A hybrid of differential evolution and genetic algorithm for constrained multiobjective optimization problems

  • Authors:

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

  • Affiliations:

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

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

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Abstract

Two novel schemes of selecting the current best solutions for multiobjective differential evolution are proposed in this paper. Based on the search biases strategy suggested by Runarsson and Yao, a hybrid of multiobjective differential evolution and genetic algorithm with (N+N) framework for constrained MOPs is given. And then the hybrid algorithm adopting the two schemes respectively is compared with the constrained NSGA-II on 4 benchmark functions constructed by Deb. The experimental results show that the hybrid algorithm has better performance, especially in the distribution of non-dominated set.