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The Fourth InternationalConference on Swarm

Intelligence

Technical Program & Abstracts

June 12-15, 2013

New Gloria Garden Plaza Hotel, Harbin, China

http://www.ic-si.org/

Welcome Message from General Chairs

We are warmly welcoming you, swarm intelligence researchers and practitioners from all

over the world, to attend The Fourth International Conference on Swarm Intelligence (ICSI

2013) in the best season of Harbin City of HeLongJiang Province. Located in the center of

Northeast Asia, Harbin is called the bright pearl on the Bridge of Eurasia Land, and also as

an important hub of Eurasia Land Bridge and air corridor.

As a sequel of the successful Shenzhen event (ICSI 2012), Chongqing event (ICSI 2011) and

Beijing event (ICSI 2010), the ICSI 2013 is the fourth event in its series. We believe that you

will enjoy this important and hard-to-get gathering for the swarm intelligence community.

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 2013 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 2013 is New Gloria Garden Plaza Hotel, Harbin. The New Gloria

Garden Plaza Hotel (Harbin) is decorated in Five-Star Standard with 360 guest rooms, meeting

rooms and conference halls. It is dominating in its location, next to Shong Hua Jiang River

and Harbin Flood Memorial Tower, situated at the north end of the famous Central Street.

Located in the center of Northeast Asia, Harbin is an important hub of Eurasia Land Bridge

and air corridor. The special historical course and geographical position has contributed to

Harbin, the beautiful city with an exotic tone, which not only brings together the historical

culture of northern ethnic minorities, but also combines western and eastern culture. It is a

famous historical and tourist city in China, with many beautiful names such as the City of

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The 4th International Conference on Swarm Intelligence, June 12-15, 2013, Harbin, China

Culture, the City of Music, Ice City, A Pearl under the Neck of the Swan, Eastern Moscow

and Eastern Little Paris.

On the other hand, the ICSI 2013 will definitely contribute a lot to the globalization of

research and teaching in China in addition to the enhancement of the research horizons of

the conference delegates. Certainly, the participants of the ICSI 2013 can also enjoy multiple

cultures, beautiful landscapes and night scenes in Harbin, and the hospitality from the people

in the northern part of china.

On behalf of the general chairs and organizing committees of the ICSI 2013, I wish the ICSI

2013 will be a memorable event for you to stay in Harbin, China.

Sincerely yours!

ICSI 2013 General Chairs

Russell C. Eberhart

Indiana University Purdue University, USA

Guihua Xia

Harbin Engineering University, China

Ying Tan

Peking University, China

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Welcome Message from Program Committee Chair

The Fourth International Conference on Swarm Intelligence (ICSI 2013) is the fourth

gathering in the world, as a sequel of the ICSI 2012 (Shenzhen), the ICSI 2011 (Chongqing) and

the 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 2013 received 268 submissions from about 613 authors in 35 countries and regions

(Algeria, Australia, Austria, Bangladesh, Bonaire Saint Eustatius and Saba, Brazil, Canada,

Chile, China, Czech Republic, France, Germany, Hong Kong, India, Islamic Republic of Iran,

Italy, Japan, Republic of Korea, Malaysia, Mexico, Pakistan, Palestinian Territory Occupied,

Romania, Russian Federation, Saudi Arabia, Singapore, South Africa, Spain, Sweden, Switzerland,

Chinese Taiwan, Thailand, Tunisia, Turkey, United Kingdom, USA) across six continents

(Asia, Europe, North America, South America, Africa, and Oceania). Each submission was

reviewed by at least two reviewers, and on average 2.5 reviewers. Based on rigorous reviews

by the Program Committee members and reviewers, 129 high-quality papers were selected for

publication in this proceedings volume with an acceptance rate of 48.13

In addition to the contributed papers, the ICSI 2013 technical program included two plenary

speeches by Qidi Wu (past president of Tongji University, past vice-minister of Chinese Ministry

of Education) and Qingfu Zhang (University of Essex, UK).

As the Program Committee Chair of the ICSI 2013, I would like to express sincere thanks to

Harbin Engineering 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. 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 2013 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 Zhongyang Zheng, Weiwei Hu, Shaoqiu Zheng, Ke Ding, Guyue Mi and Chao Yu for their

time and outstanding work in the organization of ICSI 2013.

We sincerely hope that all ICSI 2013 participants will enjoy attending conference sessions

and social activities, meeting research partners, and setting up new research collaborations.

Have a pleasant stay in Harbin and enjoy!

Cheers!

ICSI 2013 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 10

Committees 11

Organizing Committees . . . . . . . . . . . . . . . . . . . . . . . . . . 11

International Program Committee Members . . . . . . . . . . . . . . . 13

Program Schedule Overview 15

Talks 16

Plenary Talk I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Plenary Talk II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Technical Program 18

June 13, 2013(Thursday) . . . . . . . . . . . . . . . . . . . . . . . . . . 20

June 14, 2014(Friday) . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Abstracts 29

Index 45

Map Overview back endpage

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Venue

The ICSI’2013 will be held at New Gloria Garden Plaza Hotel. New Gloria Garden Plaza

Hotel Harbin ensures to complement its large selection of rooms with a high level of service.

It is comforting to know that the rooms are of a 4-star level.

Dine at one of the 2 restaurants of New Gloria Garden Plaza Hotel Harbin. A gift shop

and a mini-market are part of the conveniences. Take advantage of services such as a concierge

service, a bell boy service, or even a technology help desk. Business meetings can be organised

in one of 4 meeting rooms. Work trips are common in Harbin, therefore this hotel has a

conference space and a business center alongside an audio-visual equipment set. Choose from

one of the 264 rooms.

Air conditioned rooms make you feel comfortable during your stay at New Gloria Garden

Plaza Hotel Harbin, whatever the weather outside is. A wake-up service is provided when you

stay in the rooms of this accommodation, alongside a daily housekeeping service, both provided

for your comfort and convenience. Rooms with a city view can be reserved in advance. Rooms

here come with kitchen facilities, including a refrigerator and a coffee/tea maker.

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New Gloria Garden Plaza Hotel

Lobby Bedrooms

Restaurant

Useful Local Telephone NumbersCountry code: +86State code: 0451Directory Enquiry: 114Emergency Service: police 110, fire 119 and ambulance 999

Internet AccessProvide in-room broadband internet access.Wireless access facilities in hotel lobby, restaurants and conference rooms.

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Location Map of Third Floor

9

Sponsors

Sponsors

Harbin Engineering 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

Springer-Verlag

Lecture Notes in Computer Science

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Organizing Committees

General Chairs

Russell C. Eberhart Indiana University Purdue University Indianapolis, USA

Guihua Xia Harbin Engineering University, China

Ying Tan Peking University, China

Program Committee Chair

Yuhui Shi Xi’an Jiaotong-Liverpool University, China

Organizing Committee Chair

Hongwei Mo Harbin Engineering University, China

Advisory Committee Chairs

Gary G. Yen Okahoma University, USA

Xingui He Peking University, China

Technical Committee Chairs

Carlos A. Coello Coello CINVESTAV-IPN, Mexico

Xiaodong Li RMIT University, Australia

Andries Engelbrecht University of Pretoria Pretoria, South Africa

Ram Akella University of California, USA

Martin Middendorf University of Leipzig, Germany

Lin Zhao Harbin Engineering University, China

Special Session Chairs

Fernando Buarque Universidade of Pernambuco, Brazil

Benlian Xu Changsu Institute of Technology, China

Publication Chairs

Radu-Emil Precup Politehnica University of Timisoara, Romania

Finance and Registration Chairs

Chao Deng Peking University, China

Andreas Janecek University of Vienna, Austria

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Publicity Chairs

Hideyuki Takagi Kyushu University, Japan

Shan He University of Birmingham, UK

Yew-Soon Ong Nanyang Technological University, Singapore

Juan Luis Fernandez

Martınez

University of Oviedo, Spain

Jose Alfredo Ferreira Costa Federal University, Brazil

Kejun Wang Harbin Engineering University, China

Local Arrangement Chair

Lifang Xu Harbin Engineering University, China

Mo Tang Harbin Engineering University, China

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International Program Committee Members

Alan Jennings, University of Dayton, USA

Andreas Janecek, University of Vienna, Austria

Andrei Lihu, Politehnica University of Timisoara, Romania

Arindam K. Das, University of Washington, USA

Ba-Ngu Vo, Curtin University, Australia

Benlian Xu, Changshu Institute of Technology, China

Bernd Meyer, Monash University, Australia

Bijaya Ketan Panigrahi, Indian Institute of Technology Delhi, India

Bing Wang, Tsinghua University, China

Carmelo J. A. Bastos Filho, University of Pernambuco, Brazil

Changan Jiang, RIKEN-TRI Collaboration Center for Human-Interactive Robot Research,

Japan

Colin Johnson, University of Kent, UK

Dongbin Zhao, Institute of Automation, Chinese Academy of Science, China

Dunwei Gong, China University of Mining and Technology, China

Farrukh Khan, FAST-NUCES Islamabad, Pakistan

Fernando B. De Lima Neto, University of Pernambuco, Brazil

Germano Lambert-Torres, Itajuba Federal University, Brazil

Guangbin Huang, Nanyang Technological University, Singapore

Guoping Liu, University of Glamorgan, UK

Haibin Duan, Beijing University of Aeronautics and Astronautics, China

Haibo He, University of Rhode Island, USA

Hideyuki Takagi, Kyushu University, Japan

Jiahai Wang, Sun Yat-sen University, China

Jianhua Liu, Fujian University of Technology, China

Jie Zhang, Newcastle University, UK

Jose Alfredo Ferreira Costa, Universidade Federal do Rio Grande do Norte, Brazil

Ju Liu, Shandong University, China

Juan Luis Fernandez Martınez, University of Oviedo, Spain

Jun Zhang, Waseda University, Japan

Jun Hu, Chinese Academy of Sciences, China

Junqi Zhang, Tongji University, China

Ke Tang, University of Science and Technology of China, China

Lei Wang, Tongji University, China

Licheng Jiao, Xidian University, China

Lifeng Zhang, Renmin University of Chia, China

Ling Wang, Tsinghua University, China

Lipo Wang, Singapore Technology University, Singapore

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Lu Hongtao, Shanghai Jiao Tong University, China

Luca Gambardella, IDSIA, Switzerland

Manuel Chica, European Centre for Soft Computing, Spain

Maoguo Gong, Xidian University, China

Mark Embrechts, Rensselaer Polytechnic Institute, USA

Martin Middendorf, University of Leipzig, Germany

Mingcong Deng, Tokyo University of Agriculture and Technology, Japan

Mo Hongwei, Harbin Engineering University, China

Mohammad Taherdangkoo, Shiraz University, Iran

Payman Arabshahi, University of Washington, USA

Peng-Yeng Yin, National Chi Nan University, Taiwan

Ping Guo, Beijing Normal University, China

Prithviraj Dasgupta, University of Nebraska, USA

Qi Wang, Xi’an Institute of Optics and Precision Mechanics Of CAS, China

Qieshi Zhang, Waseda University, Japan

Qingfu Zhang, University of Essex, UK

Radu-Emil Precup, Politehnica University of Timisoara, Romania

Ran He, National Laboratory of Pattern Recognition, China

Sabri Arik, Istanbul University, Turkey

Shan He, University of Birmingham, UK

Shunren Xia, Zhejiang University, China

Thanatchai Kulworawanichpong, Suranaree University of Technology, Thailand

Thomas Potok, Oak Ridge National Laboratory, USA

Wai-Keung Fung, University of Manitoba, Canada

Walter Chen, National Taipei University of Technology, Taiwan

Wenlian Lu, Fudan University, China

Xia Li, Shenzhen University, China

Xingquan Zuo, Beijing University of Posts and Telecommunications, China

Xuelong Li, University of London, UK

Ying Tan, Peking University, China

Yingjie Yang, De Montfort University, UK

Yongsheng Ding, Donghua University, China

Yuancheng Huang, wuhan university, China

Yuhui Shi, Xi’an Jiaotong-Liverpool University, China

Zexuan Zhu, Shenzhen University, China

Zhen Ji, Shenzhen University, China

Zhi-Hua Zhou, Nanjing University, China

Zhongzhi Shi, Institute of Computing Technology, Chinese Academy of Sciences, China

Zhuhong You, Shenzhen University, China

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Program Schedule Overview

Registration

Registration will take place at Registration Desk in the Hotel Lobby during the following

hours:

Date Time

June 12, 2013 10:00-18:00

June 13, 2013 08:00-12:00

June 14, 2013 08:00-12:00

Notable Events

Date Time Events (At New Gloria Garden Plaza Hotel)

June 13, 2013 08:30-08:55 Opening Ceremony

(Function Room at Third Floor)

09:00-12:00 Plenary Talks

(Function Room at Third Floor)

12:00-13:30 Lunch

(Chinese Restaurant at the Fifth Floor)

13:30-17:10 Parallel Oral Sessions

(Meeting Rooms Daming, Weishan and Dongting at Third Floor)

18:30-21:00 Banquet

(Banquet Hall, Lao Tan Zi Fish Restaurant (No. 178, Tongjiang

Street), about 300 meters away from the Hotel)

June 14, 2013 08:00-12:00 Parallel Oral Sessions

(Meeting Rooms Daming and Weishan at Third Floor)

12:00-13:30 Lunch

(Chinese Restaurant at the Fifth Floor)

13:30-17:30 Parallel Oral Sessions

(Meeting Rooms Daming and Weishan at Third Floor)

June 15, 2013 08:00-16:00 Workshop

Post-conference Excursions: Harbin City Tour (Free Activity)

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Plenary Talk I

Multi-objective Evolutionary Computation based Decomposition and Regularity

Prof. Qingfu Zhang

University of Essex, UK

Abstract

Many optimization problems in the real world, by nature, have multiple conflicting objectives.

