一种新的求解多目标问题的分组粒子群优化算法  

A New Divisional PSO Algorithm for Solving Multi-objective Optimization

作　　者：朱世娟[1] 吴海峰[1] 程一飞[1] 

机构地区：[1]安庆师范学院计算机与信息学院,安徽安庆246133

出　　处：《安庆师范学院学报（自然科学版）》2014年第2期28-32,52,共6页Journal of Anqing Teachers College(Natural Science Edition)

基　　金：安徽省高等学校省级自然科学研究项目(KJ2012B082);安庆师范学院青年科研项目(KJ201217)资助

摘　　要：用粒子群优化算法求解多目标问题容易陷入局部最优,为此本文提出了一种分组粒子群多目标优化算法。该算法将决策空间分成Q个子空间,每个子空间随机的分配N个粒子,这Q个粒子群分别在各自的空间进行独立搜索。为保证每个种群的搜索多样性和遍历性,用混沌序列对各组粒子位置进行初始化,同时对各组进行基于聚集距离的粒子择优进化。由典型多目标函数的优化实验结果表明,经过适当的分组,该算法能迅速逼近非劣最优解集,效果令人满意。In order to solve the problem that it is easily plunged into local optima to use particle swarm optimization （ PSO） al-gorithm for multi-objective problem, this paper proposes a divisional PSO algorithm, named MODPSO.This algorithm divide func-tion domain into Q subspaces, each subspace will be randomly allocated N particles.These Q particle swarm search independently in their own space respectively.In order to guarantee each species＆#39;diversity and ergodicity of searching, chaotic sequence and crowding distance is used to initiate individual position and select the best individual .By proper dividing, experimental results on several typical multi-objective function show that the algorithm can rapidly find the Pareto optimal which is quite satisfactory .

关 键 词：粒子群优化 分组 多目标优化 非劣最优解 

分 类 号：O231.9[理学—运筹学与控制论]