Novel Fitness Allocation Algorithm for Maintaining a
Constant Selective Pressure During GA Procedure
Li Feng Zhang, Chen Xi Zhou, Rong He, Yuan Xu and Meng Ling Yan
School of Information, Renmin University of China, China
Abstract.
During the evolution procedure of GA, the fitness distribution of population
is always unforeseeable since it varies with a number of factors such as the landscape of
optimization problem, crossover and mutation algorithms, and the progress of evolution procedure.
For the GAs using stochastic selection mechanisms, fitness distribution sometimes significantly
impacts on the performance of selection process, so as to make it very ineffective in protecting
superior individuals and preventing inferior individuals. In this study, a new fitness scaling
method, named powered distance sums scaling (PDSS), is proposed to eliminate the influence
of fitness distribution on stochastic selection. Unlike previous approaches, the new method
uses the powered sums of fitness distance to substitute for both raw fitness values and ranking
numbers for computing scaled fitness. It maintains a much more constant and consistent
selective pressure in different conditions of optimization problem and GA algorithm design, and
may help GA designers in balancing exploration and exploitation during evolution procedures.
Empirical studies on both real number optimization and combinatorial optimization are employed
in this study to illustrate the new method that through using the new scaling technique the
convergence speed of GA search becomes more controllable.
Hybrid Algorithms
June 19, 2012(Tuesday) 08:00-10:00 Commercial Room
Memetic Three-Dimensional Gabor Feature Extraction for
Hyperspectral Imagery Classification
Zexuan Zhu1 , Linlin Shen1 , Yiwen Sun2 , Shan He3 and Zhen Ji1
1
City Key Laboratory of Embedded System Design, College of Computer Science and
Software Engineering, Shenzhen University, China
2
Shenzhen Key Lab of Biomedical Engineering, School of Medicine, Shenzhen
University, China
3
School of Computer Science, University of Birmingham, UK
Abstract. This paper proposes a three-dimensional Gabor feature extraction for pixel-based
hyperspectral imagery classification using a memetic algorithm.
The proposed algorithm
named MGFE combines 3-D Gabor wavelet feature generation and feature selection together
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to capture the signal variances of hyperspectral imagery, thereby extracting the discriminative
3-D Gabor features for accurate classification. MGFE is characterized with a novel fitness
evaluation function based on independent feature relevance and a pruning local search for
eliminating redundant features. The experimental results on two real-world hyperspectral
imagery datasets show that MGFE succeeds in obtaining significantly improved classification
accuracy with parsimonious feature selection.
A Novel Two-Level Hybrid Algorithm for Multiple Traveling
Salesman Problems
Qingsheng Yu1 , Dong Wang2 , Dongmei Lin3 , Ya Li2 and Chen Wu2
1
Department of Electronics and Information, Foshan Polytechnic, China
2
3
Department of Computer, Foshan University, China
Center of Information and Education Technology, Foshan University, China
Abstract. Multiple traveling salesmen problem is a NP-hard problem. The method for solving
the problem must arrange with reason all cities among traveling salesman and find optimal
solution for every traveling salesman. In this paper, two-level hybrid algorithm is put forward to
take into account these two aspects. Top level is new designed genetic algorithm to implement
city exchange among traveling salesmen with the result clustered by k-means. Bottom level
employs branch-and-cut and Lin-kernighan algorithms to solve exactly sub-problems for every
traveling salesman. This work has both the global optimization ability from genetic algorithm
and the local optimization ability from branch-and-cut.
Hybrid Particle Swarm Optimization for Preemptive
Resource-constrained Project Scheduling
Yongyi Shou, Ying Li and Changtao Lai
School of Management, Zhejiang University, China
Abstract. In this paper, a hybrid particle swarm optimization procedure is designed to solve
the preemptive resource-constrained project scheduling problem in which a maximum of one
interruption per activity is allowed. Four types of particle representations are introduced
and two schedule generation schemes are adopted to decode the particle representations.
Particle-updating mechanisms based on the peak crossover operator are designed for all particle
representations. Computational experiments were carried out on standard project scheduling
problem sets. Analysis of the computational results confirmed that introduction of preemption
helps to reduce project makespan and the proposed particle swarm optimization procedures
are effective for preemptive resource-constrained project scheduling.
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Discovery Scientific Laws by Hybrid Evolutionary Model
Fei Tang, Sanfeng Chen, Xu Tan, Tao Hu, Guangming Lin and Zuo Kang
Shenzhen Institute of Information Technology, China
Abstract. Constructing a mathematical model is an important issue in engineering application
and scientific research. Discovery high-level knowledge such as laws of natural science in the
observed data automatically is a very important and difficult task in systematic research. The
authors have got some significant results with respect to this problem. In this paper, high-level
knowledge modelled by systems of ordinary differential equations (ODEs) is discovered in the
observed data routinely by a hybrid evolutionary algorithm called HEA-GP. The application is
used to demonstrate the potential of HEA-GP. The results show that the dynamic models
discovered automatically in observed data by computer sometimes can compare with the
models discovered by humanity. In addition, a prototype of KDD Automatic System has
been developed which can be used to discover models in observed data automatically.
A Hybrid Fireworks Optimization Method with Differential
Evolution Operators
Yujun Zheng1 , Xinli Xu1 and Haifeng Ling2
1
College of Computer Science & Technology, Zhejiang University of Technology, China
2
Department of Mechanical Engineering, PLA University of Science & Technology,
China
Abstract. Fireworks algorithm (FA) is a relatively new swarm-based metaheuristic for global
optimization. The algorithm is inspired by the phenomenon of fireworks display and has
a promising performance on a number of benchmark functions. However, in the sense of
swarm intelligence, the individuals including fireworks and sparks are not well-informed by the
whole swarm. In this paper we develop an improved version of the FA by combining with
differential evolution (DE) operators: mutation, crossover, and selection. At each iteration of
the algorithm, most of the newly generated solutions are updated under the guidance of two
different vectors that are randomly selected from highly ranked solutions, which increases the
information sharing among the individual solutions to a great extent. Experimental results
show that the DE operators can improve diversity and avoid prematurity effectively, and the
hybrid method outperforms both the FA and the DE on the selected benchmark functions.
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Evolving RBF Neural Networks for Rainfall Prediction Using
Hybrid Particle Swarm Optimization and Genetic Algorithm
Jiansheng Wu1 , Jin Long2 and Mingzhe Liu3
1
School of Information Engineering, Wuhan University of Technology, China
2
3
Guangxi Research Institute of Meteorological Disasters Mitigation, China
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,
Chengdu University of Technology, China
Abstract.
In this paper, an effective hybrid optimization strategy by incorporating the
adaptive optimization of Particle Swarm Optimization (PSO) into Genetic Algorithm (GA),
namely HPSOGA, is used for determining the parameters of Radial Basis Function Neural
Networks (number of neurons, their respective centers and radii) automatically. While this
task depends upon operator’s experience with trial and error due to lack of prior knowledge,
or based on gradient algorithms which are highly dependent on initial values. In this article,
hybrid evolutionary algorithms are used to automatically build a radial basis function neural
networks (RBF-NN) that solves a specified problem, related to rainfall forecasting in this
case. In HPSOGA, individuals in a new generation are created through three approaches to
improve the global optimization performance, which are elitist strategy, PSO strategy and GA
strategy. The upper-half of the best-performing individuals in a population are regarded as
elites, whereas the half of the worst-performing individuals are regarded as a swarm. The
group constituted by the elites are enhanced by selection, crossover and mutation operation
on these enhanced elites. HPSOGA is applied to RBF-NN design for rainfall prediction. The
performance of HPSOGA is compared to pure GA in these Basis Function Neural Networks
design problems, showing the hybrid strategy is of more effective global exploration ability and
to avoid premature convergence. Our findings reveal that the hybrid optimization strategy
proposed here may be used as a promising alternative forecasting tool for higher forecasting
accuracy and better generalization ability.
Evolving Neural Network Using Hybrid Genetic Algorithm and
Simulated Annealing for Rainfall-runoff Forecasting
Hong Ding1,2 , Jiansheng Wu3 and Xianghui Li4
1
2
School of information Engineering, Wuhan University of Technology, China
Department of Physics and Information Science, Liuzhou Teachers College, China
3
Department of Mathematics and Computer, Liuzhou Teachers College, China
4
Liuzhou City Flood Control and Drainage Project Management Office, China
64
Abstract. Accurately rainfall–runoff forecasting modeling is a challenging task. Recent neural
network (NN) has provided an alternative approach for developing rainfall–runoff forecasting
model, which performed a nonlinear mapping between inputs and outputs. In this paper,
an effective hybrid optimization strategy by incorporating the jumping property of simulated
annealing (SA) into Genetic Algorithm (GA), namely GASA, is used to train and optimize the
network architecture and connection weights of neural networks for rainfall–runoff forecasting
in a catchment located Liujiang River, which is a watershed from Guangxi of China. This
new algorithm incorporates metropolis acceptance criterion into crossover operator, which
could maintain the good characteristics of the previous generation and reduce the disruptive
effects of genetic operators. The results indicated that compared with pure NN, the GASA
algorithm increased the diversity of the individuals, accelerated the evolution process and
avoided sinking into the local optimal solution early. Results obtained were compared with
existent bibliography, showing an improvement over the published methods for rainfall–runoff
prediction.
Evolutionary Three-Stage Approach for Designing of Neural
Networks Ensembles for Classification Problems
Vladimir Bukhtoyarov1 and Eugene Semenkin2
1
Department of Information Technologies Security, Siberian State Aerospace University,
Russia
2
Department of System Analysis and Operational Research, Siberian State Aerospace
University, Russia
Abstract. The use of the neural network ensemble approach for solving classification problems
is discussed. Methods for forming ensembles of neural networks and methods for combining
solutions in ensembles of classifiers are reviewed briefly. The main ideas of comprehensive
evolutionary approach for automatic design of neural network ensembles are described. A new
variant of a two-level evolutionary approach to decision making in ensembles of neural networks
is proposed for classification problems. The technique and results of a comparative statistical
investigation of various methods for producing of ensembles decisions on several well-known
test problems are given.
