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An Improved MOPSO with a Crowding Distance Based External Archive Maintenance Strategy

  • Wei-xing Li
  • Qian Zhou
  • Yu Zhu
  • Feng Pan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

Abstract

For multi-objective optimization algorithms, the maintenance policy of external archive has a great impact on the performance of convergence and solution diversity. Considering the dilemma of large population and external archive, an improved strategy of external archive maintenance based on crowding distance is proposed, which requires less particle numbers and smaller archive size, resulting in the computation cost reduction. Furthermore, the information entropy of gbest is analyzed to emphasize the diversity improvement of non-dominant solutions and well-distribution on the Pareto-optimal front. Numerical experiments of benchmark functions demonstrate the effectiveness and efficiency of proposed multi-objective particle swarm optimization.

Keywords

Multi-objective optimization Particle Swam Optimizer Pareto-optimal front information entropy 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wei-xing Li
    • 1
  • Qian Zhou
    • 1
  • Yu Zhu
    • 2
  • Feng Pan
    • 1
  1. 1.School of AutomationBeijing Institute of Technology (BIT)BeijingP.R. China
  2. 2.China Academy of Space TechnologyChina

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