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Constrained Multi-objective Particle Swarm Optimization Algorithm

  • Yue-lin Gao
  • Min Qu
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.

Keywords

particle swarm optimization algorithm constrained multi-objective optimization Pareto neighborhood crossover operation penalty function method 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yue-lin Gao
    • 1
  • Min Qu
    • 1
  1. 1.Institute of Information & System ScienceNorth Ethnic UniversityYinchuanChina

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