粒子群优化的两种改进策略  

Two Improvement Strategies for Particle Swarm Optimization

作　　者：窦全胜[1] 周春光[1] 马铭[1] 

机构地区：[1]吉林大学计算机科学与技术学院,长春130012

出　　处：《计算机研究与发展》2005年第5期897-904,共8页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(60175024);教育部"符号计算与知识工程"重点实验室基金项目(60433020)

摘　　要：粒子群优化方法(particleswarmoptimization,PSO)是由Kennedy和Eberhart于1995年提出的,并成功应用于各类优化问题.通过对PSO方法深入分析,把模拟退火和分工两种机制引入到PSO方法中,提出了模拟退火粒子群优化(PSOwSAPSOwithsimulatedannealing)和有分工策略的粒子群优化(PSOwDOWPSOwithdivisionofwork),两种不同改进方法,详细阐述了这两种方法的主要思想.测试结果表明,这两种改进方法能够克服传统PSO方法中的不足,增强了粒子群的优化能力.Particle swarm optimization (PSO) method was proposed by Kennedy and Eberhart in 1995, which can be used to solve a wide array of different optimization problem The PSO idea is inspired by natural concepts such as fish schooling, bird flocking and human social relations Some experimental results show that PSO has greater “global search” ability, but the “local search” ability around the optimum is not very good In order to enhance the “local search” ability of the traditional PSO, two improvement methods for the PSO, that is, PSO with simulated annealing (PSOwSA) and PSO with division of work (PSOwDOW), are introduced by analyzing deeply the traditional PSO Experiments for several benchmark problems show that PSOwSA and PSOwDOW can overcome the fault of traditional PSO and increase the optimization power of the particle swarm

关 键 词：粒子群方法 模拟退火 优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]