A Novel Opposition-Based Multi-objective Differential Evolution Algorithm for Multi-objective Optimization

  • Lei Peng
  • Yuanzhen Wang
  • Guangming Dai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5370)


Multiobjective optimization is of increasing importance in various fields and has very broad applications. The purpose of this paper is to describe a novel multiobjective optimization algorithm–opposition-based multi-objective differential evolution algorithm(OMODE). In the paper, OMODE uses the opposition-based population to generate the initial population of points, The important scaling factor is controlled by self-adaptive method. Performance of OMODE is demonstrated with a set of benchmark test functions and Earth-Mars double transfer problem. The results show that OMODE achieves better performance than other methods.


opposition-Based multi-objective optimization Pareto-optimal solutions differential evolution OMODE double transfer 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Lei Peng
    • 1
    • 2
  • Yuanzhen Wang
    • 1
  • Guangming Dai
    • 2
  1. 1.College of Computer ScienceHuazhong University of Science and TechnologyWuhanChina
  2. 2.School of ComputerChina University of GeosciencesWuhanChina

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