A Hierarchical Particle Swarm Optimization Algorithm Combined with Chaotic Search

摘要：

To overcome disadvantage of Particle Swarm Optimization (PSO) algorithm such as premature convergence and lack of good local search ability, a novel PSO algorithm (HCPSO) is proposed. Based on the hierarchical topology, HCPSO can take a good balance of exploration and exploitation. Combined with chaotic search, HCPSO could explore for better solution around the comprehensive best position whose dimensions learn from the corresponding dimension of other particles personal best position. The region of chaotic search is adaptively adjusted according to the distance between the particles personal best position and the comprehensive best position. The simulation results of a set of benchmark functions and the comparison with other variants of PSO algorithms verify the efficiency of the HCPSO algorithm.

关键词： Particle Swarm Optimization Chaotic search Hierarchy Subpopulation

作者: Weibo Wang Quanyuan Feng

作者单位: School of Electric Information, Xihua University School of Information Science & Technology, Southwe School of Information Science & Technology Southwest Jiaotong University Chengdu, China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering(计算机与通信技术在农业工程国际会议 CCTAE 2010)

会议地点: 成都

会议语种:英文

页码: 435-438

在线出版日期: 2010-06-12（万方平台首次上网日期，不代表论文的发表时间）