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Infeasible Elitists and Stochastic Ranking Selection in Constrained Evolutionary Multi-objective Optimization

  • Huantong Geng
  • Min Zhang
  • Linfeng Huang
  • Xufa Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

To handle the constrained multi-objective evolutionary optimization problems, the authors firstly analyze Deb’s constrained-domination principle (DCDP) and point out that it more likely stick into local optimum on these problems with two or more disconnected feasible regions. Secondly, to handle constraints in multi-objective optimization problems (MOPs), a new constraint handling strategy is proposed, which keeps infeasible elitists to act as bridges connecting disconnected feasible regions besides feasible ones during optimization and adopts stochastic ranking to balance objectives and constraints in each generation. Finally, this strategy is applied to NSGA-II, and then is compared with DCDP on six benchmark constrained MOPs. Our results demonstrate that distribution and stability of the solutions are distinctly improved on the problems with two or more disconnected feasible regions, such as CTP6.

Index Terms

Constraint multi-objective optimization infeasible elitists stochastic ranking 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huantong Geng
    • 1
  • Min Zhang
    • 1
  • Linfeng Huang
    • 1
  • Xufa Wang
    • 1
  1. 1.Nature Inspired Computation and Applications Laboratory, Department of Computer, Science and TechnologyUniversity of Science and Technology of China 

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