• DocumentCode
    2462665
  • Title

    Solving Problems with Hidden Dynamics - Comparison of Extremal Optimisation and Ant Colony System

  • Author

    Moser, I. ; Hendtlass, T.

  • Author_Institution
    Swinburne Univ., Melbourne
  • fYear
    2006
  • fDate
    16-21 July 2006
  • Firstpage
    1248
  • Lastpage
    1255
  • Abstract
    Solving dynamic combinatorial problems poses a particular challenge to optimisation algorithms. Optimising a dynamic problem that does not notify the solver when a change has been made is very difficult for most well-known algorithms. Extremal optimisation is a recent addition to the group of biologically inspired optimisation algorithms, while ant colony system has been used to solve a large variety of problem types in static and dynamic contexts. Both algorithms seem well suited to solving problems with hidden dynamics. We present a performance comparison of the two algorithms and endeavour to highlight particular strengths and weaknesses observed with different types of dynamic problem changes.
  • Keywords
    combinatorial mathematics; optimisation; ant colony system; biologically inspired optimisation algorithms; dynamic combinatorial problems; extremal optimisation; hidden dynamics; Ant colony optimization; Communications technology; Design engineering; Design optimization; Genetic mutations; Glass; Heat engines; Partitioning algorithms; Physics; Stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9487-9
  • Type

    conf

  • DOI
    10.1109/CEC.2006.1688452
  • Filename
    1688452