Unlike a single optimization problem, multi-objective optimization problem has a set of Pareto

optimal solutions (Pareto front) which could be required by a decision maker to make her

final decision. Evolutionary algorithms are able to generate an approximation to the Pareto

front in a single run, and many traditional optimization methods have been also developed

for dealing with multiple objectives. Although there is not much work that has been done,

combination of evolutionary algorithms and traditional optimization methods should be a

next generation multi-objective optimization solver. Decomposition techniques have been well

used and studied in traditional multiobjective optimization. In this talk, I will describe two

multi-objective evolutionary algorithms: MOEA/D and RM-MEDA. Both of them borrow

ideas from traditional optimization. MOEA/D decomposes a multiobjective problem into a

number of single objective subproblems or simple multi-objective subproblems, and then solves

these subproblems in a collaborative manner. RM-MEDA makes use of the regularity property

to model the distribution of Pareto optimal solutions in the search space, and then generates

new solutions from the model thus built. I will also outline some possible research issues in

multi-objective evolutionary computation.

Biography

Qingfu Zhang is currently a Professor with the School of Computer

Science and Electronic Engineering, University of Essex, UK. His is

also a Changjing Visiting Chair Professor in Xidian University, China.

From 1994 to 2000, he was with the National Laboratory of Parallel

Processing and Computing, National University of Defence Science and

Technology, China, Hong Kong Polytechnic University, Hong Kong,

the German National Research Centre for Information Technology (now

Fraunhofer-Gesellschaft, Germany), and the University of Manchester

Institute of Science and Technology, Manchester, U.K. He holds two patents and is the author

of many research publications. His main research interests include evolutionary computation,

optimization, neural networks, data analysis, and their applications. Dr. Zhang is an Associate

Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on

Systems, Man, and CyberneticsCPart B. He is also an Editorial Board Member of three other

international journals. MOEA/D, a multi-objective optimization algorithm developed in his

group, won the Unconstrained Multiobjective Optimization Algorithm Competition at the

Congress of Evolutionary Computation 2009, and was awarded the 2010 IEEE Transactions

on Evolutionary Computation Outstanding Paper Award.

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Plenary Talk II

The Unified Framework (or Unification) of Nature-inspired Optimization

Algorithms and Standardized Evaluation

Prof. Qidi Wu and Lei Wang

Tongji University, China

Abstract

Nature-inspired computation is a kind of heuristic optimization technique, which includes

diversiform algorithms, such as Genetic algorithm, Evolutionary programming, Estimation

of Distribution Algorithm, Differential Evolution, Particle Swarm Optimization, Ant Colony

Optimization, Artificial Bee ColonyGroup Search OptimizerBacterial Foraging Optimization,

DNA Computing, Self-Organization Map, Membrane Computing, Artificial Immune Systems,

Memetic Algorithm, and Social Cognitive Optimization, etc. Different computation models

usually take on relative uniform characteristic though they usually have distinct extrinsic forms.

These intelligent algorithms are coupling with deterministic and stochastic, the contradiction

between necessity and accidental unity, which promotes the evolution of the inheritance and the

creative process: stochastic is adopted to give creative ability to the implemented intelligent

system, and a succession of certainty is acted to ensure the system is converging. In this

report, a unified framework of nature-inspired optimization algorithms is proposed based on

the unifying idea. Furthermore, in order to solve the difficult of selecting algorithms for different

problems, a standardized evaluation system for nature-inspired optimization algorithms is

proposed, as well as the theory of evaluation space is in the work, by using statistic indicators

for a set number of benchmarks.

Biography

Prof. Qidi Wu received the B.S. degree in Radio Technology, and

M.S. degree in Automatic Control from Tsinghua University, Beijing,

China, in 1970 and 1981, respectively. She received the Ph.D degree in

Automation from ETH, Zurich, Switzerland, in 1986. She is currently

a professor with the Department of Control Science and Engineering,

School of Electronics and Information Engineering, Tongji University.

Her research interests are in control theory and engineering, intelligent

automation, computational intelligence, complex systems scheduling and optimization, system

engineering and management engineering. She has over 300 publications including journal and

conference proceedings papers, books and patents in the above research areas. She has been

awarded Second Prize, National Prize for Progress in Science and Technology in 2004 and 2006.

Prof. Lei Wang received the B.S. degree from Jiangsu University, Zhenjiang, Jiangsu,

China, in 1992. And received the M.S. and Ph.D degree from Tongji University, Shanghai,

China, in 1995 and 1998, respectively. He is currently a professor with the Department of

Control Science and Engineering, School of Electronics and Information Engineering, Tongji

University. His research interest covers intelligent automation, computational intelligence

and system engineering. He has about 100 publications including journal and conference

proceedings papers, books and patents in this areas.

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Technical Program

June 13, 2013(Thursday)

08:30-08:55 Opening Ceremony Function Room

09:00-10:20

Plenary Talk I

Speaker: Prof. Qingfu Zhang

Chair: Prof. Yuhui Shi

Function Room

10:20-10:40 Tea/Coffee BreakMain Entrance of

Function Room

10:40-12:00

Plenary Talk II

Speaker: Prof. Qidi Wu and Lei Wang

Chair: Prof. Yuhui Shi

Function Room

12:00-13:30 LunchChinese Restaurant at the

Fifth Floor

13:30-15:10 Oral Sessions Meeting Rooms

Daming Meeting Room Weishan Meeting

Room

Dongting Meeting

Room

Particle Swarm

Optimization (I)

Particle Swarm

Optimization (II)

Applications of PSO

Algorithms (I)

15:10-15:30 Tea/Coffea Break Corridor


Daming Meeting Room Weishan Meeting

Room

Dongting Meeting

Room

Applications of PSO

Algorithms (II)

Neural Networks and

Fuzzy Logic

Evolutionary Programming

and Differential Evolution

18:30-21:00 BanquetLao Tan Zi Fish

Restaurant

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June 14, 2013(Friday)


Daming Meeting Room Weishan Meeting Room

Swarm-robot and Multi-agent Systems Image and Video Processings




Other Swarm-based Search Methods Colony-Based Optimization Algorithms

12:00-13:30 LunchChinese Restaurant at the

Fifth Floor



Hybrid Algorithms Intelligent Control




Data Mining Methods System and Information Security

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Oral Sessions

June 13, 2013(Thursday)

Session Particle Swarm Optimization (I) Chair Yuhui Shi

Co-Chair Ben Niu

Date/Time June 13, 2013(Thursday) 13:30-15:10 Venue Daming Meeting Room

13:30 - 13:50 Maturity of the Particle Swarm as a Metric for Measuring the

Collective Intelligence of the Swarm

P29

Zdenka Winklerova

13:50 - 14:10 Particle Swarm Optimization in Regression Analysis: A Case Study P29

Shi Cheng, Chun Zhao, Jingjin Wu and Yuhui Shi

14:10 - 14:30 Multi-swarm Particle Swarm Optimization with a Center Learning

Strategy

P29

Ben Niu, Huali Huang, Lijing Tan and Jane Jing Liang

14:30 - 14:50 GSO an Improved PSO based on Geese Flight Theory P29

Shengkui Dai, Peixian Zhuang and Wenjie Xiang

14:50 - 15:10 A Test of Position Determination with PSO P30

Walter W. Chen, Jianan Wang and Zheping Shen

Session Particle Swarm Optimization (II) Chair Andreas Janecek

Co-Chair Giovanni Fasano

Date/Time June 13, 2013(Thursday) 13:30-15:10 Venue Weishan Meeting Room

13:30 - 13:50 Initial particles position for PSO, in Bound Constrained

Optimization

P30

Emilio F. Campana, Matteo Diez, Giovanni Fasano and Daniele

Peri

13:50 - 14:10 Local and Global Search Based PSO Algorithm P30

Yanxia Sun, Zenghui Wang and Barend Jacobus van Wyk

14:10 - 14:30 Cask Theory Based Parameter Optimization for Particle Swarm

Optimization

P30

Zenghui Wang and Yanxia Sun

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14:30 - 14:50 Application of an Improved Particle Swarm Optimization Algorithm

in Hydrodynamic Design

P30

Huang Sheng, Ren Wanlong, Wang Chao and Guo Chunyu

14:50 - 15:10 Agent-Based Social Simulation and PSO P31

Andreas Janecek, Tobias Jordan and Fernando Buarque de Lima

Neto

Session Applications of PSO Algorithms (I) Chair Xingquan Zuo

Date/Time June 13, 2013(Thursday) 13:30-15:10 Venue Dongting Meeting Room

13:30 - 13:50 A Piecewise Linearization Method of Significant Wave Height Based

on Particle Swarm Optimization

P31

Liqiang Liu, Zhichao Fan and Xiangguo Wang

13:50 - 14:10 Optimization Analysis of Controlling Arrivals in the Queueing

System with Single Working Vacation Using Particle Swarm

Optimization

P31

Cheng-Dar Liou

14:10 - 14:30 Anomaly Detection in Hyperspectral Imagery Based on PSO

Clustering

P32

Baozhi Cheng and Zongguang Guo

14:30 - 14:50 Transcribing Bach Chorales using Particle Swarm Optimisations P32

Somnuk Phon Amnuaisuk

14:50 - 15:10 Deadline Constrained Task Scheduling Based on Standard-PSO in a

Hybrid Cloud

P32

Guoxiang Zhang and Xingquan Zuo

Session Applications of PSO Algorithms (II) Chair Jian-Long Kuo

Date/Time June 13, 2013(Thursday) 15:30-17:10 Venue Daming Meeting Room

15:30 - 15:50 An Enhanced Node Repeatable Virtual Network Embedding

Algorithm Based PSO Solution

P32

Cong Wang, Ying Yuan, Ying Yang and Xi Hu

21

15:50 - 16:10 The Application of Particle Swarm Optimization Arithmetic in

Propeller Design

P33

Wang Chao, Wang Guoliang, Ren Wanlong, Guo Chunyu and Zhou

Bin

16:10 - 16:30 Modeling of Manufacturing N-phase Multiphase Motor Using

Orthogonal Particle Swarm Optimization

P33

Jian-Long Kuo

16:30 - 16:50 Discrete Particle Swarm Optimization Algorithm for Virtual

Network Reconfiguration

P33

Ying Yuan, Cuirong Wang, Cong Wang, Shiming Zhu and Siwei

Zhao

16:50 - 17:10 Power Distribution Network Planning Application Based on

Multi-Objective Binary Particle Swarm Optimization Algorithm

P33

Jose Roberto Bezerra, Giovanni Cordeiro Barroso, Ruth Pastora

Saraiva Leao, Raimundo Furtado and Eudes Barbosa de Medeiros

Session Neural Networks and Fuzzy Logic Chair Zeineb Chelly

Co-Chair Nasseer K. Bachache

Date/Time June 13, 2013(Thursday) 15:30-17:10 Venue Weishan Meeting Room

15:30 - 15:50 Evolved neural network based intelligent trading system for stock

market

P34

Lifeng Zhang and Yifan Sun

15:50 - 16:10 Network-based Neural Adaptive Sliding Mode Controller for the Ship

Steering Problem

P34

Guoqing Xia and Huiyong Wu

16:10 - 16:30 A New Hybrid Fuzzy-Rough Dendritic Cell Immune Classifier P34

Zeineb Chelly and Zied Elouedi

16:30 - 16:50 Design Fuzzy Logic Controller by Particle Swarm Optimization for

Wind Turbine

P34

Nasseer K. Bachache and Jinyu Wen

16:50 - 17:10 Multi Objective Swarm Optimization Design Fuzzy Controller to

Adjust Speed of AC Motor Drive

P34

Nasseer K. Bachache and Jinyu Wen

22


Session Evolutionary Programming and

Differential Evolution

Chair Xiuquan Deng

Date/Time June 13, 2013(Thursday) 15:30-17:10 Venue Dongting Meeting Room

15:30 - 15:50 A Circuit Generating Mechanism with Evolutionary Programming

for Improving the Diversity of Circuit Topology in Population-based

Analog Circuit Design

P35

Mei Xue and Jingsong He

15:50 - 16:10 An Evolutionary Game Model of Organizational Routines on

Complex Networks

P35

Dehua Gao, Xiuquan Deng and Bing Bai

16:10 - 16:30 A Novel Negative-Correlation Redundancy Evolutionary Framework

Based on Stochastic Ranking for Fault-Tolerant Design of Analog

Circuit

P35

Chao Lin and Jingsong He

16:30 - 16:50 Differential Evolution with Group Crossover for Automatic Synthesis

of Analog Circuit

P35

Ting Wu and Jingsong He

16:50 - 17:10 MMODE: a Memetic Multiobjective Differential Evolution

Algorithm

P36

Zhou Wu, Xiaohua Xia and Jiangfeng Zhang

23

June 14, 2013(Friday)

