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Automatically Terminated Particle Swarm Optimization with Principal Component Analysis

Author

Listed:
  • 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)

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.

Suggested Citation

  • Bun Theang Ong & Masao Fukushima, 2015. "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 171-194.
    • Handle: RePEc:wsi:ijitdm:v:14:y:2015:i:01:n:s0219622014500837
    DOI: 10.1142/S0219622014500837
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    Cited by:

    1. Abdel-Rahman Hedar & Wael Deabes & Hesham H. Amin & Majid Almaraashi & Masao Fukushima, 2022. "Global sensing search for nonlinear global optimization," Journal of Global Optimization, Springer, vol. 82(4), pages 753-802, April.

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