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Comparative evaluation of Particle Swarm Optimization Algorithms for Data Clustering using real world data sets


R.Karthi, S.Arumugam, K. Rameshkumar


Vol. 8  No. 1  pp. 203-212


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.


Data clustering, Particle Swarm Optimization, Genetic Algorithm, Differential Evolution Algorithm, Trace Within criteria, Variance Ratio Criteria, Marriott Criteria.