• DocumentCode
    1874713
  • Title

    Kudu herd optimization

  • Author

    Boelaert, Julien

  • Author_Institution
    CES, Univ. Paris 1 Pantheon-Sorbonne, Paris, France
  • fYear
    2012
  • fDate
    6-8 Sept. 2012
  • Firstpage
    108
  • Lastpage
    113
  • Abstract
    This work proposes a new and simple algorithm for unconstrained numeric optimization over continuous spaces. A population of candidate solutions styled as a herd of kudus performs a succession of jumps through the search space in order to find the best solution (the kudu is a species of antelope). The logic of this algorithm is quite different from that of most population-based algorithms, as the individual solutions are moved together in a herd-like fashion. Performance comparisons are conducted with the Artificial Bee Colony, Differential Evolution, the Genetic Algorithm and Particle Swarm Optimization on benchmark functions. The kudu herd seems to perform well in the early stages and on high-dimensional problems.
  • Keywords
    genetic algorithms; particle swarm optimisation; search problems; artificial bee colony; candidate solutions; continuous spaces; differential evolution; genetic algorithm; high-dimensional problems; kudu herd optimization; particle swarm optimization; population-based algorithms; search space; unconstrained numeric optimization; Lead; Optimization; Particle swarm optimization; Sociology; Standards; Statistics; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems (IS), 2012 6th IEEE International Conference
  • Conference_Location
    Sofia
  • Print_ISBN
    978-1-4673-2276-8
  • Type

    conf

  • DOI
    10.1109/IS.2012.6335199
  • Filename
    6335199