• DocumentCode
    2324759
  • Title

    Alternative hyper-heuristic strategies for multi-method global optimization

  • Author

    Grobler, Jacomine ; Engelbrecht, Andries P. ; Kendall, Graham ; Yadavalli, V.S.S.

  • Author_Institution
    Dept. of Ind. & Syst. Eng., Univ. of Pretoria, Pretoria, South Africa
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    The purpose of this paper is to investigate the use of meta-heuristics as low-level heuristics in a hyper-heuristic framework. A novel multi-method hyper-heuristic algorithm which makes use of a number of common meta-heuristics is presented. Algorithm performance is evaluated on a diverse set of real parameter benchmark problems and meaningful conclusions are drawn with respect to the selection of alternative low-level heuristics and the acceptance of the obtained solutions within the proposed multi-method meta-heuristic approach.
  • Keywords
    heuristic programming; optimisation; alternative low-level heuristics; common metaheuristics; multimethod global optimization; multimethod hyper-heuristic algorithm; Algorithm design and analysis; Benchmark testing; Equations; Evolutionary computation; Heuristic algorithms; Optimization; Software algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2010 IEEE Congress on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4244-6909-3
  • Type

    conf

  • DOI
    10.1109/CEC.2010.5585980
  • Filename
    5585980