Multi-Objective Optimization Algorithms
June 19, 2012(Tuesday) 08:00-09:45 Coffee Shop VIP Room
65
On the Performance Metrics of Multiobjective Optimization
Shi Cheng1,2 , Yuhui Shi2 and Quande Qin3
1
Department of Electrical Engineering and Electronics, University of Liverpool, UK
2
Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool
University, China
3
College of Management, Shenzhen University, China
Abstract. Multiobjective Optimization (MOO) refers to optimization problems that involve
two or more objectives. Unlike in the single objective optimization, a set of solutions representing
the tradeoff among the different objects rather than an unique optimal solution is sought
in MOO. How to measure the goodness of solutions and the performance of algorithms is
important in MOO. In this paper, we first review the performance metrics of multiobjective
optimization and then classify variants of performance metrics into three categories: set based
metrics, reference point based metrics, and the true Pareto front/set based metrics. The
properties and drawbacks of different metrics are discussed and analyzed. From the analysis of
different metrics, an algorithm’s properties can be revealed and more effective algorithms can
be designed to solve MOO problems.
Brain Storm Optimization Algorithm for Multi-objective
Optimization Problems
Jingqian Xue1 , Yali Wu1 , Yuhui Shi2 and Shi Cheng2
1
School of Automation and Information Engineering, Xi’an University of Technology,
China
2
Dept. of Eletrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, China
Abstract. In this paper, a novel multi-objective optimization algorithm based on the brainstorming
process is proposed(MOBSO). In addition to the operations used in the traditional multi-objective
optimization algorithm, a clustering strategy is adopted in the objective space. Two typical
mutation operators, Gaussian mutation and Cauchy mutation, are utilized in the generation
process independently and their performances are compared.
A group of multi-objective
problems with different characteristics were tested to validate the effectiveness of the proposed
algorithm. Experimental results show that MOBSO is a very promising algorithm for solving
multi-objective optimization problems.
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Modified Multi-objective Particle Swarm Optimization
Algorithm for Multi-objective Optimization Problems
Ying Qiao
Research Institute of Information and System Science, Beifang University of
Nationalities, China
Abstract. Multi-objective particle swarm optimization (MOPSO) is an optimization technique
inspired by bird flocking, which has been steadily gaining attention from the research community
because of its high convergence speed. However, faced with multi-objective problems, adaptations
are needed. Deeper researches must be conducted on its key steps, such as guide selection, in
order to improve its efficiency in this context. This paper proposes an modified multi-objective
particle swarm optimizer named MMOPSO, for dealing with multi-objective problems. we
introduce some ideas concerning the guide selection for each particle. The proposed algorithm
is compared against four multi-objective evolutionary approaches based on particle swarm
optimization on four benchmark problems. The numerical results show the effectiveness of the
proposed MMOPSO algorithm.
A Multi-Objective Mapping Strategy forApplication Specific
Emesh Network-on-Chip NoC
Bixia Zhang1,2 , Huaxi Gu1,2 , Sulei Tian3 and Bin Li3
1
2
State Key Laboratory of ISN, Xidian University, China
Science and Technology on Information Transmission and Dissemination in
Communication Networks Laboratory, China
3
The 54th Institute of CETC, China
Abstract. This paper proposes a new optimization model for mapping IP cores onto a new
Network-on-Chip (NoC) topology, Emesh. Since competition for one port of the router in
Emesh is violent, network competition is considered in our model, in addition to energy and
communication cost. To solve this optimization model, the authors present a new application
specific multi-objective mapping algorithm onto Emesh topology based on the idea of the
crossover and mutation of genetic algorithm. The experimental results show that, compared to
traditional heuristic genetic algorithm, the proposed algorithm has lower energy consumption,
lower communication cost and less network competition.
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Binary Nearest Neighbor Classification of Predicting Pareto
Dominance in Multi-objective Optimization
Guanqi Guo, Cheng Yin, Taishan Yan and Wenbin Li
Hunan Institute of Science and Technology, China
Abstract. A method of predicting Pareto dominance in multi-objective optimization using
binary nearest neighbor classification (BNNC) is proposed. It encodes real value feature
variables into binary bit strings with the same length. The similarity of two feature variables
is directly measured by weighted sum of the binary bits. The analysis shows that when the
orders of magnitude for various feature variables differ from each other, the similarity measured
by scaled feature variables is able to more uniformly reflect the contribution of each feature
variable to Pareto dominance relationship, and BNNC has computational complexity of O(N).
Experiments results show that, in addition to remarkably increasing classification accuracy
rate, it is more efficient and robust than the canonical nearest neighbor rule and Bayesian
classification when used to classify those problems with unbalanced class proportions and
feature vectors no less than 2 dimensions.
Multi-objective Evolutionary Algorithm Based on Layer Strategy
Sen Zhao1,2 , Zhifeng Hao1,3 , Shusen Liu4 , Weidi Xu4 and Han Huang4
1
College of Computer Science and Engineering, South China University of Technology,
China
2
3
4
Department of Computer Science, Jinan University, China
School of Computer, Guangdong University of Technology, China
School of Software Engineering, South China University of Technology, China
Abstract. In view of the unsatisfactory search performance of binary crossing operator as
well as the elitist-preserving approach’s influence on the population’s diversity, an algorithm
of multi-objective based on layer strategy and self-adaptive crossing distribution index is put
forward on the basis of research and analysis on NSGA- algorithm. The algorithm will be
applied to the ZDT series test functions. The experiment results show that the improved
algorithm maintains the diversity and distribution of population. Compared with NSGA-,the
Pareto front we get is much closer to the true Pareto optimal front.
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An Improved MOPSO with a Crowding Distance Based External
Archive Maintenance Strategy
Weixing Li1 , Qian Zhou1 , Yu Zhu2 and Feng Pan1
1
School of Automation, Beijing Institute of Technology, China
2
China Academy of Space Technology
Abstract. For multi-objective optimization algorithms, the maintenance policy of external
archive has a great impact on the performance of convergence and solution diversity. Considering
the dilemma of large population and external archive, an improved strategy of external archive
maintenance based on crowding distance is proposed, which requires less particle numbers and
smaller archive size, resulting in the computation cost reduction. Furthermore, the information
entropy of gbest is analyzed to emphasize the diversity improvement of non-dominant solutions
and well-distribution on the Pareto-optimal front. Numerical experiments of benchmark functions
demonstrate the effectiveness and efficiency of proposed multi-objective particle swarm optimization.
Machine Learning and Data Mining Methods (I)
An Automatic Learning System to Derive Multipole and Local
Expansions for the Fast Multipole Method
Seyed Naser Razavi1 , Nicolas Gaud2 , Abderrafiâa Koukam2 and Naser Mozayani1
1
Iran University of Science and Technology
2
UTBM
Abstract. This paper introduces an automatic learning method based on genetic programming
to derive local and multipole expansions required by the Fast Multipole Method (FMM). FMM
is a well-known approximation method widely used in the field of computational physics, which
was first developed to approximately evaluate the product of particular N N dense matrices
with a vector in O(N log N) operations. Later, it was applied successfully in many scientific
fields such as simulation of physical systems, Computer Graphics and Molecular dynamics.
However, FMM relies on the analytical expansions of the underlying kernel function defining
the interactions between particles, which are not always obvious to derive. This is a major
factor limiting the application of the FMM to many interesting problems. Thus, the proposed
method here can be regarded as a useful tool helping practitioners to apply FMM to their own
problems such as agent-based simulation of large complex systems. The preliminary results of
the implemented system are very promising, and so we hope that the proposed method can be
69
applied to other problems in different application domains.
Automatic Scoring on English Passage Reading Quality
Junbo Zhang, Fuping Pan and Yongyong Yan
The Key Laboratory of Speech Acoustics and Content Understanding, Institute of
Acoustics, Chinese Academy of Sciences, China
Abstract. In this paper, the computer automatic scoring for English discourse oral reading
was studied. We analyzed the oral reading voices with speech recognition technology, and
extracted a series of features in terms of pronunciation and fluency, and then mapped these
features to scores. In the testing of English discourse oral reading for 4000 middle school
students, the average scoring difference between machine and human teacher was 0.66, while
the scoring difference in human teachers was 0.57. The experience result shows that this system
can be used in practice.
An Expandable Recommendation System on IPTV
Jie Xiao and Liang He
Instituation of Computer Aplication, East China Normal University, China
Abstract. IPTV’s mass resources, high quality service and its open and free interactive service
mode attract billons of user. It has brilliant potential and also has great challenge. Traditional
one algorithm supported recommendation system is no longer content IPTV’s huge service
demand. In order to solve this problem, an expandable recommendation system is proposed in
this paper. It contains new flexible system framework and basic ideas of applicable algorithms.
And it has been implemented in real IPTV system and works well.
Intrinsic Protein Distribution on Manifolds Embedded in
Low-dimensional Space
Wei-Chen Cheng
Institute of Statistical Science, Academia Sinica, China
Abstract. There has been more than twenty thousand proteins in human body been sequenced.
This work applies three manifold learning techniques, which preserve distinct relations among
proteins coded by human DNA, to explore the geometry of the distribution of genes in three-dimensional
space. The simulations suggest that the relations hidden among genetic amino-acid sequences
could be explored by the manifolds embedded in Euclidean space.
70
A Novel Approach to Modelling Protein-Protein Interaction
Networks
Zhuhong You1 , Yingke Lei2 , Zhen Ji1 and Zexuan Zhu1
1
Shenzhen City Key Laboratory of Embedded System Design, College of Computer
Science and Software Engineering, Shenzhen University, China
2
Department of Information, Electronic Engineering Institute, China
Abstract. Finding an accurate network null model for protein-protein interactions (PPI)
networks is an important and fundamental problem in today’s systems biology. A number
of graph models were introduced to model and analyze PPI networks. However, because
the previous network models were developed to capture specific network properties or just
mimic the way that real PPI network might have evolved, the whole connectivity information
of PPI networks is not utilized to learn the topological structure of the networks. In this
paper we propose a novel model for PPI networks which is based on geometric random graphs
and employs the entire connectivity information of PPI networks to learn its structure. The
computational experiments show the superiority of the fit of our method over five other network
models to PPI data. Thus, the proposed network null model could facilitate further graph-based
studies of protein-protein interactions and may help infer their hidden underlying biological
knowledge.
Flock by Leader A Novel Machine Learning Biologically Inspired
Clustering Algorithm
Abdelghani Bellaachia and Anasse Bari
The George Washington University, School of Engineering and Applied Sciences
Computer Science Department, USA
Abstract. In the April 2010 Nature research report, it was announced that biological physicists
only very recently discovered that there exists a leadership pattern in flocks of pigeon birds.
