Research on PSO with Clusters and Heterogeneity

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

doi:10.3969/j.issn.0372-2112.2012.11.009

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Research on PSO with Clusters and Heterogeneity

更新时间：2025-07-16

- Research on PSO with Clusters and Heterogeneity
- Acta Electronica Sinica Vol. 40, Issue 11, Pages: 2194-2199(2012)
- 作者机构：
  
  武汉理工大学物流工程学院,湖北,武汉,430063
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2012.11.009
  CLC： TP18
- Published：2012
- 稿件说明：
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LI Wen-feng, LIANG Xiao-lei, ZHANG Yu. Research on PSO with Clusters and Heterogeneity[J]. Acta Electronica Sinica, 2012, 40(11): 2194-2199.
DOI：

LI Wen-feng, LIANG Xiao-lei, ZHANG Yu. Research on PSO with Clusters and Heterogeneity[J]. Acta Electronica Sinica, 2012, 40(11): 2194-2199. DOI： 10.3969/j.issn.0372-2112.2012.11.009.

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

-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.

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

-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 ex

ploring 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.

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