多种群协同进化的微粒群算法  

Cooperative evolutionary particle swarm optimization algorithm with multi-populations

作　　者：王元元[1] 曾建潮[1] 谭瑛[1] 

机构地区：[1]太原科技大学系统仿真与计算机应用研究所,山西太原030024

出　　处：《计算机工程与设计》2007年第15期3661-3664,共4页Computer Engineering and Design

基　　金：教育部重点科研基金项目(204018)

摘　　要：在对标准微粒群算法分析的基础上,提出了一种多种群协同进化的微粒群算法。它将整个种群分解为多个子种群,各子种群独立进化,周期性地更新共享信息。其中采用了两种不同的更新策略,并对这两种不同的方法进行详细地分析和比较。实验结果表明,合适地更新周期能提高算法的收敛性和最优性。A new particle swarm optimizer （PSO）, called the cooperative evolutionary PSO with multi-populations, is presented based on the analysis of the standard PSO. The whole group is divided into several sub-groups. Every subgroup evolved independently and updated sharing information periodically. Two different updating strategies are adopted and the two methods are analyzed in detail. The experimental results show that the properly communication period can greatly improve the convergence.

关 键 词：微粒群算法 多种群 周期性 协同进化 更新策略 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]