The most authoritative birds of the pigeons’ flock take the lead, and followers follow the leaders’
directions. Pigeon leaders’ roles vary over time. Following this unprecedented discovery made
by zoologists at the University of Oxford and Eötvös University, we extend in this paper
the flocking model largely used in computer science. We define a new biologically inspired
clustering algorithm entitled ”FlockbyLeader” that detects hierarchical leaders, discovers their
followers, and enables them to flock based on local proximity in an artificial virtual space to
create clusters. We offer empirical evidence that the algorithm outperforms both the existing
flocking algorithm and the K-means algorithm. We analyze the performance of the algorithm
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based on widely used datasets in the literature.
Cluster KDD: A Visual Clustering and Knowledge Discovery
Platform based on Concept Lattice
Amel Grissa Touzi, Amira Aloui and Rim Mahouachi
Ecole Nationale d’Ingénieurs de Tunis, Tunisia
Abstract. Nowadays, with the evolution of the data in data processing and storage of great
volumes of these diversified data, the software of Data Mining became without context a
necessity for the majority of the users of the Information Systems. Unfortunately, currently
marketed software are very limited and don’t meet all user needs. This software supports only
some classification algorithms and some Knowledge Discovery in Databases (KDD) algorithms
that generate a big number of rules which are not understandable by the end user. Moreover,
these approaches are applicable only for restricted datatype. In this paper, we propose new
software of classification and KDD, called Cluster-KDD, which supports a larger set of data
type and classification algorithm and offers KDD algorithms that generate comprehensible and
exploitable rules by the user.
Machine Learning and Data Mining Methods (II)
Design and Implementation of an Intelligent Automatic Question
Answering System Based on Data Mining
Zhe Qu1 and Qin Wang2
1
2
School of Computer Science, the University of Birmingham, UK
School of Information Science and Engineering, University of Jinan, China
Abstract. This paper applies data mining algorithms into intelligent question answering
system, proposes a question answering scheme based on an improved data mining algorithms
and achieves an efficient intelligent question answering system.
Ontology-based Hazard Information Extraction from Chinese
Food Complaint Documents
Xiquan Yang, Rui Gao, Zhengfu Han and Xin Sui
School of Computer Science and Information Technology, Northeast Normal University,
China
Abstract. Ensuring food safety has become a global research subject these years. In this
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paper, a knowledge model of domain ontology with the aim of hazard information extraction
from Chinese food complaint documents has been designed based on ontology theory. Two
components are essential to this model the learning model and the extraction model. In the
learning model, we propose the algorithms of seed words selection and related words generation.
In the extraction model we propose the algorithms of hazard information extraction and
modifying related words. We compare the results of our method with the method of traditional
ontology based information extraction and traditional information extraction. The results show
that the method we proposed has better indexes.
Design Similarity Measure and Application to Fault Detection of
Lateral Directional Mode Flight System
WookJe Park1 , Sangmin Lee2 , Sanghyuk Lee3 and T.O. Ting3
1
Institute for Information and Electronics Research, Inha University, Korea
2
3
Department of Electronic Engineering, Inha University, Korea
Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool
University, China
Abstract. In this work, we first obtained the similarity measures. The obtained similarity
measures were designed based on well-known Hamming distance. It was also considered by
analyzing the certainty and uncertainty of the fuzzy membership functions. The proposed
similarity measure was applied to the fault detection of primary control surface stuck of
Uninhabited Aerial Vehicle (UAV). At post-failure control surface, if the UAV has controllable
and trimmable using other control surfaces, the UAV is able to fly or returns to the safety region
through reconfiguration of the flight control system. By the calculation of similarity measure,
result could be applicable with the real-time parameter estimation method. Furthermore,
coefficients monitoring make it possible to monitor the occurrence of control surface fault.
The obtained result has the advantage of increasing reliability without adding sensors or any
additional cost.
The Effects of Customer Perceived Disposal Hardship on
Post-Consumer Product Remanufacturing A Multi-Agent
Perspective
Bo Xing, Wen-jing Gao, Fulufhelo V Nelwamondo, Kimberly Battle and Tshilidzi
Marwala
Africa
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Abstract. This research examines the impact of customer perceived disposal hardship on the
post-consumer product remanufacturing activities. The post-consumer products are classified
into three categories according to their corresponding level of intelligence. An agent-based
simulation model is developed and used to explore different intelligent scenarios. The results
suggest that the higher embedded product intelligence, the more effective post-consumer products
acquisition management for remanufacturing.
Research on Domain-Specific Features Clustering Based Spectral
Clustering
Xiquan Yang, Meijia Wang, Lin Fang, Lin Yue and Yinghua Lv
School of Computer Science and Information Technology, Northeast Normal University,
China
Abstract.
Domain-Specific features clustering aims to cluster the features from related
domains into K clusters. Although traditional clustering algorithms can be used to domain-specific
features clustering, the performance may not good as the features have little inter-connection
in related domains. In this paper, we develop a solution that uses the domain-independent
feature as a bridge to connect the domain-specific features. And we use spectral clustering to
cluster the domain-specific features into K clusters. We present theoretical analysis to show
that our algorithm is able to produce high quality clusters. The experimental results show that
our algorithm improves the clustering performance over the traditional algorithms.
Knowledge Annotation Framework Oriented Geospatial Semantic
Web Service Management
Rupeng Liang, Hongwei Li, Jian Chen, Leilei Ma and Hu Chen
Institute of Surveying and Mapping, Information Engineering University, China
Abstract. The paper designs the Geospatial semantic web service framework and konwledge
annotation methodology based on geospatial semantics thoery. And starting from the application
of geospatial semantic web technology, this paper explorers the way to combine the traditional
geospatial web service and semantic web service, so as to handle the problem of service
discovery.This paper designs the knowledge annotation engine to improve the efficiency of
rich semantics service release. And mechanism of semantic matching of service goal is set up
based on rule and reson engine. In the end, this paper dicuss the way to evaluate the effect of
geospatial semantic web service framework.
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Optimizing Supplier Selection with Disruptions by
Chance-Constrained Programming
Wenjuan Zang, Yankui Liu and Zhenhong Li
College of Mathematics & Computer Science, Hebei University, China
Abstract. This work develops a new chance-constrained programming model for supplier
selection problem, in which suppliers are assigned sequentially so that the buyer may have
already one backup when its primary supplier suffers a default due to disruptive risks. In
the proposed optimization problem, costs, quality and lead times are characterized by random
variables. The objective of the proposed model is to maximize the probability of the total
costs no more than a prescribed maximum allowable value. Two probability constraints are
used to guarantee that the probabilities about the total quality and total lead times can satisfy
the known service levels, while other constraints are utilized to ensure our allocation scheme.
By assuming multivariate normal distributions, we can transform the probability objective
and probabilistic constraint functions into their equivalent fractional forms, so we can solve
the deterministic fractional programming by conventional optimization method. Finally, some
numerical experiments have been performed to illustrate the effectiveness of the proposed
solution strategy.
Tuning the Parameters of an Integrate and Fire Neuron via a
Genetic Algorithm for Solving Pattern Recognition Problems
A. Cachón and R.A. Vázquez
Intelligent Systems Group, Universidad La Salle, Mexico
Abstract. Recently, it has been proven that spiking neurons can be used for some pattern
recognition problems. Nonetheless, the spiking neurons models have many parameters that
have to be manually adjusted in order to achieve the desired behavior. This article has the
purpose of showing an optimization method for one such model, the Integrate & Fire spiking
model (I&F). A genetic algorithm (GA) is proposed to automatically adjust the parameters,
removing the need of manual tuning and increasing efficiency. Initial experimentation is done
by tuning the I&F model parameters by hand, to confirm the importance and relevance of
determining the best parameter values. The GA is then used to automatically tune different
parameter combinations of the pattern recognition model, which uses the I&F neuron as core,
to determine which parameters are worth including in the GA. The proposed method was
tested with five different datasets, where no change was required to apply the model to each.
Very good results were achieved in all test cases, but experiments where parameters of the
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neuron model were included converged faster.
Special Session on Data Fusion and Computational Intelligence
Quantized Steady-state Kalman Filter in a Wireless Sensor
Network
Changcheng Wang, Guoqing Qi, Yinya Li and Andong Sheng
School of Automation, Nanjing University of Science and Technology, China
Abstract. This paper addresses the problem of state estimation in the wireless sensor network
(WSN). Firstly, the quantized Kalman filter based on the quantized observations is presented.
Focuses are on tradeoff between the communication energy and the estimation accuracy. A
closed-form solution to the optimization problem for minimizing the energy consumption is
given, where the total energy consumption is minimized subject to a constraint on the stead
state error covariance. An illustrative numerical example is provided to demonstrate the
usefulness and flexibility of the proposed approach.
A Multiple Shape-target Tracking Algorithm by Using MCMC
Sampling
Weifeng Liu, Zhong Chai and Chenglin Wen
Hangzhou Dianzi University, China
Abstract.
Traditional multiple target tracking (MTT) algorithms such as JPDA, MHT,
a basic assume that the targets are points source. This is unrealistic in many cases. We
consider targets with certain geometrical shape and they may give multiple measurements
using the Markov Chain Monte Carlo (MCMC) approach. We aim at estimating the states
of targets, their shape parameters, and number of targets. The proposed approach is based
on the clustering process of finite mixture models (FMM), where the parameters of the FMM
are obtained by the MCMC sampler. The states of the targets are estimated by equivalent
measurement (EQM). The final experiment of three target tracking verifies the proposed
algorithm.
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Modified UDP-Based Semi-Supervised Learning for Fruit
Internal Quality Detection
Peiyi Zhu, Benlian Xu and Jue Gao
School of Electrical and Automation Engineering, Changshu Institute of technology,
China
Abstract. In order to ignore the limitation of local structure features for the traditional linear
dimensionality reduction methods, a new semi-supervised manifold learning is proposed for
apple mealiness detection. Assuming the character of the hyperspectral scattering images, an
unsupervised non-linear dimensionality reduction method unsupervised discriminant projection
(UDP) coupled with sample label information and then develop a semi-supervised learning
algorithm, which can keep the local and global structure and can take advantage of the
important label information, then get geometric structure optimal linear projection. The
classification results with PCA-MUDP are compared with some traditional algorithm. To the
two-class classification of ’mealy’ and ’non-mealy’ apples, the results show that PCA-MUDP
is better than the others.
