期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2008
卷号：8
期号：1
页码：203-212
出版社：International Journal of Computer Science and Network Security
摘要：In this paper, well-known PSO algorithms reported in the literature for solving continuous function optimization problems were comparatively evaluated by considering real world data clustering problems. Data clustering problems are solved, by considering three performance clustering metrics such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The results obtained by the PSO variants were compared with the basic PSO algorithm, Genetic algorithm and Differential evolution algorithms. A detailed performance analysis has been carried out to study the convergence behavior of the PSO algorithms using run length distribution.
关键词：Data clustering; Particle Swarm Optimization; Genetic Algorithm; Differential Evolution Algorithm; Trace Within criteria; Variance Ratio Criteria; Marriott Criteria.
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