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An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering
Journal article   Peer reviewed

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

T. Niknam, B. Amiri, J. Olamaei and A. Arefi
Journal of Zhejiang University-SCIENCE A, Vol.10(4), pp.512-519
2009
DOI: https://doi.org/10.1631/jzus.A0820196
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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.

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52 Record Views
85 Times Cited - Web of Science

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Collaboration types
Domestic collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.61 Artificial Intelligence & Machine Learning
4.61.869 Clustering Algorithms
Web Of Science research areas
Engineering, Multidisciplinary
Physics, Applied
ESI research areas
Engineering
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