• DocumentCode
    3217482
  • Title

    Solving multiple-objective optimization problems using GISMOO algorithm

  • Author

    Zinflou, Arnaud ; Gagné, Caroline ; Gravel, Marc

  • Author_Institution
    Dept. d´´Inf. et de Math., Univ. du Quebec a Chicoutimi, Chicoutimi, QC, Canada
  • fYear
    2009
  • fDate
    9-11 Dec. 2009
  • Firstpage
    239
  • Lastpage
    244
  • Abstract
    In this paper, we proposed a new Pareto generic algorithm which hybridizes genetic algorithm and artificial immune systems. Numerical experiments were made using a classical benchmark in multiple-objective optimization (MOKP). Results show that our approach is able to obtain better performance than two state of the art approaches: NSGAII and PMSMO.
  • Keywords
    Pareto optimisation; artificial immune systems; genetic algorithms; knapsack problems; GISMOO algorithm; NSGAII; PMSMO; Pareto generic algorithm; artificial immune systems; genetic algorithm hybridization; knapsack problem; multiple-objective optimization problems; Artificial immune systems; Calibration; Cloning; Constraint optimization; Density measurement; Design optimization; Evolutionary computation; Genetic algorithms; Iterative algorithms; Pareto optimization; MOKP; Pareto front; artificial immune systems; evolutionnary algorithm; hybridization; multiple-objective optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on
  • Conference_Location
    Coimbatore
  • Print_ISBN
    978-1-4244-5053-4
  • Type

    conf

  • DOI
    10.1109/NABIC.2009.5393704
  • Filename
    5393704
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3217482