A Hybrid of Differential Evolution and Genetic Algorithm for Constrained Multiobjective Optimization Problems

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


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.


Genetic Algorithm Differential Evolution Hybrid Algorithm Benchmark Function Multiobjective Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Min Zhang
    • 1
  • Huantong Geng
    • 1
  • Wenjian Luo
    • 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 ChinaHefei, AnhuiChina

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