Session Swarm-robot and Multi-agent

Systems

Chair Abdelhak Chatty

Date/Time June 14, 2013(Friday) 08:00-10:00 Venue Daming Meeting Room

08:00 - 08:20 Learning by Imitation for the Improvement of the Individual and the

Social Behaviors of Self-Organized Autonomous Agents

P36

Abdelhak Chatty, Philippe Gaussier, Ilhem Kallel, Philippe Laroque

and Adel M. Alimi

08:20 - 08:40 An Indexed K-D Tree for Neighborhood Generation in Swarm

Robotics Simulation

P36

Zhongyang Zheng and Ying Tan

08:40 - 09:00 Interactive Robotic Fish for the Analysis of Swarm Behavior P36

Tim Landgraf, Hai Nguyen, Stefan Forgo, Jan Schneider, Joseph

Schroer, Christoph Kruger, Henrik Matzke, Romain O. Clement,

Jens Krause and Raul Rojas

09:00 - 09:20 An Study of Indoor Localization Algorithm Based on Imperfect

Signal Coverage in Wireless Networks

P36

Ping Li, Limin Sun, Qing Fang, Jinyang Xie, Wu Yang and Kui Ma

09:20 - 09:40 The Latest Application and Development of Humanoid Robot on

Swarm Intelligence

P37

Leo Chen Guanyang

09:40 - 10:00 Mechanical PSO Aided by Extremum Seeking for Swarm Robots

Cooperative Search

P37

Qirong Tang and Peter Eberhard

Session Image and Video Processings Chair Hongwei Mo

Date/Time June 14, 2013(Friday) 08:00-10:00 Venue Weishan Meeting Room

08:00 - 08:20 Remote Sensing Image Segmentation Based on Rough Entropy P37

Huijie Sun, Tingquan Deng and Yingying Jiao

08:20 - 08:40 A Real-time Noise Image Edge Detector Based on FPGA P37

Meihua Xu, Chenjun Xia and Shuping Huang

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08:40 - 09:00 Video Image Clarity Algorithm Research of USV Visual System

under the Sea Fog

P38

Zhongli Ma, Jie Wen and Xiumei Liang

09:00 - 09:20 A Study of Vision-based Lane Recognition Algorithm for Driver

Assistance

P38

Feng Ran, Zhoulong Jiang, Tao Wang and Meihua Xu

09:20 - 09:40 Comparison and Evaluation of Human Locomotion Traits with

Different Prosthetic Feet Using Graphical Methods from Control

Area

P38

Lulu Gong, Qirong Tang and Hongwei Mo

09:40 - 10:00 Reversible Data Embedment for Encrypted Cartoon Images Using

Unbalanced Bit Flipping

P38

Wien Hong, Tung-Shou Chen, Jeanne Chen, Yu-Hsin Kao, Han-Yan

Wu and Mei-Chen Wu

Session Other Swarm-based Search Methods Chair Jianhua Liu


10:20 - 10:45 Optimal Power Flow Solution Using Self-Evolving Brain-Storming

Inclusive Teaching-Learning-Based Algorithm

P38

Krishnanand K.R., S.M.F. Hasani, B.K. Panigrahi and S.K. Panda

10:45 - 11:10 A Study on an Evaluation Model for Robust Nurse Rostering based

on Heuristics

P39

Ziran Zheng and Xiaoju Gong

11:10 - 11:35 The Improvement on Controlling Exploration and Exploitation of

Firework Algorithm

P39

Jianhua Liu, Shaoqiu Zheng and Ying Tan

11:35 - 12:00 An Artificial Chemistry System for Simulating Cell Chemistry: The

First Step

P39

Chien-Le Goh, Hong Tat Ewe and Yong Kheng Goh

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Session Colony-Based Optimization

Algorithms

Chair Hongwei Mo


10:20 - 10:50 Constrained Multi-objective Biogeography Optimization Algorithm

for Robot Path Planning

P39

Hongwei Mo, Zhidan Xu and Qirong Tang

10:50 - 11:20 An Ant Colony System Based on the Network P40

Tao Qian, Zili Zhang, Chao Gao, Yuheng Wu and Yuxin Liu

11:20 - 11:50 Algorithms and Framework for Comparison of Bee-Intelligence

Based Peer-to-Peer Lookup

P40

Vesna Sesum Cavic and Eva Kuhn

Session Hybrid Algorithms Chair Shangce Gao


13:30 - 13:55 Hybrid Gravitational Search and Clonal Selection Algorithm for

Global Optimization

P40

Shangce Gao, Hongjian Chai, Beibei Chen and Gang Yang

13:55 - 14:20 A Physarum Network Evolution Model Based on IBTM P40

Yuxin Liu, Zili Zhang, Chao Gao, Yuheng Wu and Tao Qian

14:20 - 14:45 Global Optimization Inspired by Quantum Physics P41

Xiaofei Huang

14:45 - 15:10 A Study of Human Flesh Search Based on SIR Flooding on

Scale-Free Networks

P41

Dawei Meng, Lei Zhang and Long Cheng

Session Intelligent Control Chair Tze-Yee Ho


13:30 - 13:50 Brownian Snake Measure-valued Markov Decision Process P41

Zhenzhen Wang and Hancheng Xing

13:50 - 14:10 Genetic Evolution of Control Systems P41

Mu-Song Chen, Tze-Yee Ho and Chipan Hwang

26


14:10 - 14:30 A New Target Tracking Algorithm Based on Online Adaboost P41

Zhuowen Lv, Kejun Wang and Tao Yan

14:30 - 14:50 Solving Hamilton Path Problem with P System P42

Laisheng Xiang and Jie Xue

14:50 - 15:10 False Data Attacks Judgment Based on Consistency Loop Model in

Wireless Sensor Networks

P42

Ping Li, Limin Sun, Wu Yang, Qing Fang, Jinyang Xie and Kui Ma

15:10 - 15:30 The Design and Implementation of Motor Drive for an Electric

Bicycle

P42

Tze-Yee Ho, Mu-Sung Chen, Wei-Chieh Chen and Chih-Hao Chiang

Session Data Mining Methods Chair Xinchao Zhao


15:50 - 16:10 An Online Trend Analysis Method for Measuring Data Based on

Historical Data Clustering

P42

Jianfeng Qu, Maoyun Guo, Yi Chai, Zhimin Yang, Tao Zou, Tian

Lan and Zhenglei Liu

16:10 - 16:30 Discover Community Leader in Social Network with PageRank P42

Rui Wang, Weilai Zhang, Han Deng, Nanli Wang, Qing Miao and

Xinchao Zhao

16:30 - 16:50 A New Efficient Text Clustering Ensemble Algorithm Based on

Semantic Sequences

P43

Zhonghui Feng, Junpeng Bao and Kaikai Liu

16:50 - 17:10 A Novel Algorithm for Kernel Optimization of Support Vector

Machine

P43

Lijie Li

17:10 - 17:30 Training Least-square SVM by a Recurrent Neural Network Based

on Fuzzy C-mean Approach

P43

Fengqiu Liu, Jianmin Wang and Sitian Qin

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Session System and Information Security Chair Mu-Song Chen


15:50 - 16:10 Detection of Human Abnormal Behavior of the Ships Security P43

Fengxu Guan, Xiaolong Liu and Xiangyu Meng

16:10 - 16:30 Experimentation of Data Mining Technique for Systems Security A

Comparative Study

P43

Ahmed Chaouki Lokbani, Ahmed Lehireche and Reda Mohamed

Hamou

16:30 - 16:50 The Extension of Linear Coding Method for Automated Analog

Circuit Design

P44

Zhi Li and Jingsong He

16:50 - 17:10 The UML Diagram to VHDL Code Transformation Based on MDA

Methodology

P44

Chi-Pan Hwang and Mu-Song Chen

17:10 - 17:30 OFDM System with Reduce Peak-to-Average Power Ratio Using

Optimum Combination of Partial Transmit Sequences

P44

Yung-Cheng Yao, Ho-Lung Hung and Jyh-Horng Wen

17:30 - 17:50 Terrain Image Classification with SVM P44

Mu-Song Chen, Chi-Pan Hwang and Tze-Yee Ho

28


Abstracts

Particle Swarm Optimization (I)June 13, 2013(Thursday) 13:30-15:10 Daming Meeting Room

Maturity of the Particle Swarm as a Metric for Measuring theCollective Intelligence of the Swarm

Zdenka WinklerovaAbstract. The particle swarm collective intelligence has been recognized as a tool for dealing withthe optimization of multimodal functions with many local optima. In this article, a research workis introduced in which the cooperative Particle Swarm Optimization strategies are analysed and thecollective intelligence of the particle swarm is assessed according to the proposed Maturity Model.The model is derived from the Maturity Model of C2 (Command and Control) operational space andthe model of Collaborating Software. The aim was to gain a more thorough explanation of how theintelligent behaviour of the particle swarm emerges. It has been concluded that the swarm system isnot mature enough because of the lack of the system’s awareness, and that a solution would be someadaptation of particle’s behavioural rules so that the particle could adjust its velocity using controlparameters whose value would be derived from inside of the swarm system, without tuning.

Particle Swarm Optimization in Regression Analysis: A CaseStudy

Shi Cheng, Chun Zhao, Jingjin Wu and Yuhui ShiAbstract. In this paper, we utilized particle swarm optimization algorithm to solve a regressionanalysis problem in dielectric relaxation field. The regression function is a nonlinear, constrained,and difficult problem which is solved by traditionally mathematical regression method. The regressionprocess is formulated as a continuous, constrained, single objective problem, and each dimension isdependent in solution space. The object of optimization is to obtain the minimum sum of absolutedifference values between observed data points and calculated data points by the regression function.Experimental results show that particle swarm optimization can obtain good performance on regressionanalysis problems.

Multi-swarm Particle Swarm Optimization with a CenterLearning Strategy

Ben Niu, Huali Huang, Lijing Tan and Jane Jing LiangAbstract. This paper proposes a new variant of particle swarm optimizers, called multi-swarmparticle swarm optimization with a center learning strategy (MPSOCL). MPSOCL uses a centerlearning probability to select the center position or the prior best position found so far as the exemplarwithin each swarm. In MPSOCL, Each particle updates its velocity according to the experienceof the best performing particle of its partner swarm and its own swarm or the center position ofits own swarm. Experiments are conducted on five test functions to compare with some variantsof the PSO. Comparative results on five benchmark functions demonstrate that MPSOCL achievesbetter performances in both the optimum achieved and convergence performance than other algorithmsgenerally.

