Multi-swarm Particle Swarm Optimization with a Center Learning Strategy

摘要：

　　This paper proposes a new variant of particle swarm optimizers, called multi-swarm particle swarm optimization with a center learning strategy (MPSOCL).MPSOCL uses a center learning probability to select the center position or the prior best position found so far as the exemplar within each swarm.In MPSOCL, Each particle updates its velocity according to the experience of the best performing particle of its partner swarm and its own swarm or the center position of its own swarm.Experiments are conducted on five test functions to compare with some variants of the PSO.Comparative results on five benchmark functions demonstrate that MPSOCL achieves better performances in both the optimum achieved and convergence performance than other algorithms generally.

关键词： multi-swarm particle swarm optimization center learning strategy particle swarm optimizer (PSO)

作者: Ben Niu Huali Huang Lijing Tan Jane Jing Liang

作者单位: College of Management, Shenzhen University,Shenzhen 518060, China;Department of Industrial and Syste College of Management, Shenzhen University,Shenzhen 518060, China Management School, Jinan University, Guangzhou 510632, China School of Electrical Engineering, Zhengzhou University, Zhengzhou, China

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 72-78

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）