粒子群优化技术的研究与应用进展  

Overview Applications and Research on Particle Swarm Optimization Algorithm

作　　者：苏守宝[1] 汪继文[1] 方杰[1] 

机构地区：[1]安徽大学计算机科学与技术学院

出　　处：《计算机技术与发展》2007年第5期249-252,F0003,共5页Computer Technology and Development

基　　金：安徽高校省级自然科学研究重点资助项目(KJ2007A087);安徽高校省级自然科学研究资助项目(2006KJ046B;2005KJ095)

摘　　要：粒子群优化(PSO)算法是一种新兴的基于群智能搜索的优化技术,它是通过粒子追随个体最优解和群体最优解来完成优化,且算法简单、易实现、参数少,具有较强的全局优化能力,可有效应用于科学与工程实践中。文中综述了PSO各种改进技术、研究热点问题及其应用进展情况并指出了PSO的发展趋势及未来研究方向。Particle swarm optimization （PSO） is a new optimization technique based on swarm intelligent search that completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. PSO with better global optimization capability can be easily implemented with simple algorithm and few parameters need. It has been successfully applied in science and engineering practice. In this paper, the basic principles of PSO and its various improved algorithms are introduced at length, research hot issues and the application fields are analyzed and some future research directions about PSO are discussed.

关 键 词：粒子群优化 群智能 演化计算 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP301.6[自动化与计算机技术—控制科学与工程]