GSO an Improved PSO based on Geese Flight TheoryShengkui Dai, Peixian Zhuang and Wenjie Xiang

Abstract. Formation flight of swan geese is one type of swarm intelligence developed throughevolution by natural selection. The research on its intrinsic mechanism has great impact on thebionics field. Based on previous research achievements, extensive observation and analysis on suchphenomenon, five geese-flight rules and hypotheses are proposed in order to form a concise and simplegeese-flight theory framework in this paper. Goose Swarm Optimization algorithm is derived based onthe Standard Particle Swam Optimization algorithm. Experimental results show that GSO algorithmis superior in several aspects, such as convergence speed, convergence precision, robustness and etc.The theory offers the in-depth explanations for the performance superiority. Moreover, the rules andhypotheses for formation flight adhere to all five basic principles of swarm intelligence. Therefore, theproposed geese-flight theory is highly rational and has important theoretical innovations, and GSOalgorithm can be utilized in a wide range of applications.

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A Test of Position Determination with PSOWalter W. Chen, Jianan Wang and Zheping Shen

Abstract. PSO has been used in combination with ultra-high resolution 360-degree panoramic imagesin positioning field objects at a landslide site. Although the computational efficiency was exceptional,the sum of errors was high. In order to demonstrate that the errors came from GPS readings insteadof photography mistakes or erroneous computer codes, the authors designed and implemented anexperiment and used it to verify the applicability of the PSO method. A conceptual layout was firstsketched on paper and then tested on a rooftop of a campus building. Two sets of input data wereconstructed using the panoramic photos and the CAD drawing of the conceptual layout, respectively.Both data sets were computed using the brute force program and the PSO program developed inprevious studies. The results showed that cm-level and sub-mm level accuracy was achieved in theexperiment. Consequently, it was concluded that the PSO program was correct and the PSO methodwas applicable to the positioning problem. The accuracy of positioning in the field can be improvedwith the aid of better GPS devices.

Particle Swarm Optimization (II)June 13, 2013(Thursday) 13:30-15:10 Weishan Meeting Room

Initial particles position for PSO, in Bound ConstrainedOptimization

Emilio F. Campana, Matteo Diez, Giovanni Fasano and Daniele PeriAbstract. We consider the solution of bound constrained optimization problems, where we assumethat the evaluation of the objective function is costly, its derivatives are unavailable and the use ofexact derivative-free algorithms may imply a too large computational burden. There is plenty ofreal applications, e.g. several design optimization problems , belonging to the latter class, wherethe objective function must be treated as a “black-box” and automatic differentiation turns to beunsuitable. Since the objective function is often obtained as the result of a simulation, it might beaffected also by noise, so that the use of finite differences may be definitely harmful.

In this paper we consider the use of the evolutionary Particle Swarm Optimization (PSO) algorithm,where the choice of the parameters is inspired by , in order to avoid diverging trajectories of theparticles, and help the exploration of the feasible set. Moreover, we extend the ideas in and proposea specific set of initial particles position for the bound constrained problem.

Local and Global Search Based PSO AlgorithmYanxia Sun, Zenghui Wang and Barend Jacobus van Wyk

Abstract. In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In thisalgorithm, the particles are divided into two groups. The two groups have different focuses when allthe particles are searching the problem space. The first group of particles will search the area aroundthe best experience of their neighbours. The particles in the second group are influenced by the bestexperience of their neighbors and the individual best experience, which is the same as the standardPSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposedalgorithm effectively enhances searching efficiency and improves the quality of searching.

Cask Theory Based Parameter Optimization for Particle SwarmOptimization

Zenghui Wang and Yanxia SunAbstract. To avoid the bored try and error method of finding a set of parameters of ParticleSwarm Optimization (PSO) and achieve good optimization performance, it is desired to get anadaptive optimization method to search a good set of parameters. A nested optimization methodis proposed in this paper and it can be used to search the tuned parameters such as inertia weightω, acceleration coefficients c1 and c2, and so on. This method considers the cask theory to achievea better optimization performance. Several famous benchmarks were used to validate the proposedmethod and the simulation results showed the efficiency of the proposed method.

Application of an Improved Particle Swarm OptimizationAlgorithm in Hydrodynamic Design

Huang Sheng, Ren Wanlong, Wang Chao and Guo ChunyuAbstract. In order to design the hydrofoil section with good lift-drag ratio performance, the airfoil

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which received by Improved Particle Swarm Optimization algorithm and Particle Swarm Optimizationalgorithm should be compared to find the best way in accord with the target. Airfoils are representedby analytic functions, and objective function and fitness function are provided by numerical solution ofPanel method. In entire optimization process Improved Particle Swarm Optimization algorithm onlychanged the weight which influences the speed of particles flying, and optimized airfoil that comparedto the original airfoil hydrodynamic performance has improved significantly, and has better resultthan the elementary particle swarm algorithm. Results of Optimization verified the feasibility of theimproved Particle Swarm Optimization algorithm in the optimization of airfoil section design, and inthe future this algorithm has certain significance.

Agent-Based Social Simulation and PSOAndreas Janecek, Tobias Jordan and Fernando Buarque de Lima Neto

Abstract. Consumer’s behavior can be modeled using a utility function that allows for measuringthe of an individual’s decision, which consists of a tuple of goods an individual would like to buy andthe hours of work necessary to pay for this purchase and consumption. The success of such a decisionis measured by a utility function which incorporates not only the purchase and consumption of goods,but also leisure, which additionally increases the utility of an individual. In this paper, we present anew agent based social simulation in which the decision finding process of consumers is performed byParticle Swarm Optimization (PSO), a well-known swarm intelligence method.

PSO appears to be suitable for the underlying problem as it is based on previous current information,but also contains a stochastic part which allows for modeling the uncertainty usually involved in thehuman decision making process. We investigate the adequacy of different bounding strategies thatmap particles violating the underlying budget constraints to a feasible region. Experiments indicatethat one of these bounding strategies is able to achieve very fast and stable convergence for thegiven optimization problem. However, an even more interesting question refers to adequacy of thesebounding strategies for the underlying social simulation task.

Applications of PSO Algorithms (I)June 13, 2013(Thursday) 13:30-15:10 Dongting Meeting Room

A Piecewise Linearization Method of Significant Wave HeightBased on Particle Swarm Optimization

Liqiang Liu, Zhichao Fan and Xiangguo WangAbstract. A piecewise linearization method of significant wave height is proposed based on particleswarm optimization. Piecewise linearization model is used to approximate significant wave heightinversion model, minimum radius of neighborhood is used to eliminate wild value in the sample dataand sparse the data, and then the particle swarm optimization algorithm is applied for piecewisearea division and parameter optimization of the model. Simulation result shows that compared withtraditional inversion method, better practicability and the higher significant wave height inversionprecision are obtained by the proposed method.

Optimization Analysis of Controlling Arrivals in the QueueingSystem with Single Working Vacation Using Particle Swarm

OptimizationCheng-Dar Liou

Abstract. A cost function in the literature of queueing system with single working vacation wasformulated as an optimization problem to find the minimum cost. In the approach used, a directsearch method is first used to determine the optimal system capacity K and the optimal threshold Ffollowed by the Quasi-Newton method to search for the optimal service rates at the minimum cost.However, this two stage search method restricts the search space and cannot thoroughly explore theglobal solution space to obtain the optimal solutions. In overcoming these limitations, this studyemploys a particle swarm optimization algorithm to ensure a thorough search of the solution space inthe pursuit of optimal minimum solutions. Numerical results compared with those of the two stagesearch method and genetic algorithms support the superior search characteristics of the proposedsolution.

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Anomaly Detection in Hyperspectral Imagery Based on PSOClustering

Baozhi Cheng and Zongguang GuoAbstract. In this paper, we propose a novel anomaly targets detection algorithm baesd on informationprocessing method and KRX anomaly detector. It use fully nolinear feature and decrease bandsredundancy for hyperspectral imagery. Firstly, the original hyperspectral imagery is clustered by anew clustering method, i.e. clustering of particle swarm optimization. Then, we extract a largestfourth-order cumulant value in every class, and constitute a optimal band subset. Finally, theKRX detector is used on the band subset to get anomaly detection results. The simulation resultsdemonstrate that the proposed PSOC-KRX algorithm outperforms the other algorithm, it is higherprecision and lower false alarm rate.

Transcribing Bach Chorales using Particle Swarm OptimisationsSomnuk Phon Amnuaisuk

Abstract. This paper reports a novel application of particle swarm optimisation to polyphonictranscription task. The system transforms an input audio into activation strength of pitches in thedesired range. This transformation begins with audio information in time-domain to frequency-domainand finally, to activation strength of pitches (a.k.a. piano-roll representation). We can infer thelikely sounding pitches by comparing the observed activation strength of input audio to referenceTone-models. Although each Tone-model is learned offline from the pitches one wish to performtranscription with, this process often only approximates the Tone-model characteristics due to thevariations in volume and other effects introduced from the manner of note executions. Hence, predictingsounding notes based solely on Tone-models gives inaccurate predictions. Here, we apply PSO to searchfor an optimum aggregation of different predicted pitches that best represents the input activationstrength. We describe our problem formulation and the design of our approach. The experimentalresults show our approach to be of potential in the task of polyphonic transcription.

Deadline Constrained Task Scheduling Based on Standard-PSOin a Hybrid Cloud

Guoxiang Zhang and Xingquan ZuoAbstract. Public cloud providers provide Infrastructure as a Service (IaaS) to remote users. For IaaSproviders, how to schedule tasks to meet peak demand is a big challenge. Previous researches proposedpurchasing machines in advance or building cloud federation to resolve this problem. However, theformer is not economic and the latter is hard to be put into practice at present. In this paper, wepropose a hybrid cloud architecture, in which an IaaS provider can outsource its tasks to ExternalClouds (ECs) without establishing any agreement or standard when its local resources are not sufficient.The key issue is how to allocate users’s tasks to maximize its profit while guarantee QoS. The problem isformulated as a Deadline Constrained Task Scheduling (DCTS) problem which is resolved by standardparticle swarm optimization (PSO), and compared with an exact approach (CPLEX). Experimentresults show that Standard-PSO is very effective for this problem.

Applications of PSO Algorithms (II)June 13, 2013(Thursday) 15:30-17:10 Daming Meeting Room

An Enhanced Node Repeatable Virtual Network EmbeddingAlgorithm Based PSO Solution

Cong Wang, Ying Yuan, Ying Yang and Xi HuAbstract. The major challenge in network virtualization is the efficient mapping of virtual nodes andlinks of virtual networks onto substrate network. In this paper we propose ENR-VNE, an algorithmwhich can achieves high VN request acceptance ratio in the same time. We modeled VNE problemas an optimal problem to minimize the substrate resource utilization degree. Leverage the advantageof ram data switch between virtual machines host on same physical machine instead of using physicallink bandwidth, our algorithm allow repeatable node mapping for same VN. Because the initial valueof PSO algorithm is crucial, we present an initial position assign method to accelerate convergence andachieve more repeatable features. Simulation results show that our algorithm achieve high acceptanceratio on same substrate network than un-repeatable approach and initial position assign method canfurther improve the algorithm performance.

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The Application of Particle Swarm Optimization Arithmetic inPropeller Design

Wang Chao, Wang Guoliang, Ren Wanlong, Guo Chunyu and Zhou BinAbstract. In order to obtain a propeller with good efficiency and cavitation performance, the propellersections were optimization designed using particle swarm optimization (PSO) method. An interactivecalculation method was used in design process for the circulation distribution of designed propeller wasnot coincident with optimization circulation. The difference of circulation was defined as correctionfactor to adjust lift coefficients of sections. PSO method was used to optimize sections to improve thelift-to-drag ratio and pressure distribution. The convergence condition was the circulation distributionfulfilled optimum circulation distribution form. A MAU propeller was optimized using the method.Hydrodynamic performances of propeller and sections’ pressure distribution of original propeller werecompared with optimized propeller. It indicates from the results that compared with traditionalmethod, the PSO method is simpler in theory and cost less computing time. The open water efficiencyof optimized propeller advanced obviously. The min negative pressure is smaller which means thecavitation performance is better.

Modeling of Manufacturing N-phase Multiphase Motor UsingOrthogonal Particle Swarm Optimization

Jian-Long KuoAbstract. This paper intends to propose an energy functional based modeling technique on an n-phasemultiphase motor. In motor control area, the multiphase motor is becoming more and more popularrecently. The multiphase can be applied in direct-drive electric vehicle. However, the associatedmathematical model for energy functional is seldom discussed. This paper will discuss the modeling ofthe motor system by energy functional optimization. Orthogonal particle swarm optimization (OPSO)is used to derive the optimal solution set for the dynamic system. The Simulation and experimentalresults shows the validity of the proposed model. It is believed that the developed system model canbe used in the energy functional of the multiphase motor.

