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Enhancing Global Search Ability of Quantum-Behaved Particle Swarm Optimization by Maintaining Diversity of the Swarm

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Rough Sets and Current Trends in Computing (RSCTC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4259))

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Abstract

Premature convergence, the major problem that confronts evolu-tionary algorithms, is also encountered with the Particle Swarm Optimization (PSO) algorithm. Quantum-behaved Particle Swarm (QPSO), a novel variant of PSO, is a global-convergence-guaranteed algorithm and has a better search ability than the original PSO. But like PSO and other evolutionary optimization techniques, premature in QPSO is also inevitable. The reason for premature convergence in PSO or QPSO is that the information flow between particles makes the diversity of the population decline rapidly. In this paper, we propose Diversity-Maintained QPSO (DMQPSO). Before describing the new method, we first introduce the origin and development of PSO and QPSO. DMQPSO, along with the PSO and QPSO, is tested on several benchmark functions for performance comparison. The experiment results show that the DMQPSO outperforms the PSO and QPSO in many cases.

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Authors and Affiliations

  1. Center of Computational Intelligence and High Performance Computing, School of Information Technology, Southern Yangtze University, No. 1800, Lihudadao Road, Wuxi Jiangsu, 214122, China

    Jun Sun, Wenbo Xu & Wei Fang

Authors
  1. Jun Sun
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  2. Wenbo Xu
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  3. Wei Fang
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Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco

  2. Graduate School of Engineering, Department of Electrical Engineering and Computer Sciences, University of Hyogo, 2167 Shosha, 671-2280,, Himeji, Hyogo, Japan

    Yutaka Hata

  3. Department of Medical Informatics, Faculty of Medicine, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shoji Hirano

  4. Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3, Machikaneyama, Toyonaka, 560-8531, Osaka, Japan

    Masahiro Inuiguchi

  5. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto

  6. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Hung Son Nguyen

  7. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

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© 2006 Springer-Verlag Berlin Heidelberg

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Sun, J., Xu, W., Fang, W. (2006). Enhancing Global Search Ability of Quantum-Behaved Particle Swarm Optimization by Maintaining Diversity of the Swarm. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_76

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  • DOI: https://doi.org/10.1007/11908029_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47693-1

  • Online ISBN: 978-3-540-49842-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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