Infeasible elitists and stochastic ranking selection in constrained evolutionary multi-objective optimization

  • Authors:

  • Huantong Geng;Min Zhang;Linfeng Huang;Xufa Wang

  • Affiliations:

  • Nature Inspired Computation and Applications Laboratory, Department of Computer, Science and Technology, University of Science and Technology of China;Nature Inspired Computation and Applications Laboratory, Department of Computer, Science and Technology, University of Science and Technology of China;Nature Inspired Computation and Applications Laboratory, Department of Computer, Science and Technology, University of Science and Technology of China;Nature Inspired Computation and Applications Laboratory, Department of Computer, Science and Technology, University of Science and Technology of China

  • Venue:
  • SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
  • Year:
  • 2006

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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.