| Information Science |
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| An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering |
| Taher NIKNAM, Babak AMIRI, Javad OLAMAEI, Ali AREFI |
| Electronic and Electrical Engineering Department, Shiraz University of Technology, Shiraz, Iran; Information Technology Department, Islamic Azad University-Fars Science and Research Branch, Shiraz, Iran; Technical Engineering Department, Islamic Azad University-South Tehran Branch, Tehran, Iran; Power Engineering Group, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran |
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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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Received: 18 March 2008
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