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A comparative analysis of PSO, HPSO, and HPSO-TVAC for data clustering

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This article presents a comparative analysis of particle swarm optimisation (PSO), self-organising hierarchical particle swarm optimiser (HPSO) and self-organising hierarchical particle swarm optimiser with time-varying acceleration coefficients (HPSO-TVAC) for data clustering. Through experiments on six well-known benchmarks, we find that the HPSO and the HPSO-TVAC algorithms have better performance than the PSO algorithm in most cases, and all the clustering algorithms using PSO have good performance for large-scale data and high-dimensional data, especially the two algorithms proposed in this article. Furthermore, we have also observed that the convergence of the HPSO and the HPSO-TVAC algorithms are better when using a suitable fitness function.
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Keywords: data clustering; particle swarm optimisation; self-organising hierarchical particle swarm optimiser; time-varying acceleration coefficients

Document Type: Research Article

Affiliations: 1: School of Automation, Southeast University, Nanjing 210096, P.R. China,College of Electrical Engineering, Hohai University, Nanjing 210098, P.R. China 2: College of Electrical Engineering, Hohai University, Nanjing 210098, P.R. China

Publication date: March 1, 2011

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