一种基于协同进化的随机微粒群算法  

A Stochastic Particle Swarm Optimization Based the Cooperative Evolution

作　　者：王建丽[1] 夏桂梅[1] 王希云[1] 

机构地区：[1]太原科技大学应用科学学院,太原030024

出　　处：《太原科技大学学报》2010年第3期185-188,共4页Journal of Taiyuan University of Science and Technology

摘　　要：在多种群协同进化和随机微粒群算法基础上,提出了一种改进的多种群随机微粒群算法,将各个子种群度独立的按照随机微粒群去进化,周期性的更新共享信息,共同寻求最优解。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的分析和比较。实验表明:合理调整更新周期能提高算法的收敛性。A modified cooperative stochastic particle swarm optimization（CSPSO） is proposed based on the analysis of the SPSO and the cooperative evolutionary PSO with multi-populations.The whole group is divided into several sub-groups.Every subgroup evolves independently and updates sharing information periodically to cooperate and converge at the global optimum.Two different updating strategies are adopted and the two methods are analyzed in detail.The experimental results show that the proper communication period can greatly improve the convergence.

关 键 词：随机微粒群算法 多种群 协同进化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]