Advances in Swarm Intelligence

1. Front Matter

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2. Analysis of Swarm Intelligence Based Algorithms

   1. Interactive Robotic Fish for the Analysis of Swarm Behavior

      • Tim Landgraf, Hai Nguyen, Stefan Forgo, Jan Schneider, Joseph Schröer, Christoph Krüger et al.

      Pages 1-10

   2. The Improvement on Controlling Exploration and Exploitation of Firework Algorithm

      • Jianhua Liu, Shaoqiu Zheng, Ying Tan

      Pages 11-23

   3. Diversity Analysis of Population in Shuffled Frog Leaping Algorithm

      • Lianguo Wang, Yaxing Gong

      Pages 24-31

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

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

      Pages 32-39

3. Particle Swarm Optimization

   1. Maturity of the Particle Swarm as a Metric for Measuring the Collective Intelligence of the Swarm

      • Zdenka Winklerová

      Pages 40-54

   2. Particle Swarm Optimization in Regression Analysis: A Case Study

      • Shi Cheng, Chun Zhao, Jingjin Wu, Yuhui Shi

      Pages 55-63

   3. Mechanical PSO Aided by Extremum Seeking for Swarm Robots Cooperative Search

      • Qirong Tang, Peter Eberhard

      Pages 64-71

   4. Multi-swarm Particle Swarm Optimization with a Center Learning Strategy

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

      Pages 72-78

   5. Opposition-Based Learning Fully Informed Particle Swarm Optimizer without Velocity

      • Ying Gao, Lingxi Peng, Fufang Li, Miao Liu, Waixi Liu

      Pages 79-86

   6. GSO: An Improved PSO Based on Geese Flight Theory

      • Shengkui Dai, Peixian Zhuang, Wenjie Xiang

      Pages 87-95

   7. Improved Algorithms Based on the Simple Particle Swarm Optimization

      • Lei Liu, Xiaomeng Zhang, Zhiguo Shi, Tianyu Zhang

      Pages 96-103

   8. A Test of Position Determination with PSO

      • Walter W. Chen, Jian-An Wang, Zhe-Ping Shen

      Pages 104-111

   9. Initial Particles Position for PSO, in Bound Constrained Optimization

      • Emilio Fortunato Campana, Matteo Diez, Giovanni Fasano, Daniele Peri

      Pages 112-119

   10. Visualizing the Impact of Probability Distributions on Particle Swarm Optimization

       • Tjorben Bogon, Fabian Lorig, Ingo J. Timm

       Pages 120-128

   11. Local and Global Search Based PSO Algorithm

       • Yanxia Sun, Zenghui Wang, Barend Jacobus van Wyk

       Pages 129-136

   12. Cask Theory Based Parameter Optimization for Particle Swarm Optimization

       • Zenghui Wang, Yanxia Sun

       Pages 137-143

4. Applications of PSO Algorithms

   1. A Piecewise Linearization Method of Significant Wave Height Based on Particle Swarm Optimization

      • Liqiang Liu, Zhichao Fan, Xiangguo Wang

      Pages 144-151

   2. Design Fuzzy Logic Controller by Particle Swarm Optimization for Wind Turbine

      • Nasseer K. Bachache, Jinyu Wen

      Pages 152-159

   3. Parameter Identification of RVM Runoff Forecasting Model Based on Improved Particle Swarm Optimization

      • Yuzhi Shi, Haijiao Liu, Mingyuan Fan, Jiwen Huang

      Pages 160-167

This book and its companion volume, LNCS vols. 7928 and 7929 constitute the proceedings of the 4th International Conference on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised full papers presented were carefully reviewed and selected from 268 submissions. The papers are organized in 22 cohesive sections covering all major topics of swarm intelligence research and developments. The following topics are covered in this volume: analysis of swarm intelligence based algorithms, particle swarm optimization, applications of particle swarm optimization algorithms, ant colony optimization algorithms, biogeography-based optimization algorithms, novel swarm-based search methods, bee colony algorithms, differential evolution, neural networks, fuzzy methods, evolutionary programming and evolutionary games.

• applications
• design
• graphs
• machine learning
• optimization algorithms
• algorithm analysis and problem complexity

• Key Laboratory of Machine Perception (MOE), School of lectronics Engineering and Computer Science, Department of Machine Intelligence, Peking University, Beijing, China

  Ying Tan

• Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

  Yuhui Shi

• Automation College, Harbin Engineering University, Harbin, China

  Hongwei Mo

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