Advances in Swarm Intelligence

1. Front Matter

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2. Theoretical Analysis of Swarm Intelligence Algorithms

   1. Stability Problem for a Predator-Prey System

      • Zvi Retchkiman Konigsberg

      Pages 1-10

   2. Study on the Local Search Ability of Particle Swarm Optimization

      • Yuanxia Shen, Guoyin Wang

      Pages 11-18

   3. The Performance Measurement of a Canonical Particle Swarm Optimizer with Diversive Curiosity

      • Hong Zhang, Jie Zhang

      Pages 19-26

   4. Mechanism and Convergence of Bee-Swarm Genetic Algorithm

      • Di Wu, Rongyi Cui, Changrong Li, Guangjun Song

      Pages 27-33

   5. On the Farther Analysis of Performance of the Artificial Searching Swarm Algorithm

      • Tanggong Chen, Lijie Zhang, Lingling Pang

      Pages 34-41

   6. Orthogonality and Optimality in Non-Pheromone Mediated Foraging

      • Sanza Kazadi, James Yang, James Park, Andrew Park

      Pages 42-51

   7. An Adaptive Staged PSO Based on Particles’ Search Capabilities

      • Kun Liu, Ying Tan, Xingui He

      Pages 52-59

3. PSO Algorithms

   1. A New Particle Swarm Optimization Algorithm and Its Numerical Analysis

      • Yuelin Gao, Fanfan Lei, Miaomiao Wang

      Pages 60-67

   2. A New PSO Model Mimicking Bio-parasitic Behavior

      • Quande Qin, Rongjun Li, Ben Niu, Li Li

      Pages 68-77

   3. KNOB Particle Swarm Optimizer

      • Junqi Zhang, Kun Liu, Ying Tan

      Pages 78-85

   4. Grouping-Shuffling Particle Swarm Optimization: An Improved PSO for Continuous Optimization

      • Yinghai Li, Xiaohua Dong, Ji Liu

      Pages 86-93

   5. Gender-Hierarchy Particle Swarm Optimizer Based on Punishment

      • Jiaquan Gao, Hao Li, Luoke Hu

      Pages 94-101

   6. An Improved Probability Particle Swarm Optimization Algorithm

      • Qiang Lu, Xuena Qiu

      Pages 102-109

   7. An Automatic Niching Particle Swarm for Multimodal Function Optimization

      • Yu Liu, Zhaofa Yan, Wentao Li, Mingwei Lv, Yuan Yao

      Pages 110-119

   8. An Availability-Aware Task Scheduling for Heterogeneous Systems Using Quantum-behaved Particle Swarm Optimization

      • Hao Yuan, Yong Wang, Long Chen

      Pages 120-127

   9. A Novel Encoding Scheme of PSO for Two-Machine Group Scheduling

      • Cheng-Dar Liou, Chun-Hung Liu

      Pages 128-134

   10. Improved Quantum Particle Swarm Optimization by Bloch Sphere

       • Yu Du, Haibin Duan, Renjie Liao, Xihua Li

       Pages 135-143

   11. An Improved Particle Swarm Optimization for Permutation Flowshop Scheduling Problem with Total Flowtime Criterion

       • Xianpeng Wang, Lixin Tang

       Pages 144-151

4. Applications of PSO Algorithms

   1. Broadband MVDR Beamformer Applying PSO

      • Liang Wang, Zhijie Song

      Pages 152-159

This book and its companion volume, LNCS vols. 6145 and 6146, constitute the proceedings of the International Conference on Swarm Intelligence (ICSI 2010) held in Beijing, the capital of China, during June 12-15, 2010. ICSI 2010 was the ?rst gathering in the world for researchers working on all aspects of swarm intelligence, and providedan academic forum for the participants to disseminate theirnewresearch?ndingsanddiscussemergingareasofresearch.Italsocreated a stimulating environment for the participants to interact and exchange inf- mation on future challenges and opportunities of swarm intelligence research. ICSI 2010 received 394 submissions from about 1241 authors in 22 countries and regions (Australia, Belgium, Brazil, Canada, China, Cyprus, Hong Kong, Hungary, India, Islamic Republic of Iran, Japan, Jordan, Republic of Korea, Malaysia, Mexico, Norway, Pakistan, South Africa, Chinese Taiwan, UK, USA, Vietnam) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Each submission was reviewed by at least three reviewers. Based on rigorous reviews by the Program Committee members and reviewers, 185 high-quality papers were selected for publication in the proceedings with the acceptance rate of 46.9%. The papers are organized in 25 cohesive sections covering all major topics of swarm intelligence research and development.

• algorithms
• computational intelligence
• data mining
• evolution
• evolutionary computation
• genetic algorithm
• learning
• machine learning
• multi-objective optimization
• optimization
• robot
• swarm intelligence
• algorithm analysis and problem complexity

• Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, Beijing, China

  Ying Tan

• Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University, Suzhoum, China

  Yuhui Shi

• Department of Electrical and Computer Engineering, National University of Singapore, Singapore

  Kay Chen Tan

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