基于密集距离的多目标粒子群优化算法  

Crowding-measure-based multi-objective particle swarm optimization

作　　者：郑友莲[1] 樊俊青[2] 

机构地区：[1]湖北大学数学与计算机科学学院,湖北武汉430062 [2]中国地质大学计算机学院,湖北武汉430074

出　　处：《湖北大学学报（自然科学版）》2008年第2期141-144,191,共5页Journal of Hubei University：Natural Science

基　　金：湖北省自然科学基金(2007ABA044)资助

摘　　要：设计出基于密集距离的多目标粒子群优化算法(CMPSO),该算法根据密集距离大小按轮盘赌方式为每个粒子从外部档案选取全局最好位置并采用基于密集距离的方法对外部档案进行维护.将算法应用于3个复杂的测试实例,并与强度Pareto进化算法2等算法进行比较,计算结果表明CMPSO具有良好的连续优化能力.Crowding-measure-based multi-objective particle swarm optimization （CMPSO） is proposed, in which crowding measure is used to maintain external archive and select global best position for each particle from archive and some archive members are chosen to perform mutation operator. The proposed algorithm has been applied to three complex test instances and invalidated by the comparison among CMPSO, strength Pareto evolutionary algorithm, etc. The computational results demonstrate that CMPSO has promising advantage in multi-objective optimization.

关 键 词：密集距离 多目标优化 粒子群优化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]