Discrete Particle Swarm Optimization Algorithm for VirtualNetwork Reconfiguration

Ying Yuan, Cuirong Wang, Cong Wang, Shiming Zhu and Siwei ZhaoAbstract. Network virtualization allows multiple virtual networks (VNs) to coexist on a sharedphysical substrate infrastructure. Efficient network resource utilization is crucial for such problem.Most of the current researches focus on algorithms to allocate resources to VNs in mapping. However,reconfiguration problem of running VNs is relatively less explored. Aiming at dynamic scheduling ofrunning VNs, this paper introduces a virtual network reconfiguration model to achieve more substratenetwork resource utilization. We formulate the virtual network reconfiguration problem as a multiobject optimal problem and use discrete particle swarm optimization (DPSO) algorithm to searchoptimal solution. Experimental results show that by rescheduling the running VNs on substratenetwork according to the optimal reconfiguration solution our approach can observably reduce thebiggest load in both physical node and link load, balance average load and avoid bottlenecks insubstrate network so as to gain high VNs accept ratio.

Power Distribution Network Planning Application Based onMulti-Objective Binary Particle Swarm Optimization Algorithm

Jose Roberto Bezerra, Giovanni Cordeiro Barroso, Ruth Pastora Saraiva Leao, RaimundoFurtado and Eudes Barbosa de Medeiros

Abstract. Power distribution networks are the most susceptible sector of the whole electric grid interms of reliability. Failures along the lines cause the disconnection of a great number of customerswhat have an immediate impact on quality and security indices. Innovations capable to mitigateimpacts or improve reliability are ever pursued by the electric utilities. In view of that, the planningof the modern distribution networks must consider the installation of switches along the network as animportant procedure to isolate failures reducing the impact and the number of customers not supplied.However, the complexity and the dimension of the current distribution networks, makes the task ofproper allocation of switches strongly dependent on the expertise of engineers. This paper proposesan application based on a Multi-Objective Particle Swarm Optimization algorithm that determinesthe suitable placement and a feasible number of switches on the power distribution networks in orderto minimize the number of customers affected by faults. Detailed information about the algorithm

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and its application in a test distribution system is presented. The effectiveness of the algorithm ispresented in a case study applied to the IEEE 123-Node Test Feeder.

Neural Networks and Fuzzy LogicJune 13, 2013(Thursday) 15:30-17:10 Weishan Meeting Room

Evolved neural network based intelligent trading system for stockmarket

Lifeng Zhang and Yifan SunAbstract. In the present study, evolved neural network is applied to construct a new intelligent stocktrading system. First, heterogeneous double populations based hybrid genetic algorithm is adoptedto optimize the connection weights of feedforward neural networks. Second, a new intelligent stocktrading system is proposed to generates buy and sell signals automatically through predicting a newtechnical indicator called medium term trend. Compared to traditional NN, the new model providesan enhanced generalization capability that both the average return and variance of performance aresignificantly improved.

Network-based Neural Adaptive Sliding Mode Controller for theShip Steering Problem

Guoqing Xia and Huiyong WuAbstract. In this paper, the concept of networked control system (NCS) is introduced into thecourse autopilot of the ship. A network-based neural adaptive sliding mode controller is designed forthe ship steering in waves. The unknown term, including the wave disturbances and the unmodeleddynamics, is approximated by the RBF neural network. The sliding mode controller is designedto compensate the neural network approximation error besides of the network-induced delay. Thestability of the closed-loop system is proven and the neural network weight is updated using theLyapunov theory. It indicates that the designed controller can guarantee the system state tracks thedesired state asymptotically. Finally, a simulation on a Mariner class vessel in waves is carried out todemonstrate the effectiveness of the proposed control scheme.

A New Hybrid Fuzzy-Rough Dendritic Cell Immune ClassifierZeineb Chelly and Zied Elouedi

Abstract. The Dendritic Cell Algorithm (DCA) is an immune-inspired classification algorithmbased on the behavior of natural dendritic cells (DC). This paper proposes a novel version of theDCA based on a two-level hybrid fuzzy-rough model. In the top-level, the proposed algorithm,named RST-MFDCM, applies rough set theory to build a solid data pre-processing phase. In thesecond level, RST-MFDCM applies fuzzy set theory to smooth the crisp separation between the DC’ssemi-mature and mature contexts. The experimental results show that RST-MFDCM succeeds inobtaining significantly improved classification accuracy.

Design Fuzzy Logic Controller by Particle Swarm Optimizationfor Wind Turbine

Nasseer K. Bachache and Jinyu WenAbstract. In this work the Particle Swarm Optimization (PSO) is utilized to framing the optimalparameters of Fuzzy Logic Controller FLC, this parameter is (centers and width) of triangle membershipfunctions, the proposed method can design a robust controller to govern the speed of wind turbine WT,adjusting pitch angle of blade can regulate the output power of WT at a wide range of wind speed,the mean objective of this work is to make the operation of WT works as like as traditional motivatorused in power system. By SIMULINK-MATLAB we implement the complete mathematical model ofthe system. The simulation results demonstrate that the Optimized Fuzzy Logic Control (OFLC) getsa better parameters of fuzzy sets using PSO, and realizes a good dynamic behavior compared withconventional FLC.

Multi Objective Swarm Optimization Design Fuzzy Controller toAdjust Speed of AC Motor Drive

Nasseer K. Bachache and Jinyu WenAbstract. In this paper a Multi-Objective Particle Swarm Optimization (MOPSO) is utilized todesign sets of linguistic Fuzzy Logic Controller (FLC) type Mamdani to govern the speed of InductionMotor (IM). The first objective function is the error between the actual speed and desired speed, and

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the second function is the energy dissipated during (10 Sec). PSO are implemented in M-file/MATLAB,but when the algorithm reaches the step of assessing the “fitness functions”, this program linked withSIMULINK-MATLAB to evaluate these values. This simulation includes the complete mathematicalmodel of IM and the inverter. The simulation results show the proposed controller offers an optimizedspeed behavior as possible with a low-slung of energy along the points of Pareto front.

Evolutionary Programming and Differential EvolutionJune 13, 2013(Thursday) 15:30-17:10 Dongting Meeting Room

A Circuit Generating Mechanism with EvolutionaryProgramming for Improving the Diversity of Circuit Topology in

Population-based Analog Circuit DesignMei Xue and Jingsong He

Abstract. This paper presents an analog circuit generating mechanism based on connecting pointguidance existing in circuit netlist. With the proposed mechanism, the initial circuit topology can bea random netlist, and the evolutionary operation can be executed directly on connecting point. Also,the knowledge of graph theory is introduced for evaluating the degree of diversity of circuit structures.Experimental results show that the proposed mechanism is beneficial to improve the diversity oftopology in population. In the case of no robustness evolution mechanism, the diversity of topologyin population can improve the fault tolerance of population.

An Evolutionary Game Model of Organizational Routines onComplex Networks

Dehua Gao, Xiuquan Deng and Bing BaiAbstract. Organizational routines are collective phenomena with multiple actors involved in. In thispaper, we introduce the evolutionary game theory into the study of organizational routines, and buildup an evolutionary game model of organizational routines on complex networks. On the bases of thismodel, we provide a multi-agent based simulation via Swarm package. The results of our researchshow that: the evolutionary game theory, with the aid of multi-agent simulation as well, can afford usa general framework for formalized quantitative analysis, and provide an absolutely novelty directionsfor the researches of organizational routines based on mathematical methods.

A Novel Negative-Correlation Redundancy EvolutionaryFramework Based on Stochastic Ranking for Fault-Tolerant

Design of Analog CircuitChao Lin and Jingsong He

Abstract. The fault-tolerant evolutionary design based on negative-correlation redundancy techniqueis an effective way to improve the fault-tolerance of analog circuits with uncertain faults. In theexisting negative-correlation redundancy evolutionary framework (ENCF), the negative-correlationpenalty coefficient plays an important role, and it affects the performance of ENCF greatly. However,the value of the negative-correlation penalty coefficient is heavily dependent on the experience ofdesigners. In this paper, we propose a new negative-correlation redundancy evolutionary frameworkbased on stochastic ranking strategy. In order to make comparisons with the existing researches, weemploy analog filter as a design example. Experimental results show that the framework proposed inthis paper can generate negatively correlated redundancies without specifying the penalty coefficient,and it shows a relatively high ability to convergence compared to ENCF.

Differential Evolution with Group Crossover for AutomaticSynthesis of Analog Circuit

Ting Wu and Jingsong HeAbstract. Analog circuit design is significant and challenging. In this paper, we propose a group-crossover-basedvariable-length differential evolution (GVDE) for automatic synthesis of analog circuit. We present twoexperimental results obtained using the proposed GVDE, including a low-pass filter and an invertingamplifier. The results showed that GVDE is able to evolve with variable-length chromosome, whichallows both the topology and sizing of analog circuit to be evolved. The proposed GVDE is an efficientalgorithmic approach for automatic synthesis of analog circuit.

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MMODE: a Memetic Multiobjective Differential EvolutionAlgorithm

Zhou Wu, Xiaohua Xia and Jiangfeng ZhangAbstract. For the multiobjective problems, some global search methods may fail to find the Paretooptima with both accuracy and diversity. To pursue the two goals at the same time, a new memeticmultiobjective differential evolution algorithm (MMODE) is proposed to hybridize the local search withdifferential evolution (DE) algorithm. The local search is conducted in an independent populationto accelerate the search process, while DE can maintain the diversity. In MMODE, we use a newmultiobjective Pareto differential evolution (MOPDE). Experimental results show that the MMODEperforms better than other two MODEs in respects of the accuracy and diversity, especially for themultimodal functions.

Swarm-robot and Multi-agent SystemsJune 14, 2013(Friday) 08:00-10:00 Daming Meeting Room

Learning by Imitation for the Improvement of the Individual andthe Social Behaviors of Self-Organized Autonomous AgentsAbdelhak Chatty, Philippe Gaussier, Ilhem Kallel, Philippe Laroque and Adel M. Alimi

Abstract. This paper shows that learning by imitation leads to a positive effect not only in humanbehavior but also in the behavior of the autonomous agents (AA) in the field of self-organized creationdeposits. Indeed, for each agent, the individual discoveries (i.e. goals) have an effect on the performanceof the population level and therefore they induce a new learning capability at the individual level.Particularly, we show through a set of experiments that adding a simple imitation capability toour bio-inspired architecture allows increasing the ability of agents to share more information andimproving the overall performance of the whole system. We will conclude with robotics’ experimentswhich will feature how our approach applies accurately to real life environments.

An Indexed K-D Tree for Neighborhood Generation in SwarmRobotics Simulation

Zhongyang Zheng and Ying TanAbstract. In this paper, an indexed K-D tree is proposed to solve the problem of neighborhoodsgeneration in swarm robotic simulation. The problem of neighborhoods generation for both robotsand obstacles can be converted as a set of range searches to locate the robots within the sensing areas.The indexed K-D tree provides an indexed structure for a quick search for the robots’ neighbors inthe tree generated by robots’ positions, which is the most time consuming operation in the processof neighborhood generation. The structure takes full advantage of the fact that the matrix generatedby robots’ neighborhoods is symmetric and avoids duplicated search operations to a large extent.Simulation results demonstrate that the indexed K-D tree is significantly quicker than normal K-Dtree and other methods for neighborhood generation when the population is larger than 10.

Interactive Robotic Fish for the Analysis of Swarm BehaviorTim Landgraf, Hai Nguyen, Stefan Forgo, Jan Schneider, Joseph Schroer, Christoph Kruger,

Henrik Matzke, Romain O. Clement, Jens Krause and Raul RojasAbstract. Biomimetic robots can be used to analyze social behavior through active interference withlive animals. We have developed a swarm of robotic fish that enables us to examine collective behaviorsin fish shoals. The system uses small wheeled robots, moving under a water tank. The robots arecoupled to a fish replica inside the tank using neodymium magnets. The position of the robots andeach fish in the swarm is tracked by two cameras. The robots can execute certain behaviors integratingfeedback from the swarm’s position, orientation and velocity. Here, we describe implementation detailsof our hardware and software and show first results of the analysis of behavioral experiments.

