• DocumentCode
    2258223
  • Title

    A New Multi-objective Differential Evolution Algorithm

  • Author

    Gao, Yuelin ; Zhou, Jingke ; Jia, Songwei

  • Author_Institution
    Inst. of Inf. & Syst. Sci., North Nat. Univ., China
  • fYear
    2010
  • fDate
    11-14 Dec. 2010
  • Firstpage
    170
  • Lastpage
    173
  • Abstract
    A new multi-objective differential evolution algorithm is proposed. A dual elitist selection strategy based on Individual Pareto Rank and Individual Density is employed in the proposed new algorithm. It also remains the characteristic of keeping elitists. The corresponding effects comparisons of new algorithm with other classic multi-objective evolutionary algorithms show that new algorithm require initial population small in size, fewer iterations, and output more optimal solutions. It can improve the diversity metric significantly while ensuring satisfactory convergence metric.
  • Keywords
    Pareto optimisation; convergence; evolutionary computation; iterative methods; convergence metric; diversity metric; dual elitist selection strategy; individual Pareto rank; individual density; iteration; keeping elitists; multiobjective differential evolution algorithm; differential evolution; dual elitist selection strategy; multi-objective optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Security (CIS), 2010 International Conference on
  • Conference_Location
    Nanning
  • Print_ISBN
    978-1-4244-9114-8
  • Electronic_ISBN
    978-0-7695-4297-3
  • Type

    conf

  • DOI
    10.1109/CIS.2010.44
  • Filename
    5696256