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Intelligent Particle Swarm Optimization in Multi-objective Problems

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Advances in Knowledge Discovery and Data Mining (PAKDD 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3918))

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Abstract

In this paper, we proposes a novel intelligent multi-objective particle swarm optimization (IMOPSO) to solve multi-objective optimization problems. High performance of IMOPSO mainly arises from two parts: one is using generalized Pareto-based scale-independent fitness function (GPSISF) can efficiently given all candidate solutions a score, and then decided candidate solutions level. The other one is replacing the conventional particle move process of PSO with an intelligent move mechanism (IMM) based on orthogonal experimental design to enhance the search ability. IMM can evenly sample and analyze from the best experience of an individual particle and group particles by using a systematic reasoning method, and then efficiently generate a good candidate solution for the next move of the particle. Some benchmark functions are used to evaluate the performance of IMOPSO, and compared with some existing multi-objective evolution algorithms. According to experimental results and analysis, they show that IMOPSO performs well.

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Author information

Authors and Affiliations

  1. Department of Automation Engineering, National Formosa University, Huwei, Yunlin, 632, Taiwan

    Shinn-Jang Ho

  2. Department of Information Engineering and Computer Science, Feng Chia University, Taichung, 407, Taiwan

    Wen-Yuan Ku, Jun-Wun Jou & Ming-Hao Hung

  3. Department of Biological Science and Technology and Institute of Bioinformatics, National Chiao Tung University, Hsinchu, 300, Taiwan

    Shinn-Ying Ho

Authors
  1. Shinn-Jang Ho
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  2. Wen-Yuan Ku
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  3. Jun-Wun Jou
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  4. Ming-Hao Hung
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  5. Shinn-Ying Ho
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Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore

    Wee-Keong Ng

  2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8505, Tokyo, Japan

    Masaru Kitsuregawa

  3. School of Computer Science and Technology, Heilongjiang University, China

    Jianzhong Li

  4. School of Computer Engineering, Nanyang Technological University, 639798, Singapore, Singapore

    Kuiyu Chang

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© 2006 Springer-Verlag Berlin Heidelberg

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Ho, SJ., Ku, WY., Jou, JW., Hung, MH., Ho, SY. (2006). Intelligent Particle Swarm Optimization in Multi-objective Problems. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_92

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  • DOI: https://doi.org/10.1007/11731139_92

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33206-0

  • Online ISBN: 978-3-540-33207-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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