An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

Niknam, Taher, Amiri, Babak, Olamaei, Javad, & Arefi, Ali (2009) An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering. Zhejiang University Journal Science A : Applied Physics & Engineering, 10(4), pp. 512-519.

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The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

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59 citations in Scopus
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42 citations in Web of Science®

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ID Code: 68732
Item Type: Journal Article
Refereed: Yes
Keywords: Simulated annealing (SA), Data clustering, Hybrid evolutionary optimization algorithm, K-means clustering, Particle swarm optimization (PSO)
DOI: 10.1631/jzus.A0820196
ISSN: 1673-565X
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2009 Springer & Zhejiang University Press
Deposited On: 19 Mar 2014 03:37
Last Modified: 04 Dec 2015 02:37

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