Research Progress of a Novel Hybrid 3G-VHF Communication
System over Maritime Buoys
Xiaoying Wang, Yingge Chen and Benlian Xu
Department of Computer Science, Changshu Institute of Technology, China,
Abstract. This paper put forward a kind of hybrid 3G-VHF wide range communication
system over buoys and offered latest research progress. Buoy to buoy, buoy to monitoring
center has a self-organizing wide-area topology structure and each node can select or switch
3G or VHF protocol to communicate according to their signal receiving area. System test
shows that it is an effective means to expand maritime ecological monitoring coverage with
very low cost.
Cell Automatic Tracking Technique with Particle Filter
Mingli Lu1,2 , Benlian Xu2 and Andong Sheng1
1
2
School of Automation, Nanjing University of Science & Technology, China
School of Electrical & Automatic Engineering, Changshu Institute of Technology, China
Abstract. Cell motion analysis contributes to research the mechanism of the inflammatory
process and to the development of anti-inflammatory drugs. To gain full dynamics of multiple
cells, a hybrid cell detection algorithm is first designed, which is combined with several methods,
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such as threshold processing, distance transform, watershed negative transform, and shape and
boundary constraint, to reduce over-segmentation and contour missing.By exploiting temporal
information and prior knowledge, a particle-filter-based tracking technique is then proposed for
image sequences to estimate individual state of multiple cells. Simulation results are presented
to support obtained favorable performance of our algorithm.
Ocean Buoy Communication Node Selection Strategy with
Intelligent Ant Behavior
Benlian Xu1 , Qinglan Chen2 , Wan Shi1 and Xiaoying Wang1
1
School of Electrical & Automatic Engineering, Changshu Institute of Technology, China
2
School of Mechanical Engineering, Changshu Institute of Technology, China
Abstract.
In this paper, we propose a novel ant system algorithm for balancing node
energy distribution with maximum the number of complete data transmission in ocean buoy
communication sensor network. In our algorithm, a complete transmission process is regarded
as an ant tour, and each ant stochastically select corresponding node based on such information
as energy function, heuristic function, and pheromone amount. An appropriate objective
function is carefully designed with the expectation of maximizing the number of complete
transmission and uniform minimum energy distribution. Simulation results are presented to
support obtained favorable performance of our algorithm.
Scheduling and Path Planning
Distributed Rate Allocation for Multi-path Routing Based on
Network Utility Maximization
Youjun Bu, Wei He, Kunpeng Jiang and Binqiang Wang
National Digital Switching System Engineering & Technology R&D Center, China
Abstract. In this paper, the multi-path utility maximization problem is investigated. As
the objective function of Multipath routing is not strictly concave, the dual problem may not
be differentiable for every point when one attempts to use a duality approach. To ensure the
objective function strictly concave, a logarithmic term is introduced. We propose a distributed
rate allocation algorithm and prove the convergence performance. Simulation results show that
the mechanism can converge rapidly and make better use of network resources.
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Integration of Battery Charging to Tour Schedule Generation for
An EV-based Rent-a-car Business
Junghoon Lee, Hye-Jin Kim and Gyung-Leen Park
Dept. of Computer Science and Statistics, Jeju National University, Republic of Korea
Abstract. To promote an electric vehicle-based rent-a-car business, this paper designs a
tour scheduler capable of minimizing the waiting time induced by frequent and long battery
charging during the tour. As charging can be conducted during the stay time in each tourist
spot, the waiting time is greatly dependent on the visiting order. After formulating the per-spot
waiting time according to the initial battery amount and the earned distance credit, our scheme
traverses the search space to find the visiting sequence having the minimum waiting time. The
performance measurement results obtained from a prototype implementation reveal that the
proposed scheme can add just 40 minutes when the total trip length is about 195 km, which
may need about a few hour charging in slow chargers, for the given parameter set including
average stay time and inter-spot distance. Moreover, our scheme outperforms the well-known
traveling salesman problem solver by up to 14.7 % in terms of tour time.
A Scalable Algorithm for Finding Delay-constraint Least-cost
End-to-end Path
Yue Han, Zengji Liu, Mingwu Yao and Jungang Yang
ISN State Key Lab, Xidian University, China
Abstract. The Delay-Constrained Least-Cost(DCLC) routing problem is known to be NP
complete, hence various heuristic methods have been proposed for this problem. However,
these heuristic methods have poor scalability as the network scale increases. In this paper
we propose a new method based on Markov Decision Process (MDP) theory to address the
scalability issue of the DCLC routing problem. The proposed algorithm combines the benefit
of the hierarchical routing with the advantage of the probabilistic routing in decreasing the
advertisement of the network state information. Simulation results show that the proposed
method improves the scalability significantly.
Resolving Single Depot Vehicle Routing Problem with Artificial
Fish Swarm Algorithm
Zhi Li, Haixiang Guo, Longhui Liu, Juan Yang and Peng Yuan
School of Economics and Management, China University of Geosciences, China
Abstract. The paper establishes the mathematical model about the vehicles routing problem
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(VRP) of transporting dangerous goods in Zhengzhou Coal Material Supply and Marketing
Company. Then, use artificial fish swarm algorithm to explore the optimal solution of the VRP.
The algorithm first initializes a group of artificial fishes, and a repair operator guarantee the
current state of each artificial fish represents a feasible distribution scheme and then these
artificial fishes find the globally optimal solution through implementation of the designed
random behavior, and behaviors of prey, swarm and follow. At last, it compares with sweep
algorithm and genetic algorithm and the results show the validity of artificial fish swarm
algorithm.
Research of Biogeography Particle Swarm Optimization for
Robot Path Planning
Hongwei Mo and Lifang Xu
Automation College, Harbin Engineering University, China
Abstract. Global path planning of mobile robot in a static environment is one of the most
important problems in the field of mobile robot. Biogeography-based Optimization(BBO)
is a relative new algorithm inspired by biogeography. It mainly uses the biogeography-based
migration operator to share the information among solutions. Particle swarm optimization(PSO)
is a classical heuristic search method whose mechanics are inspired by the swarming or collaborative
behavior of biological populations. This paper presents a new method of global path planning
by combining BBO, PSO and approximate voronoi boundary network(AVBN) in a static
environment. The idea of this paper is to apply position updating strategy of PSO to increase
the diversity of population in BBO and then use the obtained biogeography particle swarm
optimization algorithm(BPSO) to optimize the paths in path network obtained by AVBN
modeling. Experimental results in simulation show that the proposed method is feasible and
effective.
Path Planning Based on Voronoi Diagram and
Biogeography-based Optimization
Ning Huang, Gang Liu and Bing He
Xi’an Hongqing Research Institute of High-tech, China
Abstract. In this paper, an approach of cruise missile hierarchical path planning based on
Voronoi diagram and Biogeography-Based Optimization (BBO) is proposed. First, based on
Voronoi diagram, we establish the threat model to describe the planning environment and
generate the initial paths and navigation nodes. Then the Biogeography-Based Optimization
(BBO) is utilized to search the optimal path. In order to improve the performance of BBO, we
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adopt an integer priority-based encoding, analyze and discuss the migration rate model and
design the migration, mutation and elite operator. Finally, the simulation results show that
this approach is effective in cruise missile path planning.
A New Probability Model for Insuring Critical Path Problem
with Heuristic Algorithm
Zhenhong Li, Yankui Liu and Guoqing Yang
College of Mathematics & Computer Science, Hebei University, China
Abstract.
This paper studies a new class of two-stage stochastic insuring critical path
problems. The first-stage objective function is to minimize the probability of total costs
exceeding a predetermined threshold value, while the second-stage objective function is to
maximize the insured task durations.
The proposed problem is referred to as two-stage
minimum risk problem in order to obtain an adequate description of risk aversion for insuring
critical path problem. For general task duration distributions, we employ the sample average
approximation (SAA) to turn the original insuring critical path problem into its approximation
problem. Since the resulting approximation problem is a two-stage integer programming one,
and the analytical expression of the second-stage value function is unavailable, we cannot
solve it by conventional optimization algorithms. To avoid this difficulty, we develop a hybrid
algorithm by combining the dynamic programming method (DPM) and genotype-phenotype-neighborhood
based binary particle swarm optimization (GPN-BPSO) to solve the resulting approximation
problem, where the DPM is employed to find the critical path in the second-stage programming
problem. Finally, to demonstrate the developed modeling idea, we perform some numerical
experiments via a critical path problem with 30 nodes and 42 arcs. We solve our critical
path problem by the hybrid GPN-BPSO algorithm and compare its solution results with those
obtained by hybrid genetic algorithm (GA) and hybrid binary particle swarm optimization
(BPSO).
Memetic Differential Evolution for Vehicle Routing Problem with
Time Windows
Wanfeng Liu, Xu Wang and Xia Li
Department of Electrical Engineering College of Information Engineering, Shenzhen
University, China
Abstract. In this paper, an improved memetic differential evolution algorithm with generalized
fitness (MDEGF) is proposed for vehicle routing problem with time windows (VRPTW). A
generalized fitness strategy is designed to evaluate the quality of source-individuals, which
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incorporates three simple local search techniques and helps to improve the convergent performance.
Experimental results show that the novel algorithm can solve the VRPTW and obtain better
solution in short time.
The Application of a Hybrid Algorithm to the Submersible
Path-Planning
Chongyang Lv1 , Fei Yu2,1 , Na Yang1 , Jin Feng3 and Meikui Zou1
1
2
3
College of Science, Harbin Engineering University, China
College of Graduate, Harbin Engineering University, China
College of Automation, Harbin Engineering University, China
Abstract. The premature problem is always being a hot topic in the swarm intelligence
research field. PSO could easily fall into local optima because the particles could quickly
get closer to the best particle. To this end, this paper proposes a new hybrid PSO named
HGC-PSO to solve this problem. The mutation mainly considers the particles which have
the better fitness values. Firstly, we add the Gauss mutation to the current global optimal.
Secondly, we use the Cauchy mutation to change the rest of the particles. The purpose of
this method is to increase the population diversity and avoid the PSO fall into local optima.
Finally, HGC-PSO is applied to path planning problem in 3D space for robot in this paper.
The experiment of results prove that the proposed algorithm has higher convergence speed and
precision, besides a path without collision is found.
An Improved Bean Optimization Algorithm for Solving TSP
Xiaoming Zhang1 , Kang Jiang2 , Hailei Wang1 , Wenbo Li1 and Bingyu Sun1
1
Institute of Intelligent Machines, Chinese Academy of Sciences, China
2
Abstract.
