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基于聚类分析的随机微粒群算法
引用本文:郝武伟,曾建潮.基于聚类分析的随机微粒群算法[J].计算机工程与应用,2010,46(8):40-44.
作者姓名:郝武伟  曾建潮
作者单位:1. 山西交通职业技术学院,经济管理系,太原,030031
2. 太原科技大学,系统仿真与计算机应用研究所,太原,030024
基金项目:国家自然科学基金Grant No.60674104~~
摘    要:在对一种保证全局收敛的微粒群算法——随机PSO算法(SPSO)进行分析的基础上,提出了一种基于聚类分析的随机微粒群算法(CSPSO)。CSPSO算法保证了种群的多样性,使微粒能够有效地进行全局搜索。并证明了它依概率收敛于全局最优解。最后以典型的复杂基准优化问题进行了仿真实验,验证了CSPSO的有效性。

关 键 词:随机微粒群算法  聚类分析  全局优化  收敛性
收稿时间:2009-3-12
修稿时间:2009-5-11  

Stochastic particle swarm optimization algorithm based on cluster analysis
HAO Wu-wei,ZENG Jian-chao.Stochastic particle swarm optimization algorithm based on cluster analysis[J].Computer Engineering and Applications,2010,46(8):40-44.
Authors:HAO Wu-wei  ZENG Jian-chao
Affiliation:HAO Wu-wei1,ZENG Jian-chao21.Department of Economics , Management,Shanxi Communications Polytechnic,Taiyuan 030031,China 2.Division of System Simulation , Computer Application,Taiyuan University of Science , Technology,Taiyuan 030024,China
Abstract:A new Stochastic Particle Swarm Optimization algorithm based on Cluster analysis (CSPSO) is proposed based on the analysis of Stochastic Particle Swarm Optimization algorithm (SPSO) that guarantees global convergence.The CSPSO is guaranteed that the particles are diversiform,and can make particles explore the global optimization more efficiently.The CSPSO is guaranteed to converge to the global optimization solution with probability one.Finally,several complex examples are simulated to show that CSPSO is mo...
Keywords:Stochastic Panicle Swarm Optimization(SPSO)  cluster analysis  global optimization  convergence
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