• DocumentCode
    2837279
  • Title

    Artificial Bee Colony Programming Made Faster

  • Author

    XingBao Liu ; Zixing Cai

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China
  • Volume
    4
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    154
  • Lastpage
    158
  • Abstract
    The artificial bee colony (ABC) algorithm is a stochastic, population-based evolutionary method that can be applied to a wide range of problems, including global optimization. The paper proposes a variation on the traditional ABC algorithm, called the artificial bee colony programming, or ABCP, employing randomized distribution, bit hyper-mutation and a novel crossover operator to significantly improve the performance of the original algorithm. Application of the new ABC algorithm on fifteen benchmark optimization problems shows a marked improvement in performance over the traditional ABC.
  • Keywords
    evolutionary computation; artificial bee colony programming; crossover operator; global optimization; population-based evolutionary method; randomized distribution; Artificial intelligence; Educational institutions; Educational programs; Educational technology; Genetic mutations; Information science; Optimization methods; Programming profession; Stochastic processes; Tin; artificial immune systems; clone selection algorithm; optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.377
  • Filename
    5364518