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A Co-evolutionary Particle Swarm Optimization-Based Method for Multiobjective Optimization

  • Hong-yun Meng
  • Xiao-hua Zhang
  • San-yang Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

A co-evolutionary particle swarm optimization is proposed for multiobjective optimization (MO), in which co-evolutionary operator, competition mutation operator and new selection mechanism are designed for MO problem to guide the whole evolutionary process. By the sharing and exchange of information among particles, it can not only shrink the searching region but maintain the diversity of the population, avoid getting trapped in local optima which is proved to be effective in providing an appropriate selection pressure to propel the population towards the Pareto-optimal Front. Finally, the proposed algorithm is evaluated by the proposed quality measures and metrics in literatures.

Keywords

Particle Swarm Optimization Extreme Point Pareto Front Multiobjective Optimization Pareto Optimal Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hong-yun Meng
    • 1
  • Xiao-hua Zhang
    • 2
  • San-yang Liu
    • 1
  1. 1.Dept.of Applied Math.XiDian UniversityXianChina
  2. 2.Institute of Intelligent Information ProcessingXiDian UniversityXianChina

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