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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particle swarm optimisation;
self-organising hierarchical particle swarm optimiser;
time-varying acceleration coefficients
Document Type: Research Article
School of Automation, Southeast University, Nanjing 210096, P.R. China,College of Electrical Engineering, Hohai University, Nanjing 210098, P.R. China
College of Electrical Engineering, Hohai University, Nanjing 210098, P.R. China
Publication date: March 1, 2011
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