An improved multi-objective particle swarm optimizer for multi-objective problems

  • Authors:

  • Shang-Jeng Tsai;Tsung-Ying Sun;Chan-Cheng Liu;Sheng-Ta Hsieh;Wun-Ci Wu;Shih-Yuan Chiu

  • Affiliations:

  • Department of Electrical Engineering, National Dong Hwa University, 97401 Hualien, Taiwan, ROC;Department of Electrical Engineering, National Dong Hwa University, 97401 Hualien, Taiwan, ROC;Institution of Information Science, Academia Sinica, Taipei, Taiwan, ROC;Department of Communication Engineering, Oriental Institute of Technology, 220 Taipei County, Taiwan, ROC;Department of Electrical Engineering, National Dong Hwa University, 97401 Hualien, Taiwan, ROC;Department of Electrical Engineering, National Dong Hwa University, 97401 Hualien, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

This paper proposes an improved multi-objective particle swarm optimizer with proportional distribution and jump improved operation, named PDJI-MOPSO, for dealing with multi-objective problems. PDJI-MOPSO maintains diversity of new found non-dominated solutions via proportional distribution, and combines advantages of wide-ranged exploration and extensive exploitations of PSO in the external repository with the jump improved operation to enhance the solution searching abilities of particles. Introduction of cluster and disturbance allows the proposed method to sift through representative non-dominated solutions from the external repository and prevent solutions from falling into local optimum. Experiments were conducted on eight common multi-objective benchmark problems. The results showed that the proposed method operates better in five performance metrics when solving these benchmark problems compared to three other related works.