Cask Theory Based Parameter Optimization for Particle Swarm Optimization

摘要：

　　To avoid the bored try and error method of finding a set of parameters of Particle Swarm Optimization (PSO) and achieve good optimization performance, it is desired to get an adaptive optimization method to search a good set of parameters.A nested optimization method is proposed in this paper and it can be used to search the tuned parameters such as inertia weight ω, acceleration coefficients c1 and c2, and so on.This method considers the cask theory to achieve a better optimization performance.Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.

关键词： PSO Parameter Optimization Try and Error method Nested Optimization method Cask theory

作者: Zenghui Wang Yanxia Sun

作者单位: Department of Electrical and Mining engineering, University of South Africa,Florida 1710, South Afri Department of Electrical engineering, Tshwane University of Technology,Pretoria 0001, South Africa

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: 137-143

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）