Hefei University of Technology, China
Inspired by the transmission of beans in nature, a novel swarm intelligence
algorithm-Bean Optimization Algorithm (BOA) is proposed. In the area of continuous optimization
problems solving, BOA has shown a good performance. In this paper, an improved BOA
is presented for solving TSP, a typical discrete optimization problem. Two novel evolution
mechanisms named population migration and priori information cross-sharing are proposed to
improve the performance of BOA. The improved BOA algorithm maintains the basic idea of
BOA and overcomes the shortcoming that BOA with continuous distribution function can not
be applied to solve the discrete optimization problems. The experimental results of TSP show
that the improved BOA algorithm is suit for solving discrete optimization problems with high
efficiency.
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Visual Simulation and Parallel Implementation
Application of Visual Simulation in Building Marine Engine
Room Simulator
Yelan He1 and Hui Chen2
1
College of Computer Science & Technology, Wuhan University of Technology, China
2
Key Laboratory of Marine Power Engineering and Techology of Ministry of
Communications, Wuhan University of Technology, China
Abstract.
The visual simulation system is an important part of the electric propulsion
simulator. Based on the practice of several marine engine room simulator projects, this paper
discussed application of visual simulation in building marine engine room simulator demand,
compare and analyze different methods and key technologies in system implementation. And
some research achievement was introduced.
Development of Visual Action Design Environment for
Intelligent Toy Robot
Jianqing Mo1,2 , Hanwu He1 and Hong Zhang1,3
1
2
Guangdong University of Technology, China
Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges,
Zhejiang Normal University, China
3
Guangdong South China Institute of Industrial Design, China
Abstract. To improve efficiency of action design for intelligent toy robot, a visual action design
environment is created. Compared with the text-based action design software, it’s intuitive,
good usability. An algorithm for an automatic generation action sequence file (ASF) is present
according to the data structure of action sequence, the file structure of ASF and the judgment
methods of operation rules. Then using Eon Studio as virtual simulation development platform,
the analysis to complicated action sequence files and reappearance of virtual action are realized.
A Robust Adaptive Filter Estimation Algorithm for Vision-based
Cooperative Motions of Unmanned Aerial Vehicle
Chaoxu Li1,2 , Zhong Liu1 , Zhihua Gao1 and Xuesong Li2
1
Electronics Engineering College, Naval University of Engineering, China
2
Electronics College, Air Force Engineering University, China
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Abstract. The thesis intends to propose a robust adaptive time-varying Kalman Filter (KF)
estimation algorithm for a class of Multi-input Multi-output (MIMO) uncetain system. The
proposed algorithm combines a time-varying KF with an adaptive neural network. The KF
is used for estimating the status of target, and the adaptive neural network overcomes the
uncertain factors, being trained by two error signals for the purpose of improving the robust
performance of this proposed algorithm, and the bounded of the estimation error is proved by
Lyapunov theory. Finally, the state estimation of lead aircraft (leader) in the unmanned aerial
vehicle (UAV) cooperative motions is designed on the basis of the proposed method. Simulation
test demonstrates that this proposed algorithm can estimate the state of leader which contains
uncertain factors, and the wing aircraft (follower) can cooperate with the leader well, then the
effectiveness of this algorithm is validated.
Design and Implement of a CUDA Based SPH Particle System
Editor
Xianjun Chen1,2 and Yongsong Zhan1,3
1
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic
Technology, China
2
3
Information Engineering School, Haikou College of Economics, China
College of Computer Science and Engineering, Guilin University of Electronic
Technology, China
Abstract.
Particle system is the key technique for special effects visualization in many
computer graphics applications such as computer games and animations. In this paper, we
propose a novel approach for simplifying particle effect simulation in a particle system editing
environment, where a SPH (Smoothed Particle Hydrodynamics) solver is used for particles
movement simulation. We leverage the parallelism computing architecture of NVIDIA CUDA
to power up the SPH solver implementation. Experimental results show the robustness and
efficiency of the proposed system for real time graphics applications.
Implementations of Main Algorithms for Generalized
Eigenproblem on GPU Accelerator
Yonghua Zhao, Jian Zhang and Xuebin Chi
Supercomputing Center, Computer Network Information Center, Chinese Academy of
Sciences
Abstract.
A generalized eigensystem problem is usually transformed, utilizing Cholesky
decomposition, to a standard eigenproblem. The latter is then solved efficiently by a matrix
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reduction approach based on Householder tridiagonalization method. We present parallel
implementation of an integrated transformation-reduction algorithm on GPU accelerator using
CUBLAS. Experimental results clearly demonstrate the potential of data-parallel coprocessors
for scientific computations. When comparing against the CPU implementation, the GPU
implementations achieve above 16-fold and 26-fold speedups in double precision for reduction
and transformation respectively.
Parallel Max-Min Ant System Using MapReduce
Qing Tan1,2 , Qing He1 and Zhongzhi Shi1
1
The Key Laboratory of Intelligent Information Processing, Institute of Computing
Technology, Chinese Academy of Sciences, China
2
Graduate University of Chinese Academy of Sciences, China
Abstract. Ant colony optimization algorithms have been successfully applied to solve many
problems. However, in some large scale optimization problems involving large amounts of data,
the optimization process may take hours or even days to get an excellent solution. Developing
parallel optimization algorithms is a common way to tackle with this issue. In this paper, we
present a MapReduce Max-Min Ant System (MRMMAS), a MMAS implementation based on
the MapReduce parallel programming model. We describe MapReduce and show how MMAS
can be naturally adapted and expressed in this model, without explicitly addressing any of the
details of parallelization. We present benchmark travelling salesman problems for evaluating
MRMMAS. The experimental results demonstrate that the proposed algorithm can scale well
and outperform the traditional MMAS with similar running times.
Parallel Implementation of Ant-based Clustering Algorithm
Based on Hadoop
Yan Yang1,2 , Xianhua Ni1,2 , Hongjun Wang1,2 and Yiteng Zhao1
1
School of Information Science & Technology, Southwest Jiaotong University, China
2
Abstract.
Key Lab of Cloud Computing and Intelligent Technology, China
Hadoop is a distributed system infrastructure of cloud computing. Based on
the characteristics of ant-based clustering algorithm, the paper implements the parallelization
of this algorithm using MapReduce on Hadoop. The Map function calculates the average
similarity of the object with its neighborhood objects. The Reduce function processes the
objects with the Map outputs and updates related information of both ants and the objects
to get ready for the next job. Results on the Hadoop clusters show that our method can
significantly improve the computational efficiency with the premise of maintaining clustering
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accuracy.
Biometrics and Information Security
Dynamic ROI Extraction Algorithm for Palmprints
K. Hemantha Kumar1 , Munaga.V.N.K. Prasad2 and Arun Agarwal3
1
CSE Department, Vignan University, India
2
3
IDRBT, MasabTank, Hyderabad, India
Department of Computer and Information Sciences, University of Hyderabad, India
Abstract. Region of Interest (ROI) extraction is an important task for palmprint identification.
Earlier reported works used fixed size ROI for the recognition of palmprints. When the fixed size
ROI is used the palm area taken up for recognition is less compared to dynamic ROI extraction.
The proposed algorithm focuses on extraction of maximum possible ROI compared to existing
fixed and dynamic ROI extraction techniques. The experimental results demonstrate that the
proposed approach extracts better ROI on three databases, 1. The PolyU Palmprint Database,
2. CASIA Palmprint Image Database and 3. IIT Delhi Palmprint Database, when compared
to the existing fixed size and dynamic size ROI extraction techniques.
Video-base People Counting and Gender Recognition
Yuen Sum Wong, Cho Wing Tam, Siu Mo Lee, Chuen Pan Chan and Hong Fu
Department of Computer Science, Chu Hai College of Higher Education, Hong Kong
Abstract. Video based people counting and gender recognition are important but challenging
tasks. A neural network method for video-base people counting and gender recognition is
proposed in this paper. A multilayer perceptron structure is constructed and meaningful
features from target video are extracted as input. The neural network is trained by back-propagation
training algorithm. This method is experimented on four videos, including more than 240
peoples. Experiment results have shown the effectiveness of this method.
Facial Expression Recognition Based on Cortex-Like Mechanisms
Heng Zhao1 , Xiaoping Wang2 and Qiang Zhang3
1
School of Life Sciences and Technology, Xidian University, China
2
School of Electronic Engineering, Xidian University, China
3
School of Science, Xidian University, China
Abstract. As an important aspect of human-computer interaction, auto facial expression
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recognition tries to make a computer extract and analyze human’s expression from input face
image. In this paper, a facial expression recognition method based on cortex-like mechanisms
and visual receptive field is presented. Some biological vision theory such as excited field
and inhibitory field are made use to imitate human visual perception mechanism for features
extracting. The support vector machine is used for the facial expression classification. As this
new feature has very good expression ability for visual perception, the experiments on JAFFE
and TFEID databases indicates that the method has the better performance in robustness and
accuracy than some present algorithms based on PCA, LDA, LBP and so on.
A ClientServer Based Mechanism to Prevent ARP Spoofing
Attacks
Haider Salim, Zhitang Li, Hao Tu and Zhengbiao Guo
Computer Science and Technology, Network CenterHuazhong University of Science and
Technology, China
Abstract. Address Resolution Protocol (ARP) is the network part that is responsible for
identifying a Media Access Control (MAC) address of each other, through mapping an IP
address to the corresponding MAC address. Unfortunately, ARP is a stateless protocol, the
weakness in ARP effects directly on the security standards of the network and especially in
Ethernet. In this paper, we propose a new architecture; named a CSIDS Client/Server based
Intrusion Detection System designed to detection and defense against ARP spoofing attacks.
The main idea behind this approach is to implement a real-time analyzing for received ARP
packets and in case of detection a suspicious ARP packet a resolution message will be exchanged
between system parts on the same network. This system is resilience by making at most two
objects (client/server) to work efficiently; on the other hand, just one client is capable of
defending on himself.
