Particle swarm optimization with preference order ranking for multi-objective optimization

  • Authors:

  • Yujia Wang;Yupu Yang

  • Affiliations:

  • Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China;Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2009

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Abstract

A new optimality criterion based on preference order (PO) scheme is used to identify the best compromise in multi-objective particle swarm optimization (MOPSO). This scheme is more efficient than Pareto ranking scheme, especially when the number of objectives is very large. Meanwhile, a novel updating formula for the particle's velocity is introduced to improve the search ability of the algorithm. The proposed algorithm has been compared with NSGA-II and other two MOPSO algorithms. The experimental results indicate that the proposed approach is effective on the highly complex multi-objective optimization problems.