Intelligent particle swarm optimization in multi-objective problems

  • Authors:

  • Shinn-Jang Ho;Wen-Yuan Ku;Jun-Wun Jou;Ming-Hao Hung;Shinn-Ying Ho

  • Affiliations:

  • Department of Automation Engineering, National Formosa University, Huwei, Yunlin, Taiwan;Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan;Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan;Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan;Department of Biological Science and Technology and Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan

  • Venue:
  • PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
  • Year:
  • 2006

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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.