A Novel Focused Crawler Based on Breadcrumb Navigation
Lizhi Ying, Xinhao Zhou, Jian Yuan and Yongfeng Huang
Institute of Information Cognition and Intelligence System, Department of Electric
Engineering, Tsinghua University, China
Abstract. In this paper, a novel focused crawler based on Breadcrumb Navigation (BN)
is proposed. It mainly leverages Breadcrumb Navigation in the webpages to reconstruct the
website structures and resolve focused crawling problems. Different from some previous focused
crawlers which use prediction models, the BN crawler firstly samples the web to construct the
semantic forest for websites based on Breadcrumb Navigation, and then searches the forest
87
to find the sub-trees relevant to the given topic. After sampling, the BN crawler only needs
to download the webpages belonging to the relevant sub-forest. By using this method, the
BN crawler costs less time to analyze the Webpage-to-Topic (W2T) similarity but results
in a highly efficient performance. The experimental evidences show that the BN crawler
significantly outperforms Breadth-First and Best-First crawlers in harvest ratio and can be
widely used for most websites.
Other Applications
TAC-RMTO Trading Agent Competition in
Remanufacture-to-Order
Bo Xing, Wenjing Gao, Fulufhelo V. Nelwamondo, Kimberly Battle and Tshilidzi
Marwala
Africa
Abstract. TAC-RMTO is a multi-agent-based game that extends and enhances the trading
agent competition (TAC) scenario to work in remanufacturing area. The game is a configurable
Internet-mediated remanufacture-to-order (RMTO) platform that supports both human and
software agents to interact. The main feature of the TAC-RMTO game is that it supports the
design, development and execution of RMTO market scenarios involving auctions analogous
to those of the TAC by third parties. Thus this platform can be used for conducting research
on RMTO related markets analysis and strategies design as well as for educational purposes.
E-HASH An Energy-efficient Hybrid Storage System Composed
of One SSD and Multiple HDDs
Jiao Hui1,2 , Xiongzi Ge3,4 , Xiaoxia Huang1,2 , Yi Liu1,2 and Qiangjun Ran5
1
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
2
3
Graduate University of the Chinese Academy of Sciences, China
National Supercomputing Center of Shenzhen (Shenzhen Cloud Computing Center),
China
4
School of Computer, Huazhong University of Science and Technology, China
5
College of Communication Engineering, Chongqing University, China
Abstract. In this paper, we describe a novel architecture for Energy-efficient Hybrid storAge
system composed of a flash memory-based solid state disk (SSD) and multiple hard disk drives
88
(HDD) called E-HASH. We arrange SSD and HDDs in a horizontal style. The SSD stores
the most frequently read data and the HDDs store a log of update distance between currently
accessed I/O blocks and their corresponding original blocks and handle all the writes. In
consideration of disk energy saving, the distance and original data will be merged periodically.
Consequently, the underutilized HDD can gain more chance to switch to low power state even
spin down. E-HASH makes use of the fast read performance of SSD and the considerably high
sequential write speed of mechanical HDDs to maximize system performance. The lifespan
of SSD is prolonged due to avoidance of online writes. Our trace-driven simulation combined
with a popular storage simulator has been implemented to evaluate E-HASH performance.
Compared with most of existing SSD/HDD hybrid architectures, our experimental results
show that E-HASH reduces average I/O response time by 72% to RAID and 53% to existing
SSD/HDD storage hierarchies. The energy consumption is reduced by 61% and 45.2%, respectively.
Fault Diagnosis and Optimization for Agent Based on the D-S
Evidence Theory
Jianfang Wang, Qiuling Zhang and Huilai Zhi
School of Computer Science and Technology, Henan Polytechnic University, China
Abstract. To solve only consider the evidence oneself in fault diagnosis conflict using Dempster-Shafer
evidence theory(D-S), not consider environment influence and the different capacities of diagnosis
method, and sometimes because of the more subjectivity, the more qualitative factor and the
less quantitative analysis, the fairness of tender evaluation is suspected. The fault diagnosis and
optimization for Agent based on the D-S evidence theory is proposed. Firstly, the dynamical
adjustment of Agent weight which is integrated into the D-S classified optimization and Agent
with rewards and punishments mechanism as the main content is introduced. Secondly, the
weight is constantly corrected according to the Agent diagnosis result to avoid the subjectivity
and form a closed loop using the adjustment weight, the optimization result and environment
feedback. Finally, the test result shows that our proposed method can raise the accuracy rates
of diagnosis and improve optimization precision and ensure algorithm reliability.
Situation Cognitive in Adjustable Autonomy System Theory and
Application
Rubo Zhang1,2 and Lili Yin1
1
College of Computer Science and Technology, Harbin Engineering University, China
2
State Key Laboratory of Autonomous Underwater Vehichle, Harbin Engineering
University, China
89
Abstract. Situation cognitive in adjustable autonomous system aims to communicate mission
assessment to unmanned vehicle or human, to make adjustable autonomy system know what the
situation is currently, what needs to be done in the present situation, and how the risk of task
is in the present situation. The contribution of this paper is presenting the Situation Cognitive
Module (SCM) for adjustable autonomous system, which encapsulates event detection, threat
assessment and situation reason. The paper concludes by demonstrating the benefits of the
SCM in a real-world scenario. A situation cognitive simulation in an Unmanned Surface
Vehicles (USV) while performing a complicated mission. The method presented in this paper
represents a new SCM to cognitive the situation for adjustable autonomous system. While the
results presented in the paper are based on fuzzy logic and Bayesian network methodology. The
results of this paper can be applicable to land, sea and air robotics in adjustable autonomous
system.
Performance of Termite-hill Routing Algorithm on Sink Mobility
in Wireless Sensor Networks
Adamu Murtala Zungeru1 , Li-Minn Ang2 and Kah Phooi Seng3
1
2
School of Electrical and Electronics Engineering, University of Nottingham, Malaysia
Centre for Communications Engineering Research, Edith Cowan University, Australia
3
School of Computer Technology, Sunway University, Malaysia
Abstract. High efficient and energy-aware routing is an important issue for the design of
resource constrained environments like Wireless Sensor Networks (WSNs). Many protocols
have been developed for WSN that try to overcome the constraints that characterized this
type of networks. Termite based routing protocols can add a significant contribution to assist
in the maximization of the network lifetime without performance degradation. But this is only
possible by means of an adaptable and balanced algorithm that takes into account the main
constraints of WSN. This paper presents a biological inspired self-organized routing protocol
for WSN which is based on termite colony optimization metaheuristic termed Termite-hill.
The main objective of the proposed algorithm is to efficiently relay all the traffic destined
for the sink, and also balance the network energy. The results of our extensive experiments
on Routing Modeling Application Simulation Environment (RMASE) demonstrated that with
sink mobility, our proposed routing algorithm was able to balance the network traffic load and
prolong the network lifetime without performance degradation.
90
Distributed compressed Sensing based on Bipartite graph in
wireless sensor networks
Zhemin Zhuang, Chuliang Wei and Fenlan Li
Department of Electronics Engineering, Shantou University, China
Abstract. In this paper, Combined with bipartite graph thought in graph theory distributed
compressive sensing network architecture based on unbalanced expander is proposed. Meanwhile
we’ve designed the distributed algorithm corresponding with the architecture. And we apply
the distributed compressive sensing network based on unbalanced expander to the fire ground
simulation experiment, through analysis of the mean square error and signal-to-noise ratio, we
prove the proposed model not only takes good effect on reducing nodes’ energy consumption
but also ensuring the performance for the signal reconstruction in noisy and noise-free case.
Identity Manage Interoperation Based on OpenID
Shaofeng Yu1 , Dongmei Li2 and Jianyong Chen1
1
Department of Computer Science and Technology,
2
henzhen University, China
Abstract. In order to extend the trust domain that user locates, a new identity manage
framework based on OpenID standards is designed to build a contact mechanism for different
identity providers. It is composed of two OpenID providers and one service provider. In
order to obey the OpenID standards, two proxy web applications are designed to check the
validate results from OpenID providers and build contact between two of them. By this means,
different OpenID providers can share user’s authentication result among them and users can
use a uniform identity to get more services controlled by different identity providers. The
framework can evidently benefit the development of e-business.
Nonlinear Calibration for N Thermocouple Sensor
Xiaobin Li1 , Haiyan Sun2 , Naijie Xia1 and Jianhua Wang1
1
School of Electrical and Electronic Engineering, Shanghai Institute of Technology,
China
2
School of Ecological Technology and Engineering, Shanghai Institute of Technology,
China
Abstract. Aiming at the problem existed in the application of N type thermocouple with big
and small data samples, the LSSVM method for correcting nonlinear error of thermocouple
sensor and RBF neural network using PSO are introduced. These methods are compared with
91
some commonly used calibration methods, such as BP neural network, RBF neural network
and ANFIS method. The result of experiment shows that the nonlinear calibration method
based on LSSVM and PSO- RBF has higher precision than the methods based on BP, RBF or
ANFIS. LSSVM method was used to test fire-path temperature in anode baking process, and
satisfactory result was achieved. Test result proves that the method is effective.
Mathematics
The Existence of Nonoscillatory of a Third-order Quasilinear
Ordinary Differential Equation
Jinyan Wang
College of Information and Science, Beifang University of Nationalities, China
Abstract. This paper is concerned with nonoscillation solutions of the third order quasilinear
α−1 0 00
differential equation (p(t)|u0 |
β−1
u ) + q(t)|u|
u = 0. Where α > 0, β > 0, p(t) and q(t) are
continuous functions on an interval [α, +∞), satisfying p(t) > 0, q(t) > 0. Give the necessary
and sufficient conditions that equation has specific nonoscillatory solutions when t → ∞ the
R ∞ t 1/α
equation satisfies a p(t)
dt = ∞.
Connective Stability Analysis for a Class of Large-scale Systems
Based on the Inclusion Principle
Xuebo Chen1 , Xufei Lu1 , Xinyu Ouyang1,2 and Xiao Xiao1
1
School of Electronics and Information Engineering, Liaoning University of Science and
Technology, China
2
School of Control Science and Engineering, Dalian University of Technology, China
Abstract.
Based on the inclusion principle, a judging method of connective stability is
presented for a class of linear interconnected large-scale systems. First, the system is decomposed
into a group of subsystems. Through stability analysis and judgment of each subsystem, a
group of judgment matrices are obtained. After being permuted and corrected, these judgment
matrices can be composed an extended judgment matrix. According to the constraints conditions
of inclusion principle, it can be contracted into a judgment matrix. If the judgment matrix is
diagonally dominant and an M-matrix, then the system is connective stable. Parameters of
judgment matrices are derived from each subsystem, which are easy to be obtained. Finally,
it takes an example to show that the method is of the validity.
