Automatically Terminated Particle Swarm Optimization with Principal Component Analysis

Bun Theang Ong () and Masao Fukushima ()
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Bun Theang Ong: Information Services Platform Laboratory, Universal Communication Research Institute, National Institute of Information and Communications Technology, 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan
Masao Fukushima: Department of System and Mathematical Sciences, Faculty of Science and Engineering, Nanzan University, Nagoya 466-8673, Japan

International Journal of Information Technology & Decision Making (IJITDM), 2015, vol. 14, issue 01, 171-194

Abstract: A hybrid Particle Swarm Optimization (PSO) that features an automatic termination and better search efficiency than classical PSO is presented. The proposed method is combined with the so-called "Gene Matrix" to provide the search with a self-check in order to determine a proper termination instant. Its convergence speed and reliability are also increased by the implementation of the Principal Component Analysis (PCA) technique and the hybridization with a local search method. The proposed algorithm is denominated as "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis" (AT-PSO-PCA). The computational experiments demonstrate the effectiveness of the automatic termination criteria and show that AT-PSO-PCA enhances the convergence speed, accuracy and reliability of the PSO paradigm. Furthermore, comparisons with state-of-the-art evolutionary algorithms (EA) yield competitive results even under the automatically detected termination instant.

Keywords: Global optimization; particle swarm optimization; termination criteria; gene matrix; principal component analysis (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622014500837

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