• DocumentCode
    2909157
  • Title

    Dynamic Problems and Nature Inspired Meta-Heuristics

  • Author

    Hendtlass, Tim ; Moser, Irene ; Randall, Marcus

  • Author_Institution
    Swinburne University, Australia
  • fYear
    2006
  • fDate
    Dec. 2006
  • Firstpage
    111
  • Lastpage
    111
  • Abstract
    Biological systems are, by their very nature, adaptive. However, the meta-heuristic search algorithms inspired by them have mainly been applied to static problems (i.e., problems that do not change while they are being solved). Recently, a greater body of work has been completed on the newer meta-heuristics, particularly ant colony optimisation, particle swarm optimisation and extremal optimisation. This survey paper examines representative works and methodologies of these techniques on this class of problems. Beyond this we outline the limitations of these methods.
  • Keywords
    Ant colony optimization; Biological systems; Bonding; Cities and towns; Communications technology; Evolutionary computation; Genetic algorithms; Information technology; Particle swarm optimization; Testing; ant colony optimisation; evoluationary and adaptive dynamics; extremal optimisation.; particle swarm optimisation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    e-Science and Grid Computing, 2006. e-Science '06. Second IEEE International Conference on
  • Conference_Location
    Amsterdam, The Netherlands
  • Print_ISBN
    0-7695-2734-5
  • Type

    conf

  • DOI
    10.1109/E-SCIENCE.2006.261195
  • Filename
    4031084