电子学报 ›› 2012, Vol. 40 ›› Issue (11): 2194-2199.DOI: 10.3969/j.issn.0372-2112.2012.11.009

• 学术论文 • 上一篇    下一篇

具有异构分簇的粒子群优化算法研究

李文锋, 梁晓磊, 张煜   

  1. 武汉理工大学物流工程学院,湖北武汉 430063
  • 收稿日期:2011-12-13 修回日期:2012-03-01 出版日期:2012-11-25 发布日期:2012-11-25
  • 作者简介:李文锋 男,1966年出生,湖南临湘人,教授、博士生导师,IEEE 高级会员,IEEE SMC CSCWD的TC成员.现任武汉理工大学 "物流与机器人技术实验室"主任.主要研究方向:环境感知与协同控制、物流自动化、复杂系统建模与仿真等. E-mail:liwf@whut.edu.cn 梁晓磊 男,1985年出生,山西省长治市人,武汉理工大学物流工程学院博士研究生.研究方向为:群智能优化、物流系统仿真与建模、复杂系统分析. E-mail:liangxiaolei.cn@gmail.com张 煜 男,1974年生,天津人,副教授,武汉理工大学物流工程学院讲师,主要研究方向:系统仿真、优化;虚拟现实;流体传动及控制.
  • 基金资助:
    湖北省自然科学基金重点项目(No.2010CDA022);国家自然科学基金(No.51175394)

Research on PSO with Clusters and Heterogeneity

LI Wen-feng, LIANG Xiao-lei, ZHANG Yu   

  1. School of Logistics Engineering,Wuhan University of Technology,Wuhan,Hubei 430063,China
  • Received:2011-12-13 Revised:2012-03-01 Online:2012-11-25 Published:2012-11-25

摘要: 粒子群优化(Particle Swarm Optimization,PSO)算法在复杂多峰函数可行域空间搜索时极易陷入局部极值点.研究表明改变种群拓扑结构和调整算法参数有助于改善种群的多样性,但是目前研究中少有同时考虑种群全局拓扑结构和局部粒子个体能力.本文提出一种具有异构分簇特性的自适应PSO算法.该算法采用K-均值聚类算法对种群进行动态分簇,形成多异构子群,并采用Ring型拓扑结构进行子群间信息流通.而后采用基于寻解水平评价的粒子自适应参数调整策略进行个体调整.通过实验分析表明该算法能够提高粒子群优化的种群的多样性、粒子活性、搜索能力和收敛性能,同时也降低了算法对参数初值的依赖性.

关键词: 粒子群算法, 自适应, 异构, 聚类, 函数优化

Abstract: Particle Swarm Optimization (PSO) algorithm easily falls into local optimal solution when solving complex multimodal function optimization problem.Researches show that dynamic topology and variable parameters can improve the diversity of swarm to improve the situation.However,the effect of topology and parameters is rarely considered simultaneously.In this paper,a new PSO algorithm based on clustering is proposed.It takes K-means clustering method to divide the swarm into different neighborhoods dynamically.These neighborhoods have different number of particles and are heterogeneous clusters.A Ring-structure is applied to exchange information among clusters.Furthermore,a novel discriminating method is proposed to detect the exploring stage of a cluster.Each particle adjusts its parameters automatically according to the exploring stage of its cluster.The results of experiments show that the operations above can improve diversity and energetic of the particles,increase exploring ability and convergence,and reduce the dependence of initial election of parameters.

Key words: particle swarm optimization, adaptability, heterogeneous, clustering, function optimization

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