92
Calculations of Amounts of Joint Reserve of Airplanes in Civil
Aviation Systems
Zhe Yin1,2 , Yunfei Guo1 , Feng Lin1 , Di Gao1 and Maosheng Lai2
1
2
Mathematics Department, Yanbian University, China
Department of Information Management, Peking University, China
Abstract. In this paper, based on the ”airplane repairing” model and ”airplane purchasing”
model, for the systems of single unit from different airline companies where reserve of airplanes
starts at the same time under the same storage conditions that normal probability is not less
than during the designed storage period, the spare parts number M for N identical systems is
derived. According to the same principle, ideas of joint reserve of airplanes have been proposed,
and the validity has been checked through the examples of Weibull distribution.
About Eigenvalues from Embedding Data Complex in Low
Dimension
Jiun-Wei Liou and Cheng-Yuan Liou
Department of Computer Science and Information Engineering, National Taiwan
University, Taiwan
Abstract. LLE(Local linear embedding) and Isomap are widely used approaches for dimension
reduction on data complex. The embedding results from the two methods are eigenvectors from
solving specific matrices. The corresponding eigenvalues for the selected eigenvectors have
important meaning for the embedding results. In this paper, the k-nn method and -distance
approach are used for neighborhood function with parameters. Then, different dataset and
parameters will be applied to obtain the embedding results and eigenvalues. The main change
of eigenvalues and the corresponding embedding results will be shown in this paper.
Rotation of Matrix and DUET for Underdetermined BSS
Gongxian Sun1 , Feng Gao1 and Ming Xiao2
1
School of Electrics & Information Engineering, South China University of Technology,
China
2
School of Computer & Infor-mation, Guangdong University of Petrochemical
Technology, China
Abstract. The source recovery in underdetermined blind source separation is discussed in this
paper. DUET is a famous source recovery method in underdetermined BSS. It requires the
sources are approximately disjoint. To obtain better results in sources recovery, an improved
93
DUET algorithm is proposed for two mixtures.
The improved algorithm utilizes DUET
after the rotation of the mixing matrix and mixtures. Several speech signals experiments
demonstrate its performance and practical.
A Differentiating Evolutionary Computation Approach for the
Multidimensional Knapsack Problem
Meysam Mohagheghi Fard, Yoon-Teck Bau and Chien-Le Goh
Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia,
Malaysia
Abstract. In this paper, the DEC (Differentiating Evolutionary Computation) algorithm
is presented for solving a zero-one multidimensional knapsack problem. It has three new
improvements. They are the use of a chromosome bank for elitism, the use of the superior
clan and the inferior clan to improve exploitation and exploration, and the use of genetic
modification to enable faster convergence. The experimental results have shown that the DEC
algorithm is better than a greedy algorithm and a generic genetic algorithm. It can find
solutions very close to those found by the algorithm proposed by Chu & Beasley.
The Evaluation of Data Uncertainty and Entropy Analysis for
Multiple Events
Sanghyuk Lee and T.O. Ting
Dept. Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, China.
Abstract. In this paper, data analysis for multiple facts was carried out via fuzzy entropy.
The entropy for the fuzzy data with respect to multiple facts was designed through distance
measure. The obtained fuzzy entropy was used to analyze the uncertainties with respect to
each fact. By summarizing fuzzy entropy, data uncertainty information was limited by the
total fact (n) minus one, that is, n − 1. The bounded calculation of data uncertainty to each
fact was also proven for multiple facts and the decision of fuzzy data to the certain fact among
multiple facts has been considered with the help of fuzzy entropy calculation.
94
An Improved Artificial Immune Recognition System Based on
the Average Scatter Matrix Trace Criterion
Xiaoyang Fu1 and Shuqing Zhang2
1
Department of Computer Science and Technology, Zhuhai College of Jilin University,
China
2
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences,
China
Abstract. This paper proposed an improved artificial immune recognition system (IAIRS)
based on the average scatter matrix trace (ASMT) criterion. In essence, the artificial immune
recognition system (AIRS) is an evolving algorithm. Through clonal expansion, affinity maturation,
resource competition and immune memory etc, a set of new samples (memory cells) is produced.
The ASMT of memory cells will be decreased and the minimized ASMT can be as the optimal
criterion of AIRS. The IAIRS algorithm is demonstrated on a number of benchmark data sets
effectively.
A Danger Feature Based Negative Selection Algorithm
Pengtao Zhang1,2 and Ying Tan1,2
1
2
Key Laboratory of Machine Perception (MOE), Peking University
Department of Machine Intelligence, School of Electronics Engineering and Computer
Science, Peking University, China
Abstract. This paper proposes a danger feature based negative selection algorithm (DFNSA).
The DFNSA divides the danger feature space into four parts, and reserves the information of
danger features to the utmost extent, laying a good foundation for measuring the danger of
a sample. In order to incorporate the DFNSA into the procedure of malware detection, a
DFNSA-based malware detection (DFNSA-MD) model is proposed. It maps a sample into
the whole danger feature space by using the DFNSA. The danger of a sample is measured
precisely in this way and used to classify the sample. Eight groups of experiments on three
public malware datasets are exploited to evaluate the effectiveness of the proposed DFNSA-MD
model using cross validation. Comprehensive experimental results suggest that the DFNSA is
able to reserve as much information of danger features as possible, and the DFNSA-MD model is
effective to detect unseen malware. It outperforms the traditional negative selection algorithm
based and the negative selection algorithm with penalty factor based malware detection models
in all the experiments for about 5.34% and 0.67% on average, respectively.
95
Alpha Matting Using Artificial Immune Network
Zhifeng Hao1 , Jianming Liu1 , Xueming Yan2 , Wen Wen1 and and Ruichu Cai1,3
1
Faculty of Computer Science ,Guangdong University of Technology, China
2
3
School of Computer science,South China Normal University, China
State Key Laboratory for Novel Software Technology, Nanjing University, China
Abstract. Alpha matting refers to the problem of softly extracting the foreground from an
image. To solve the matting problem initialized with a trimap (a partition of the image into
three regions: foreground, background and unknown pixels), an approach based on artificial
immune network is proposed in this paper.The method firstly uses Artificial Immune Network(aiNet)
to map the color feature for unknown region, attaining the color subset both on the foreground
and background color distributions,then estimate the alpha matte for unknown region, and
finally apply guided filter to improve the matting results. Experiments on several different
image data sets show that the proposed method produces high-quality matting results.
Particle Swarm Optimize Fuzzy Logic Memberships of AC-drive
Nasseer K. Bachache and Jinyu Wen
College of Electrical and Electronic Engineering, Huazhong University of Science and
Technology (HUST), China
Abstract. A numerous industries need to the applications of a variable speed underneath
a high quality controller, in recent years the ac drive has been one of the most important
strategies in speed control due to a high reliability of induction motor and the development in
power electronic field, this paper proposed the Fuzzy Logic Controller (FLC) to get a superior
behavior over a wide range of speed variation. Fuzzy logic is a robust controller for linear
and non-linear system, but adjusting fuzzy controller parameters is a challenging problem, it
depends on operator’s experience. (Nowadays, many intelligent techniques are used for this
task). In this work Particle Swarm Optimization (PSO) algorithm is utilized to adapting
centers and width of triangle membership functions, this method deal with a simulation of a
complete mathematical model of an induction motor and its inverter. The simulation results
demonstrate that the proposed PSO-FLC speed controller realizes a good dynamic behavior
of the I.M compared with conventional FLC and PID controller.
96
Multistep Fuzzy Classifier Forming with
Cooperative-Competitive Coevolutionary Algorithm
Roman Sergienko and Eugene Semenkin
Departement of System Analysis and Operation Research, Siberian State Aerospace
University, Russian Federation
Abstract. This paper is about multistep fuzzy classifier forming method with cooperative-competitive
coevolutionary algorithm. Cooperative-competitive coevolutionary algorithm automatically
allows avoiding the problem of genetic algorithm parameters setting. This approach is included
in a new method combining Michigan and Pittsburgh approaches for fuzzy classifier design.
The procedure is performed several times. After each step classification efficiency is increased
and standard deviation of values is decreased. Results of numerical experiments for machine
learning problems from UCI repository are presented.
