Fully Learned Multi-swarm Particle Swarm Optimization

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• 作者：Ben Niu (18) (19) (20)
  Huali Huang (18)
  Bin Ye (21)
  Lijing Tan (22)
  Jane Jing Liang (23)
  
• 关键词：multi ; swarm particle swarm optimization ; fully learned ; particle swarm optimizer (PSO)
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8794
• 期：1
• 页码：150-157
• 全文大小：224 KB
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  17. Niu, B., Huang, H., Tan, L., Liang, J.J.: Multi-swarm Particle Swarm Optimization with a Center Learning Strategy. In: Tan, Y., Shi, Y., Mo, H. (eds.) ICSI 2013, Part I. LNCS, vol.聽7928, pp. 72鈥?8. Springer, Heidelberg (2013) CrossRef
  
• 作者单位：Ben Niu (18) (19) (20)
  Huali Huang (18)
  Bin Ye (21)
  Lijing Tan (22)
  Jane Jing Liang (23)
  
  18. College of Management, Shenzhen University, Shenzhen, 518060, China
  19. Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China
  20. Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hong Kong
  21. State Grid Anhui Economic Research Institute, Hefei, 230022, China
  22. Business Management School, Shenzhen Institute of Information Technology, Shenzhen, 518172, China
  23. School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
  
• ISSN：1611-3349

文摘

This paper presents a new variant of PSO, called fully learned multi-swarm particle swarm optimization (FLMPSO) for global optimization. In FLMPSO, the whole population is divided into a number of sub-swarms, in which the learning probability is employed to influence the exemplar of each individual and the center position of the best experience found so far by all the sub-swarms is also used to balance exploration and exploitation. Each particle updates its velocity based on its own historical experience or others relying on the learning probability, and the center position is also applied to adjust its flying. The experimental study on a set of six test functions demonstrates that FLMPSO outperform the others in terms of the convergence efficiency and the accuracy.