An Study of Indoor Localization Algorithm Based on ImperfectSignal Coverage in Wireless Networks

Ping Li, Limin Sun, Qing Fang, Jinyang Xie, Wu Yang and Kui MaAbstract. Existing localization algorithms didn’t consider the important factor of the antennameasuring angles. And most wireless indoor localization algorithms require a site survey processwhich is time-consuming and labor-intensive. This paper presents a featured region localizationalgorithm without site survey and discusses the measured angle in different intervals. According

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to the relationship among the fingerprints sets of the same angle interval, our proposed algorithm isused to find the featured points within the region. Regions of points are determined by calculatingEuclidean distance between the points and APs (Access Points). Experiments are conducted in an8m by 8m laboratory, and results show that this algorithm has superior performance compared withexisting algorithms.

The Latest Application and Development of Humanoid Robot onSwarm Intelligence

Leo Chen GuanyangAbstract. Robotics is the fastest growing and most advanced technology used in research andeducation. Aldebaran Robotics is a recognized world leader in the rapidly expanding field of humanoidrobotics. Founded in 2005 with offices in France, China and the United States, Aldebaran launched the”NAO” robot which has become an internationally adopted platform used in research and education.More than 780 prestigious universities, labs and secondary schools worldwide are working daily withNAO. By using NAO robot, professors and researchers stay up to date with major technical breakthroughsin programming and applied research.

Aldebaran believes that in coming years robots will positively impact our lives to the same extentas PCs and mobile devices did during the past 3 decades. Robots will change the way we learn, workand communicate. For example, a robotic companion to assist humans is no longer science fiction buta realistic answer to the requirements of an aging society. Aldebaran Robotics is developing practicalsolutions for everyday life by conducting research in areas such as autistic child therapy, human-robotinteraction and personal robotics.

This presentation will discuss the latest application and development of NAO robot in swarmintelligence field. Below is the outline.

1. Presentation of Aldebaran Robotics and NAO2. Why NAO for swarm intelligence research and potential solutions for your needs3. The latest worldwide research cases and results on swarm intelligence by using NAO

Mechanical PSO Aided by Extremum Seeking for Swarm RobotsCooperative Search

Qirong Tang and Peter EberhardAbstract. This paper addresses the issue of swarm robots cooperative search. A swarm intelligencebased algorithm, mechanical Particle Swarm Optimization (PSO), is first conducted which takes intoaccount the robot mechanical properties and guiding the robots searching for a target. In order toavoid the robot localization and to avoid noise due to feedback and measurements, a new scheme whichuses Extremum Seeking (ES) to aid mechanical PSO is designed. The ES based method is capableof driving robots to the purposed states generated by mechanical PSO without the necessity of robotlocalization. By this way, the whole robot swarm approaches the searched target cooperatively. Thispilot study is verified by numerical experiments in which different robot sensors are mimicked.

Image and Video ProcessingsJune 14, 2013(Friday) 08:00-10:00 Weishan Meeting Room

Remote Sensing Image Segmentation Based on Rough EntropyHuijie Sun, Tingquan Deng and Yingying Jiao

Abstract. Remote sensing image segmentation algorithms are proposed for different thresholds withrough sets theory and fuzzy sets theory in this paper. The target and background fuzzy sets aregotten with the gray image as a fuzzy sets; The target and background fuzzy sets are approximatedby two rough fuzzy sets, the optimal image segmentation threshold is chosen by the optimal standard,Experimental results show that the proposed algorithms are more effective and flexible.

A Real-time Noise Image Edge Detector Based on FPGAMeihua Xu, Chenjun Xia and Shuping Huang

Abstract. This paper describes a real-time noisy image edge detector to remove the noise which willbring negative effects on extraction and detection of image features. The average filtering algorithmis used to eliminate the noise of the original image and the Sobel edge detection operator is used toobtain image data. Both of the operation completes the functions including image acquisition andprocessing. Feasible verification of the edge detector is implemented in Altera EP3C55 using Verilog

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HDL language. Experimental results show that the edge detector is adaptive to the environment andcan extract the noisy image edge effectively and promptly.

Video Image Clarity Algorithm Research of USV Visual Systemunder the Sea Fog

Zhongli Ma, Jie Wen and Xiumei LiangAbstract. The visual system is one of the main equipment of unmanned surface vehicle (USV)autonomous navigation. Under the sea fog, atmospheric particles scattering leads to serious imagedegradation of the visual system. Because there is obvious sea-sky-line and the larger sky area in theimage of offshore, so firstly, the image segmentation is done to get sky area, and through anglicizingsky area characteristics, the sky brightness is estimated, and then a simplified physical model ofatmospheric scattering is built up, lastly image scene recovery is finished. Thinking about usingthis simple image defogging method to video image, foreground and background separation is done.Comparative research with several defogging methods onshore, results show that the proposed methodcan enhance the video image clarity of the USV visual system under sea fog very well. This researchbrought a good foundation to further improve the accuracy and precision of surface target identificationand tracking algorithm.

A Study of Vision-based Lane Recognition Algorithm for DriverAssistance

Feng Ran, Zhoulong Jiang, Tao Wang and Meihua XuAbstract. In this paper, a real-time lane detection algorithm based on vision is presented. Thisalgorithm improves the robustness and real-time of processing by combining with the dynamic regionof interest (ROI) and the prior knowledge. When the lanes detected from previous frames have littlechanges for several frames, we recognize the lane only in dynamic ROI. We also proposed an erosionoperator to refine the edge and a Hough transform with a restrict search space to detect lines witha faster rate. Experiments in structured road showed that the proposed lane detection method canwork robustly in real-time, and can achieve a speed of 30ms/frame for 720×480 image size.

Comparison and Evaluation of Human Locomotion Traits withDifferent Prosthetic Feet Using Graphical Methods from Control

AreaLulu Gong, Qirong Tang and Hongwei Mo

Abstract. This study investigates joint kinematics, joint angular positions, and orbital dynamicstability of human walking with different prosthetic feet by using graphical methods of phase planeportraits, Poincare maps and Floquet multipliers, respectively. The Flex foot, SACH foot, Seattlefoot and one non-specific optimized foot are taken as the research objects. Numerical experiments areperformed to compare and evaluate human locomotion traits on several aspects by focusing on theconcerned four kinds of prosthetic feet.

Reversible Data Embedment for Encrypted Cartoon ImagesUsing Unbalanced Bit Flipping

Wien Hong, Tung-Shou Chen, Jeanne Chen, Yu-Hsin Kao, Han-Yan Wu and Mei-Chen WuAbstract. In this paper, we propose a reversible data hiding technique to improve Zhang and Honget al.’s methods on cartoon images. Zhang and Hong et al. exploit the block complexity for dataextraction and image recovery. Their methods are efficient for natural images, however, the results areunsatisfactory when applies on cartoon images consisting of large flat area. By unbalanced flippingthe bits of pixel groups, the block complexity before and after flipping can be distinguished and thusthe error rate can be further reduced. Experimental results show that the proposed method has lowererror rate than those of Zhang and Hong et al.’s methods without degrading the image quality.

Other Swarm-based Search MethodsJune 14, 2013(Friday) 10:20-12:00 Daming Meeting Room

Optimal Power Flow Solution Using Self-EvolvingBrain-Storming Inclusive Teaching-Learning-Based Algorithm

Krishnanand K.R., S.M.F. Hasani, B.K. Panigrahi and S.K. PandaAbstract. In this paper, a new hybrid self-evolving algorithm is presented with its application to ahighly nonlinear problem in electrical engineering. The optimal power flow problem described here

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focuses on the minimization of the fuel costs of the thermal units while maintaining the voltagestability at each of the load buses. There are various restrictions on acceptable voltage levels,capacitance levels of shunt compensation devices and transformer taps making it highly complexand nonlinear. The hybrid algorithm discussed here is a combination of the learning principles fromBrain Storming Optimization algorithm and Teaching-Learning-Based Optimization algorithm, alongwith a self-evolving principle applied to the control parameter. The strategies used in the proposedalgorithm makes it self-adaptive in performing the search over the multi-dimensional problem domain.The results on an IEEE 30 Bus system indicate that the proposed algorithm is an excellent candidatein dealing with the optimal power flow problems.

A Study on an Evaluation Model for Robust Nurse Rosteringbased on Heuristics

Ziran Zheng and Xiaoju GongAbstract. Staff scheduling problem has been researched for decades and dozens of approaches havebeen proposed. Since in the hospital ward, an optimal solution could be changed for the uncertaincauses, such as sick leave or other unforeseen events. If these occur, the roster that has been settled asan optimal solution often needs to make changes such as shift moves and others, some of which couldhave impact on the roster’s fitness value. We first investigate the sensitive of an optimal solution underseveral operations of those types and the result shows that the solutions which are optimal obtainedwith the searching technique could indeed be affected by those disturbance. Secondly, the evaluationmethod is used to construct new evaluation function to improve the robustness of a roster. The modelcould apply to any method such as population-based evolutionary approaches and metaheuristics.Experiments show that it could help generate more robust solutions.

The Improvement on Controlling Exploration and Exploitation ofFirework Algorithm

Jianhua Liu, Shaoqiu Zheng and Ying TanAbstract. Firework algorithm (FWA) is a new Swarm Intelligence (SI) based optimization technique,which presents a different search manner and simulates the explosion of fireworks to search theoptimal solution of problem. Since it was proposed, fireworks algorithm has shown its significance andsuperiority in dealing with the optimization problems. However, the calculation of number of explosionspark and amplitude of firework explosion of FWA should dynamically control the exploration andexploitation of searching space with iteration. The mutation operator of FWA needs to generate thesearch diversity. This paper provides a kind of new method to calculate the number of explosion sparkand amplitude of firework explosion. By designing a transfer function, the rank number of fireworkis mapped to scale of the calculation of scope and spark number of firework explosion. A parameteris used to dynamically control the exploration and exploitation of FWA with iteration going on. Inaddition, this paper uses a new random mutation operator to control the diversity of FWA search.The modified FWA have improved the performance of original FWA. By experiment conducted by thestandard benchmark functions, the performance of improved FWA can match with that of particleswarm optimization (PSO).

An Artificial Chemistry System for Simulating Cell Chemistry:The First Step

Chien-Le Goh, Hong Tat Ewe and Yong Kheng GohAbstract. Artificial chemistry is a man-made system that is similar to a real chemical system. Itrepresents a good starting point to simulate cell processes from the bio-chemistry level. In this article,an artificial chemistry system which strikes a balance among closeness to reality, fast simulation speedand high flexibility is proposed. Preliminary results have shown that the model can simulate a generalreversible reaction well.

Colony-Based Optimization AlgorithmsJune 14, 2013(Friday) 10:20-11:50 Weishan Meeting Room

Constrained Multi-objective Biogeography OptimizationAlgorithm for Robot Path Planning

Hongwei Mo, Zhidan Xu and Qirong TangAbstract. Constrained multi-objective optimization involves multiple objectives subjected to some

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equality or inequality constraints so that it may require search a set of non-dominated feasible solutions.Inspired from this, in this paper, a novel constrained multi-objective biogeography optimizationalgorithm is proposed and used for solving robot path planning problem since it can be defined as aconstrained multi-objective optimization problem. Experimental results compared with Non-dominatedSorting Genetic AlgorithmII show that the proposed algorithm has better performance.

An Ant Colony System Based on the NetworkTao Qian, Zili Zhang, Chao Gao, Yuheng Wu and Yuxin Liu

Abstract. The Network model exhibits the feature of important pipelines being reserved with theevolution of network during the process of solving a maze problem. Drawing on this feature, an AntColony System (ACS), denoted as PNACS, is proposed based on the Network (PN). When updatingpheromone matrix, we should update both pheromone trails released by ants and the pheromonesflowing in a network. This hybrid algorithm can overcome the low convergence rate and local optimalsolution of ACS when solving the Traveling Salesman Problem (TSP). Some experiments in syntheticand benchmark networks show that the efficiency of PNACS is higher than that of ACS. Moreimportant, PNACS has strong robustness that is very useful for solving a higher dimension TSP.

