文献类型：期刊文献

中文题名：三群协同粒子群优化算法

英文题名：Three Swarms Cooperative Particle Swarm Optimization

作者：刘卓倩[1];顾幸生[1];陈国初[1]

机构：[1]华东理工大学自动化研究所,上海200237

年份：2006

卷号：32

期号：7

起止页码：754

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;EI(收录号：20063410084323);Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：粒子群算法;协同;优化

外文关键词：particle swarm optimization ; cooperative ; optimization

摘要：针对基本粒子群优化算法易陷入局部极值点、搜索精度低等缺点,提出了一种三群协同粒子群优化算法(TSC-PSO)。搜索时,如果全局极值连续若干代没有改善,粒子未找到全局最优点,就任选某个优群,将其群内粒子和差群粒子交换。仿真结果显示,对一些经典多峰值函数、非凸病态函数,TSC-PSO增强了全局搜索能力,具有比基本PSO更好的优化性能。
In order to overcome the drawback of basic PSO,such as being subject to falling into local optimization and being poor in performance of precision, an improved PSO algorithm, three swarms cooperative particle swarm optimization （TSC-PSO）, is proposed. Regarding to several special multimodal functions and singular non-convex functions, the results of simulation show that the TSC-PSO can strengthen the global searching ability and have better optimization performance than basic PSO.