97
Index (c=chair cc=cochair)
Chou, Penchen, 25, 25cc
Cunningham, Bryan, 27
Agarwal, Arun, 36
Aloui, Amira, 32, 32cc
Ang, Li-Minn, 37
Ardjmand, Ehsan, 26
Ariffin, Junaidah, 26
Deng, Miao, 29
Deng, Xiangyang, 28
Deng, Yong, 28
Ding, Hong, 31
Bachache, Nasseer K., 38
Bari, Anasse, 32
Battle, Kimberly, 28, 33, 36
Bau, Yoon-Teck, 38
Bazargan, Masoud, 26
Bellaachia, Abdelghani, 32
Bu, Youjun, 34
Bukhtoyarov, Vladimir, 31
Ersoy, Nur, 29
Fang, Lin, 33
Fang, Wei, 26
Fang, Xiaochun, 28
Fard, Meysam Mohagheghi, 38
Fei, Rong, 27
Feng, Jin, 35
Filho, C. J. A. Bastos, 25, 25c
Folly, Komla A, 28, 28c
Fu, Hong, 36, 36c
Fu, Xiaoyang, 38
Cachón, A., 33
Cai, and Ruichu, 38
Camlidere, Zelal Seda, 29
Cao, Jinlong, 27
Cao, Yong, 27
Chai, Yi, 27
Chai, Zhong, 34
Chan, Chuen Pan, 36
Chen, Bolun, 28
Chen, Haoyong, 26
Chen, Hu, 33
Chen, Hui, 35
Chen, Jian, 33
Chen, Jianyong, 37
Chen, Ling, 28
Chen, Qinglan, 34
Chen, Sanfeng, 31
Chen, Siping, 25
Chen, Tiejun, 30
Chen, Walter, 25cc, 26
Chen, Xi, 28
Chen, Xianjun, 36
Chen, Xuebo, 37
Chen, Yingge, 34
Cheng, Shi, 25, 26, 31
Cheng, Wei-Chen, 32
Cheng, Xu, 27
Chi, Xuebin, 36
Chi, Yuhong, 25
Cho, Hsun-Jung, 30
Gao, Di, 37
Gao, Donghui, 25
Gao, Feng, 38
Gao, Hongyuan, 27
Gao, Jue, 34
Gao, Rui, 33
Gao, Wei, 30
Gao, Wen-jing, 33
Gao, Wenjing, 28, 36
Gao, Yuelin, 26
Gao, Zhihua, 35
Gaud, Nicolas, 32
Ge, Xiongzi, 37
Goh, Chien-Le, 38
Gong, Dunwei, 26
Goyal, Rahul, 27
Gu, Huaxi, 32
Guo, Guanqi, 32
Guo, Haixiang, 35
Guo, Yunfei, 37
Guo, Zhengbiao, 36
Han, Yue, 34
Han, Zhengfu, 33
Hao, Zhifeng, 32, 38
98
He, Bing, 35
He, Hanwu, 35
He, Liang, 32
He, Qing, 35c, 36
He, Rong, 30
He, Shan, 30
He, Wei, 34
He, Yelan, 35
Hei, Xinghong, 27
Hsu, Chia-Chun, 30, 30c
Hu, Tao, 31
Hu, Ying, 26
Huang, Han, 30, 32
Huang, Ning, 35
Huang, Xiaoxia, 37
Huang, Yongfeng, 36
Huang, Zhenhua, 30
Hui, Jiao, 37
Li, Penghua, 27
Li, Wei, 27
Li, Weixing, 32
Li, Wenbin, 32
Li, Wenbo, 35
Li, Xia, 35
Li, Xianghui, 31
Li, Xiaobin, 37
Li, Xiaolong, 25
Li, Xuesong, 35
Li, Ya, 31
Li, Ying, 31
Li, Yinya, 34
Li, Zhenhong, 33, 35
Li, Zhi, 35
Li, Zhitang, 36
Liang, Jing, 30
Liang, Rupeng, 33
Liang, Yongsheng, 29
Liao, Huilian, 26
Lin, Dongmei, 31
Lin, Feng, 37
Lin, Guangming, 29, 31
Lin, Qinhe, 28
Lin, Xiufang, 26
Ling, Haifeng, 31
Liou, Cheng-Yuan, 37, 37c
Liou, Jiun-Wei, 37
Liu, Gang, 35
Liu, Guanqun, 27
Liu, Hao, 30
Liu, Jianming, 38
Liu, Lei, 27
Liu, Longhui, 35
Liu, Mingzhe, 31
Liu, Shusen, 32
Liu, Wanfeng, 35
Liu, Wei, 29
Liu, Weifeng, 34
Liu, Xinyu, 27
Liu, Yankui, 33, 35
Liu, Yi, 37
Liu, Zengji, 34
Liu, Zhong, 35
Long, Jin, 31
Lu, Mingli, 34
Lu, Xufei, 37
Ji, Chunlin, 28
Ji, Zhen, 25, 26, 30, 32
Jiang, Kang, 35
Jiang, Kunpeng, 34
Jiang, Langfan, 25
Jimoh, A.A., 26
Kang, Zuo, 31
Kilic, Hurevren, 29
Kim, Hye-Jin, 34
Koc, Ekin, 29
Koukam, Abderrafiâa, 32
Kumar, K. Hemantha, 36
Kundu, Anirban, 28
Lai, Changtao, 31
Lai, Maosheng, 37
Lee, Junghoon, 34, 34cc
Lee, Sanghyuk, 25, 33, 38
Lee, Sangmin, 33
Lee, Siu Mo, 36
Lei, Yingke, 32
Leu, Min-Shyang, 25
Li, Bin, 32
Li, Chaoxu, 35
Li, Dongmei, 37
Li, Fenlan, 37
Li, Haiyuan, 27
Li, Hongwei, 33
99
Luo, Lan, 28
Lv, Chongyang, 35
Lv, Yinghua, 33
Ma, Leilei, 33
Maeda, Michiharu, 28c, 29
Mahouachi, Rim, 32
Marwala, Tshilidzi, 28, 33, 36
Mo, Hongwei, 34c, 35
Mo, Jianqing, 35
Mohamed, Azlinah, 26
Mozayani, Naser, 32, 32c
Naseri, Mohammad Reza Amin, 26, 26c
Nelwamando, Fulufhelo V., 28
Nelwamondo, Fulufhelo V, 33
Nelwamondo, Fulufhelo V., 36, 36c
Ni, Xianhua, 36
Niu, Ben, 28, 28cc
Numbi, B.P., 26
Ouyang, Xinyu, 37
Palade, Vasile, 26
Pan, Feng, 32
Pan, Fuping, 32
Park, Gyung-Leen, 34
Park, WookJe, 33
Prasad, Munaga.V.N.K., 36
Qi, Guoqing, 34
Qian, Tao, 28
Qiao, Ying, 31
Qin, Quande, 31
Qu, Boyang, 30
Qu, Zhe, 33
Ran, Qiangjun, 37
Razavi, Seyed Naser, 32
Ribeiro, S. F., 25
Salim, Haider, 36
Semenkin, Eugene, 30, 31, 38
Semenkina, Maria, 30
Seng, Kah Phooi, 37
Sergienko, Roman, 38
Sharma, Tushar, 27
Shen, Linlin, 30
Shen, Zheping, 26
Sheng, Andong, 34
Shi, Wan, 34
Shi, Youqun, 27
Shi, Yue, 28
Shi, Yuhui, 25, 26, 31, 31c
Shi, Zhiguo, 27, 27cc
Shi, Zhongzhi, 36
Shimohara, Katsunori, 27
Shou, Yongyi, 31
Siti, M.W., 25c, 26
Suganthan, Ponnuthurai Nagaratnam, 30
Sui, Xin, 33
Sun, Bingyu, 35
Sun, Fuchun, 25
Sun, Gongxian, 38
Sun, Haiyan, 37
Sun, Jun, 26
Sun, Ying, 26
Sun, Yiwen, 30
Tam, Cho Wing, 36
Tan, Qing, 36
Tan, Xu, 31
Tan, Ying, 38, 38c
Tanev, Ivan, 27
Tang, Cheng, 27
Tang, Fei, 31
Tang, Lixin, 27
Tian, Sulei, 32
Tian, Yubo, 25
Ting, T.O., 25, 33, 33cc, 38
Tiwari, Ritu, 27
Tong, Haibo, 27
Touzi, Amel Grissa, 32
Tsai, Fuan, 26
Tsai, Ming-Tang, 25
Tsuda, Shinya, 29
Tu, Hao, 36
Tu, Jun, 27
Tungadio, D.H., 26
Vázquez, R.A., 33, 33c
Vitorino, L. N., 25
Wang,
Wang,
Wang,
Wang,
100
Binqiang, 34
Changcheng, 34
Dong, 31
Hailei, 35
Wang, Hongjun, 36
Wang, Jianan, 26
Wang, Jianfang, 37
Wang, Jianhua, 37
Wang, Jingwen, 28
Wang, Jinyan, 37
Wang, Lei, 26cc, 27, 30
Wang, Meijia, 33
Wang, Qin, 33
Wang, Tianmiao, 27
Wang, Xiaoping, 36
Wang, Xiaoying, 34
Wang, Xu, 35
Wei, Chuliang, 37
Wei, Hongxin, 27c
Wei, Hongxing, 27
Wei, Junming, 27
Wei, Shanbi, 27
Wen, Cheng, 25
Wen, Chenglin, 34
Wen, Jinyu, 38
Wen, Wen, 38
Wong, Yuen Sum, 36
Wu, Chen, 31
Wu, Henggao, 27
Wu, Jiansheng, 31
Wu, Qidi, 30
Wu, Qinghua, 26
Wu, Szu-Wzi, 25
Wu, Xiaojun, 26
Wu, Yali, 31
Wu, Yingjun, 30
Wu, Yuheng, 28
Xia, Naijie, 37
Xiang, Wei, 28
Xiao, Jiangyang, 27
Xiao, Jie, 32
Xiao, Ming, 38
Xiao, Xiao, 37
Xing, Bo, 28, 33, 36
Xu, Benlian, 34, 34c
Xu, Lifang, 35
Xu, Peng, 28
Xu, Tianwei, 30
Xu, Weidi, 32
Xu, Wenbo, 26
Xu, Xinli, 31
Xu, Yuan, 30
Xue, Jingqian, 31
Yan, Meng Ling, 30
Yan, Taishan, 32
Yan, Xueming, 38
Yan, Yongyong, 32
Yang, Guoqing, 35
Yang, Juan, 35
Yang, Jungang, 34
Yang, Na, 35
Yang, Xiquan, 33
Yang, Yan, 36
Yao, Mingwu, 34
Yeh, Ming-Feng, 25
Yin, Cheng, 32
Yin, Hongpeng, 27
Yin, Jiao, 28
Yin, Lili, 37
Yin, Zhe, 37
Ying, Lizhi, 36
You, Zhuhong, 32
Yu, Chunming, 25
Yu, Fei, 35
Yu, Ling, 28
Yu, Qingsheng, 31
Yu, Shaofeng, 37
Yuan, Jian, 36
Yuan, Peng, 35
Yue, Lin, 33
Yusoff, Marina, 26
Zang, Wenjuan, 33
Zhan, Yongsong, 36
Zhang, Bixia, 32
Zhang, Hong, 35
Zhang, Jian, 36
Zhang, Jihong, 29
Zhang, Junbo, 32
Zhang, Li Feng, 30
Zhang, Limin, 28
Zhang, Pengtao, 38
Zhang, Ping, 25
Zhang, Qiang, 36
Zhang, Qiao, 27
Zhang, Qiuling, 37
Zhang, Rubo, 27, 37
Zhang, Shuqing, 38
101
Zhang, Wanqiu, 26
Zhang, Xiaoming, 35
Zhang, Yong, 26
Zhang, Yongwei, 30
Zhang, Yungang, 30
Zhang, Zili, 28
Zhao, Heng, 36
Zhao, Sen, 32
Zhao, Weidong, 28
Zhao, Yiteng, 36
Zhao, Yonghua, 36
Zheng, Yujun, 31
Zhi, Huilai, 37
Zhou, Chen Xi, 30
Zhou, Dadian, 26
Zhou, Huan, 28
Zhou, Jiarui, 25
Zhou, Qian, 32
Zhou, Xinhao, 36
Zhu, Peiyi, 34
Zhu, Yu, 32
Zhu, Zexuan, 25, 30, 30c, 32
Zhuang, Zhemin, 37
Zou, Meikui, 35
Zungeru, Adamu Murtala, 36cc, 37
102

The ICSI 2012 Final Program

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