Algorithms and Framework for Comparison of Bee-IntelligenceBased Peer-to-Peer Lookup

Vesna Sesum Cavic and Eva KuhnAbstract. Peer-to-peer has proven to be a scalable technology for retrieval of information that iswidely spread among distributed sites and that is subject to dynamic changes. However, selection of aright search algorithm depends on many factors related to actual data content and application problemat hand. A comparison of different algorithms is difficult, especially if many different approaches(intelligent or unintelligent ones) shall be evaluated fairly and possibly also in combinations. Inthis paper, we describe a generic architectural pattern that serves as an overlay network based onautonomous agents and decentralized control. It supports plugging of different algorithms for searchingand retrieving data, and thus eases comparison of algorithms in various topology configurations. Afurther novelty is to use bee intelligence for the lookup problem, spot optimal parameters’ settings, andevaluate the bee algorithm by using the architectural pattern to benchmark it with other algorithms.

Hybrid AlgorithmsJune 14, 2013(Friday) 13:30-15:10 Daming Meeting Room

Hybrid Gravitational Search and Clonal Selection Algorithm forGlobal Optimization

Shangce Gao, Hongjian Chai, Beibei Chen and Gang YangAbstract. In recent years, there has been a growing interest in algorithms inspired by the behaviorsof natural phenomena. However, the performance of any single pure algorithm is limited by the sizeand complexity of the problem. To further improve the search effectiveness and solution robustness,hybridization of different algorithms is a promising research direction. In this paper, we propose ahybrid iteration algorithm by combing the gravitational search algorithm with the clonal selection. Thegravitational search performs exploration in the search space, while the clonal selection is implementedto carry out exploitation within the neighborhood of the solutio found by gravitational search. Theemerged hybrid algorithm, called GSCSA, thus reasonably combines the characteristics of both basealgorithms. Experimental results based on several benchmark functions demonstrate the superiorityof the proposed algorithm in terms of solution quality and convergence speed.

A Physarum Network Evolution Model Based on IBTMYuxin Liu, Zili Zhang, Chao Gao, Yuheng Wu and Tao Qian

Abstract. The traditional Cellular Automation-based Physarum model reveals the process of amoebicself-organized movement and self-adaptive network formation based on bubble transportation. However,a bubble in the traditional Physarum model often transports within active zones and has little changeto explore new areas. And the efficiency of evolution is very low because there is only one bubblein the system. This paper proposes an improved model, named as Improved Bubble TransportationModel (IBTM). Our model adds a time label for each grid of environment in order to drive bubbles toexplore new areas, and deploys multiple bubbles in order to improve the evolving efficiency of Physarumnetwork. We first evaluate the morphological characteristics of IBTM with the real Physarum, and

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then compare the evolving time between the traditional model and IBTM. The results show that IBTMcan obtain higher efficiency and stability in the process of forming an adaptive network.

Global Optimization Inspired by Quantum PhysicsXiaofei Huang

Abstract. Scientists have found that atoms and molecules in nature have an amazing power at findingtheir global minimal energy states even when their energy landscapes are full of local minima. Recently,the author postulated an optimization algorithm for understanding this fundamental feature of nature.This paper presents a version of this algorithm for attacking continuous optimization problems. Onlarge size benchmark functions, it significantly outperformed the standard particle swarm optimizationalgorithm.

A Study of Human Flesh Search Based on SIR Flooding onScale-Free Networks

Dawei Meng, Lei Zhang and Long ChengAbstract. With the development of social networks, information is shared, amended, and integratedamong users. Meanwhile, some questions begin to generate public interests. How does the informationpropagate in the network? How do human factors affect the spreading patterns of information? Howdo we construct models to understand the collective group behavior based on probabilistic individualchoices? We try to answer the above questions by investigating “Human Flesh Search”(HFS), aphenomenon of digging out full privacy information of a target person with the help of massivecollaboration of netizens by integrating information pieces during propagation. SIR model, whichis often used to study epidemic diseases, is employed to provide a mathematical explanation of theprocess of HFS. Experimental results reveal that information entropy has significant influence on thenetwork topology, which in turn affects the probability of affecting network neighbors and finally resultsin different efficiency of information spreading.

Intelligent ControlJune 14, 2013(Friday) 13:30-15:30 Weishan Meeting Room

Brownian Snake Measure-valued Markov Decision ProcessZhenzhen Wang and Hancheng Xing

Abstract. This paper presents a model called Brownian snake measure-valued Markov decisionprocess (BSMMDP) that can simulate an important characteristic of human thought, that is, whenpeople think problems, sometimes they can suddenly connect events that are remote in space-timeso as to solve problems. We also discuss how to find an (approximate) optimal policy within thisframework. If Artificial Intelligence can simulate human thought, then maybe it is beneficial for itsprogress. BSMMDP is just following this idea, and trying to describe the talent of human mind.

Genetic Evolution of Control SystemsMu-Song Chen, Tze-Yee Ho and Chipan Hwang

Abstract. In this paper, we present to utilize Genetic Algorithms (GAs) as tools to model controlprocesses. Two different crossover operators are combined during evolution to maintain populationdiversity and to sustain local improvement in the search space. In this manner, a balance betweenglobal exploration and local exploitation is reserved during genetic search. To verify the efficiency ofthe proposed method, the desired control sequences of a given system are solved by the optimal controltheory as well as GA with hybrid crossovers to compare their performances. The experimental resultsshowed that the control sequences obtained from the proposed GA with hybrid crossovers are quiteconsistent with the results of the optimal control.

A New Target Tracking Algorithm Based on Online AdaboostZhuowen Lv, Kejun Wang and Tao Yan

Abstract. In order to overcome the effect of blocking in process of target tracking under stationarycamera, a target tracking algorithm based on online Adaboost was presented. Codebook model wasset up to detect moving target in YUV color space; in process of tracking, feature of online Adaboostfused texture contours and color, then accurate target location was obtained. The experimental resultsshow that, the detecting algorithm in this paper has good detecting results, which provides assistanceto tracking. The proposed tracking algorithm is effective for the targets having blocking, even a largearea of blocking in more complex scenes.

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Solving Hamilton Path Problem with P SystemLaisheng Xiang and Jie Xue

Abstract. P systems are biologically inspired theoretical models of distributed and parallel computing.Hamilton path is a classical NP problem, recently, there are lots of methods to solve it. Today we givea new and efficient algorithm to this classic. This paper uses the improved P system with priority andpromoters/inhibitors to give an efficient solution to Hamilton path problem. We give two examples toillustrate our method’s feasibility. We discuss future research problems also.

False Data Attacks Judgment Based on Consistency Loop Modelin Wireless Sensor Networks

Ping Li, Limin Sun, Wu Yang, Qing Fang, Jinyang Xie and Kui MaAbstract. Wireless sensor networks are usually deployed in complex environments; an attacker caneasily inject false data by capturing nodes, causing serious consequences. The main work of this paperis as follows. Firstly, the logical loop model is created based on the estimated value of the sourceevents of every wireless sensor network node. Secondly, each node based on RSSI find neighbor nodesby establishing consistency loop model. Finally, the malicious node is determined by comparing thesimilarities and differences of the nodes between the two loop models. The simulation shows that thismechanism is effective to inhibit the infringement of malicious nodes to the network, and improvenetwork security performance.

The Design and Implementation of Motor Drive for an ElectricBicycle

Tze-Yee Ho, Mu-Sung Chen, Wei-Chieh Chen and Chih-Hao ChiangAbstract. In recent years, the highly growth and development of world economy results in thenatural resources being gradually run out and the environment further directly and indirectly beingpolluted more severe. Consequently, any kind of alternative energy resource have been developed,harvested and designed. An electric bicycle based on a blushless dc motor drive which has highefficiency, zero pollution, clean and convenient, is then designed and implemented in this paper. Thehardware design based on a microcontroller is analyzed and discussed. The software programmingis developed in MPLAB integrated development environment from the Microchip Technology Inc.Finally, a prototype of blushless dc motor drive for an electric bicycle is realized and demonstrated.The experimental results show the feasibility and fidelity of the complete designed system.

Data Mining MethodsJune 14, 2013(Friday) 15:50-17:30 Daming Meeting Room

An Online Trend Analysis Method for Measuring Data Based onHistorical Data Clustering

Jianfeng Qu, Maoyun Guo, Yi Chai, Zhimin Yang, Tao Zou, Tian Lan and Zhenglei LiuAbstract. It is important to analyze and predict the measuring data trend in industrial measuring andcontrolling process. The paper introduces a method for predicting the trend of the current measuringdata based on clustering the historical data. It calculates the similarities of the current trend and thebases result from the clustering. And with these similarities, the future trend of the current measuringdata can be predicted , the combination of the above bases representing low frequency and a reviserrepresenting high frequency. The simulation shows the weights of high or low frequency have effect onthe precision of predict results. It is also found that the proposed method can predict more preciselythan the RBFNNs method in high frequency.

Discover Community Leader in Social Network with PageRankRui Wang, Weilai Zhang, Han Deng, Nanli Wang, Qing Miao and Xinchao Zhao

Abstract. Community leaders are individuals who have huge influence on social network communities.Discovering community leaders in social networks is of great significance for research on the structuresof the social networks and for commercial application. Based on the core idea of the PageRankalgorithm, this paper firstly processes data selected from Sina microblog, and extracts three keyindicators, comprising the number of followers, the number of comments and the number of reposts;then based on their mutual relationship, that is following or followed, it obtains the weight of influencefor each individual user; and then after a finite number of iterations, this paper identifies the communityleader in Sina microblog, by which its comprehensive influence on its community are reflected.

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A New Efficient Text Clustering Ensemble Algorithm Based onSemantic Sequences

Zhonghui Feng, Junpeng Bao and Kaikai LiuAbstract. The idea of cluster ensemble is combining the multiple clustering of a data set into aconsensus clustering for improving the quality and robustness of results. In this paper, a new textclustering ensemble (TCE) algorithm is proposed. First, text clustering results of applying k-means andsemantic sequence algorithms are produced. Then in order to generate co-association matrix betweensemantic sequences, the clustering results are combined based on the overlap coefficient similarityconcept. Finally, the ultimate clusters are obtained by merging documents corresponding to similarsemantic sequence on this matrix. Experiment results of proposed method on real data sets arecompared with other clustering results produced by individual clustering algorithms. It is showed thatTCE is efficient especially on long documents set.

A Novel Algorithm for Kernel Optimization of Support VectorMachine

Lijie LiAbstract. Model optimization namely the kernel function and parameter selection is an importantfactor to affect the generalization ability of support vector machine (SVM). To solve model optimizationproblem of support vector machine classifier, a novel algorithm (GC-ABC) is proposed which integrateartificial bee colony algorithm, greedy algorithm and chaos search strategy. The simulation resultsshow that the accuracy of SVM optimized by GC-ABC is superior to the SVM optimized by geneticalgorithm and ant colony algorithm. The experiments further suggest that GC-ABC algorithm has fastconvergence and strong global search ability, which improves the performance of the support vectormachine.

Training Least-square SVM by a Recurrent Neural NetworkBased on Fuzzy C-mean Approach

Fengqiu Liu, Jianmin Wang and Sitian QinAbstract. An algorithm to solve the least square support vector machine (LSSVM) is presented.The underlying optimization problem for LSSVM follows a system of linear equations. The proposedalgorithm incorporates a fuzzy c-mean (FCM) clustering approach and the application of a recurrentneural network (RNN) to solve the system of linear equations. First, a reduced training set is obtainedby the FCM clustering approach and used to train LSSVM. Then a gradient system with discontinuousrighthand side, interpreted as an RNN, is designed by using the corresponding system of linearequations. The fusion of FCM clustering approach and RNN overcomes the loss of spareness ofLSSVM. The efficiency of the algorithm is empirically shown on a benchmark data set generated fromthe University of California at Irvine (UCI) machine learning database.

System and Information SecurityJune 14, 2013(Friday) 15:50-17:30 Weishan Meeting Room

Detection of Human Abnormal Behavior of the Ships SecurityFengxu Guan, Xiaolong Liu and Xiangyu Meng

Abstract. The ship’s security depends on the patrol, which is difficult to ensure ship safety inreal-time. In order to secure ship more safety, the intelligent video surveillance technology is appliedto the ship. Firstly, the background model is established through codebook algorithm, then themovement target of the human are detected accurately. Secondly, the characteristics of the humanbody are extracted through HU invariant moments. By similarity matching with the abnormal behaviortemplate and the feature of aspect ratio of the human body, the abnormal behavior of the human bodyis detected. Finally, the experimental results show that this algorithm can be achieved very well. Ithas obvious advantages on frame difference algorithm and mixed Gaussian algorithm. In the actualenvironment of anchoring ship, the abnormal behavior of the human body is detected effectively.

