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CLC number: TP301.6

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Received: 2008-03-18

Revision Accepted: 2008-06-25

Crosschecked: 2008-12-26

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.4 P.512~519

http://doi.org/10.1631/jzus.A0820196


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


Author(s):  Taher NIKNAM, Babak AMIRI, Javad OLAMAEI, Ali AREFI

Affiliation(s):  Electronic and Electrical Engineering Department, Shiraz University of Technology, Shiraz, Iran; more

Corresponding email(s):   niknam@sutech.ac.ir

Key Words:  Simulated annealing (SA), Data clustering, Hybrid evolutionary optimization algorithm, K-means clustering, Particle swarm optimization (PSO)


Taher NIKNAM, Babak AMIRI, Javad OLAMAEI, Ali AREFI. An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering[J]. Journal of Zhejiang University Science A, 2009, 10(4): 512~519.

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%A Ali AREFI
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Abstract: 
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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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