An Improved Particle Swarm Optimization With Fuzzy c-means Clustering Algorithm

被引：3
|
作者：
Mei Congli [1 ]
Zhou Dawei [1 ]
机构：
[1] Jiangsu Univ, Dept Automat, Zhenjiang, Peoples R China
来源：
2009 INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS, VOL 2, PROCEEDINGS | 2009年
关键词：
Particle swarm optimization; Fuzzy c-means cluster; Human social behavior;
D O I：
10.1109/IHMSC.2009.154
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper introduces a novel velocity equation of particle swarm optimization algorithm (PSO) based on fuzzy c-means (FCM) cluster analysis of the current particles' position. Besides the previous best location and the global best point, the cluster weighted centers could also be important biological force in the evolution of particles. And local information could be transferred among individuals by a cluster center points. In contrast to standard PSO (SPSO) and PSO with constriction factor (CPSO), the proposed approach is tested with a set of six benchmark functions with different dimensions. Experimental results indicate that this enhancement make the algorithm converge rapidly to good solutions on benchmark functions.
引用
收藏
页码：118 / 122
页数：5
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