• DocumentCode
    3265582
  • Title

    A Modified Differential Evolution Multi-objective Optimization Method

  • Author

    Zhang, L.B. ; Xu, X.L. ; Sun, C.T. ; Zhou, C.G.

  • Author_Institution
    Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
  • Volume
    1
  • fYear
    2009
  • fDate
    6-7 June 2009
  • Firstpage
    511
  • Lastpage
    514
  • Abstract
    Differential evolution (DE) is population based, direct search, global optimization new algorithm. DE is simple and efficient, so solving multi-objective optimization problems (MOP) using DE has become a new hot research topic. It was found in the research process that there are deteriorations in the process of solving MOP based on DE. Due to deterioration occurs, thus the convergence can no longer be guaranteed for the algorithm, and the efficiency is reduced for solving. This paper proposed resolve methods correspond to these two deteriorations. Numerical experiments show the effectiveness of the proposed algorithm.
  • Keywords
    Pareto optimisation; evolutionary computation; Pareto dominance; modified differential evolution method; multiobjective optimization problem; Computational intelligence; Computer science; Convergence; Degradation; Educational institutions; Evolutionary computation; Genetic mutations; Optimization methods; Pareto optimization; Sun; Pareto dominance; differential evolution; mulit-objective optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3645-3
  • Type

    conf

  • DOI
    10.1109/CINC.2009.145
  • Filename
    5231073