Experimentation of Data Mining Technique for Systems SecurityA Comparative Study

Ahmed Chaouki Lokbani, Ahmed Lehireche and Reda Mohamed HamouAbstract. Given the increasing number of users of computer systems and networks, it is difficult toknow the profile of the latter and therefore the intrusion has become a highly prized of community

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of network security. In this paper to address the issues mentioned above, we used the data miningtechniques namely association rules, decision trees and Bayesian networks. The results obtained onthe KDD’99 benchmark has been validated by several evaluation measures, and are promising andprovide access to other techniques and hybridization to improve the security and confidentiality in thefield.

The Extension of Linear Coding Method for Automated AnalogCircuit Design

Zhi Li and Jingsong HeAbstract. Encoding method is one of the key factors of evolutionary design of analog circuit. Due tothe adaptability, convenience and relatively short length of linear coding method, it has been widelyused for automation of analog circuit design. Evolutionary design of analog circuits, which is notlimited to traditional knowledge, could generate circuits with novel structures and parameters. Thenovel structures provide more possible solutions for fault-tolerance design of analog circuits. While,the current linear coding method based on five connection ways limits the number of possible circuitstructures. So in this paper, we improve the existing linear coding method by expanding the instructionset. The experimental results show that the improved linear coding method can generate richer circuitstructures, and it opens up a new way for the fault-tolerance design of analog circuits.

The UML Diagram to VHDL Code Transformation Based onMDA Methodology

Chi-Pan Hwang and Mu-Song ChenAbstract. The Model Driven Architecture (MDA) methodology requires several intelligent operationstages, such as the computation independent model transformation (CIMT), the platform independentmodel transformation (PIMT), and the platform specific model transformation (PSMT), to progressivelytransform an abstract model to a physical system. The special Unified Modeling Language (UML)or StarUML is the core tool of CIMT that models a digital system in a diagram paradigm. PIMTuses the Python language with minidom object to perform a series translation from UML diagram toVHSIC Hardware Description Language (VHDL) code. Finally, the PSMT imports an os object toPython for running a series of synthesis command script to get bit stream that is finally downloadedinto FPGA device to complete the realization of the digital logic circuit.

OFDM System with Reduce Peak-to-Average Power Ratio UsingOptimum Combination of Partial Transmit Sequences

Yung-Cheng Yao, Ho-Lung Hung and Jyh-Horng WenAbstract. In this paper, we propose a new peak-to-average power ratio (PAPR) reduction scheme oforthogonal frequency division multiplexing (OFDM) system, called invasive weed optimization (IWO)scheme, which considerably reduces the computational complexity with keeping the similar PAPRreduction performance compared with the conventional partial transmit sequences (PTS) scheme. PTSis a distortionless PAPR reduction technique, but its high search complexity for finding optimal phasefactors must be reduced for usable applications. The proposed scheme is analytically and numericallyevaluated for the OFDM system specified in the IEEE 802.16 standard. IWO based PTS is comparedto different PTS schemes for PAPR reduction and search complexity performances. The simulationresults show that the proposed IWO-based PTS method provides good PAPR reduction and bit errorrate (BER) performances.

Terrain Image Classification with SVMMu-Song Chen, Chi-Pan Hwang and Tze-Yee Ho

Abstract. Remote sensing is an important tool in a variety of scientific researches which can helpto study and solve many practical environmental problems. Classification of remote sensing image,however, is usually complex in many respects that a lot of different ground objects show mixturedistributions in space and change with temporal variations. Therefore, automatic classification ofland covers is of practical significance to the exploration of desired information. Recently, supportvector machine (SVM) has shown its capability in solving multi-class classification for different groundobjects. In this paper, the extension of SVM to its online version is employed for terrain imageclassification. An illustration of online SVM learning and classification on San Francisco Bay area isalso presented to demonstrate its applicability.

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Index (a=abstract c=chair cc=cochair)

Alimi, Adel M., 24, 36a

Amnuaisuk, Somnuk Phon, 21, 32a

Bachache, Nasseer K., 22, 22cc, 34a

Bai, Bing, 23, 35a

Bao, Junpeng, 27, 43a

Barroso, Giovanni Cordeiro, 22, 33a

Bezerra, Jose Roberto, 22, 33a

Bin, Zhou, 22, 33a

Campana, Emilio F., 20, 30a

Cavic, Vesna Sesum, 26, 40a

Chai, Hongjian, 26, 40a

Chai, Yi, 27, 42a

Chao, Wang, 21, 22, 30a, 33a

Chatty, Abdelhak, 24, 24c, 36a

Chelly, Zeineb, 22, 22c, 34a

Chen, Beibei, 26, 40a

Chen, Jeanne, 25, 38a

Chen, Mu-Song, 26, 28, 28c, 41a, 44a

Chen, Mu-Sung, 27, 42a

Chen, Tung-Shou, 25, 38a

Chen, Walter W., 20, 30a

Chen, Wei-Chieh, 27, 42a

Cheng, Baozhi, 21, 32a

Cheng, Long, 26, 41a

Cheng, Shi, 20, 29a

Chiang, Chih-Hao, 27, 42a

Chunyu, Guo, 21, 22, 30a, 33a

Clement, Romain O., 24, 36a

Dai, Shengkui, 20, 29a

Deng, Han, 27, 42a

Deng, Tingquan, 24, 37a

Deng, Xiuquan, 23, 23c, 35a

Diez, Matteo, 20, 30a

Eberhard, Peter, 24, 37a

Elouedi, Zied, 22, 34a

Ewe, Hong Tat, 25, 39a

Fan, Zhichao, 21, 31a

Fang, Qing, 24, 27, 36a, 42a

Fasano, Giovanni, 20, 20cc, 30a

Feng, Zhonghui, 27, 43a

Forgo, Stefan, 24, 36a

Furtado, Raimundo, 22, 33a

Gao, Chao, 26, 40a

Gao, Dehua, 23, 35a

Gao, Shangce, 26, 26c, 40a

Gaussier, Philippe, 24, 36a

Goh, Chien-Le, 25, 39a

Goh, Yong Kheng, 25, 39a

Gong, Lulu, 25, 38a

Gong, Xiaoju, 25, 39a

Guan, Fengxu, 28, 43a

Guanyang, Leo Chen, 24, 37a

Guo, Maoyun, 27, 42a

Guo, Zongguang, 21, 32a

Guoliang, Wang, 22, 33a

Hamou, Reda Mohamed, 28, 43a

Hasani, S.M.F., 25, 38a

He, Jingsong, 23, 28, 35a, 44a

Ho, Tze-Yee, 26, 26c, 27, 28, 41, 42a, 44a

Hong, Wien, 25, 38a

Hu, Xi, 21, 32a

Huang, Huali, 20, 29a

Huang, Shuping, 24, 37a

Huang, Xiaofei, 26, 41a

Hung, Ho-Lung, 28, 44a

Hwang, Chi-Pan, 28, 44a

Hwang, Chipan, 26, 41a

Janecek, Andreas, 20c, 21, 31a

Jiang, Zhoulong, 25, 38a

Jiao, Yingying, 24, 37a

Jordan, Tobias, 21, 31a

K.R., Krishnanand, 25, 38a

Kallel, Ilhem, 24, 36a

Kao, Yu-Hsin, 25, 38a

Krause, Jens, 24, 36a

45

Kruger, Christoph, 24, 36a

Kuhn, Eva, 26, 40a

Kuo, Jian-Long, 21c, 22, 33a

Lan, Tian, 27, 42a

Landgraf, Tim, 24, 36a

Laroque, Philippe, 24, 36a

Leao, Ruth Pastora Saraiva, 22, 33a

Lehireche, Ahmed, 28, 43a

Li, Lijie, 27, 43a

Li, Ping, 24, 27, 36a, 42a

Li, Zhi, 28, 44a

Liang, Jane Jing, 20, 29a

Liang, Xiumei, 25, 38a

Lin, Chao, 23, 35a

Liou, Cheng-Dar, 21, 31a

Liu, Fengqiu, 27, 43a

Liu, Jianhua, 25, 25c, 39a

Liu, Kaikai, 27, 43a

Liu, Liqiang, 21, 31a

Liu, Xiaolong, 28, 43a

Liu, Yuxin, 26, 40a

Liu, Zhenglei, 27, 42a

Lokbani, Ahmed Chaouki, 28, 43a

Lv, Zhuowen, 27, 41a

Ma, Kui, 24, 27, 36a, 42a

Ma, Zhongli, 25, 38a

Matzke, Henrik, 24, 36a

Medeiros, Eudes Barbosa de, 22, 33a

Meng, Dawei, 26, 41a

Meng, Xiangyu, 28, 43a

Miao, Qing, 27, 42a

Mo, Hongwei, 24c, 25, 26, 26c, 38, 39a

Neto, Fernando Buarque de Lima, 21, 31a

Nguyen, Hai, 24, 36a

Niu, Ben, 20, 20cc, 29a

Panda, S.K., 25, 38a

Panigrahi, B.K., 25, 38a

Peri, Daniele, 20, 30a

Qian, Tao, 26, 40a

Qin, Sitian, 27, 43a

Qu, Jianfeng, 27, 42a

Ran, Feng, 25, 38a

Rojas, Raul, 24, 36a

Schneider, Jan, 24, 36a

Schroer, Joseph, 24, 36a

Shen, Zheping, 20, 30a

Sheng, Huang, 21, 30a

Shi, Yuhui, 20, 20c, 29a

Sun, Huijie, 24, 37a

Sun, Limin, 24, 27, 36a, 42a

Sun, Yanxia, 20, 30a

Sun, Yifan, 22, 34a

Tan, Lijing, 20, 29a

Tan, Ying, 24, 25, 36a, 39a

Tang, Qirong, 24–26, 37–39a

Wang, Cong, 21, 22, 32, 33a

Wang, Cuirong, 22, 33a

Wang, Jianan, 20, 30a

Wang, Jianmin, 27, 43a

Wang, Kejun, 27, 41a

Wang, Nanli, 27, 42a

Wang, Rui, 27, 42a

Wang, Tao, 25, 38a

Wang, Xiangguo, 21, 31a

Wang, Zenghui, 20, 30a

Wang, Zhenzhen, 26, 41a

Wanlong, Ren, 21, 22, 30a, 33a

Wen, Jie, 25, 38a

Wen, Jinyu, 22, 34a

Wen, Jyh-Horng, 28, 44a

Winklerova, Zdenka, 20, 29a

Wu, Han-Yan, 25, 38a

Wu, Huiyong, 22, 34a

Wu, Jingjin, 20, 29a

Wu, Mei-Chen, 25, 38a

Wu, Ting, 23, 35a

Wu, Yuheng, 26, 40a

Wu, Zhou, 23, 36a

Wyk, Barend Jacobus van, 20, 30a

Xia, Chenjun, 24, 37a

46


Xia, Guoqing, 22, 34a

Xia, Xiaohua, 23, 36a

Xiang, Laisheng, 27, 42a

Xiang, Wenjie, 20, 29a

Xie, Jinyang, 24, 27, 36a, 42a

Xing, Hancheng, 26, 41a

Xu, Meihua, 24, 25, 37, 38a

Xu, Zhidan, 26, 39a

Xue, Jie, 27, 42a

Xue, Mei, 23, 35a

Yan, Tao, 27, 41a

Yang, Gang, 26, 40a

Yang, Wu, 24, 27, 36a, 42a

Yang, Ying, 21, 32a

Yang, Zhimin, 27, 42a

Yao, Yung-Cheng, 28, 44a

Yuan, Ying, 21, 22, 32, 33a

Zhang, Guoxiang, 21, 32a

Zhang, Jiangfeng, 23, 36a

Zhang, Lei, 26, 41a

Zhang, Lifeng, 22, 34a

Zhang, Weilai, 27, 42a

Zhang, Zili, 26, 40a

Zhao, Chun, 20, 29a

Zhao, Siwei, 22, 33a

Zhao, Xinchao, 27, 27c, 42a

Zheng, Shaoqiu, 25, 39a

Zheng, Zhongyang, 24, 36a

Zheng, Ziran, 25, 39a

Zhu, Shiming, 22, 33a

Zhuang, Peixian, 20, 29a

Zou, Tao, 27, 42a

Zuo, Xingquan, 21, 21c, 32a

47

the fourth international conference on swarm intelligence …2013.ic-si.org/download/icsi 2013 final...

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