Particle swarm optimization with selective particle regeneration for data clustering

► We present a novel algorithm developed based on particle swarm optimization. ► The algorithm contains particle regeneration operation. ► We apply this algorithm to data clustering problems. ► The proposed algorithm performs very well in the conducted numerical experiment. This paper presents selec...

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Veröffentlicht in:Expert systems with applications Jg. 38; H. 6; S. 6565 - 6576
Hauptverfasser: Tsai, Chi-Yang, Kao, I-Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.06.2011
Schlagworte:
Algorithms
Clustering
Convergence
Data clustering
Expert systems
K-means algorithm
Mathematical models
Optimization
Particle swarm optimization
Performance evaluation
Regeneration
K-means algorithm
Particle swarm optimization
Data clustering
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
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Zusammenfassung:► We present a novel algorithm developed based on particle swarm optimization. ► The algorithm contains particle regeneration operation. ► We apply this algorithm to data clustering problems. ► The proposed algorithm performs very well in the conducted numerical experiment. This paper presents selective regeneration particle swarm optimization (SRPSO), a novel algorithm developed based on particle swarm optimization (PSO). It contains two new features, unbalanced parameter setting and particle regeneration operation. The unbalanced parameter setting enables fast convergence of the algorithm and the particle regeneration operation allows the search to escape from local optima and explore for better solutions. This algorithm is applied to data clustering problems for performance evaluation and a hybrid algorithm (KSRPSO) of K-means clustering method and SRPSO is developed. In the conducted numerical experiments, SRPSO and KSRPSO are compared to the original PSO algorithm, K-means, as well as, other methods proposed by other studies. The results demonstrate that SRPSO and KSRPSO are efficient, accurate, and robust methods for data clustering problems.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2010.11.082