Research on convergence of ACO subset algorithms

• Wenyu Chen ^[1] ; Wangyang Bian ^[1] ; Ru Zeng ^[1]
  1. [1] University of Electronic Science and Technology of China

     University of Electronic Science and Technology of China

     China

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 32, Nº 2 (Special Issue: CAC 2010), 2013, págs. 649-660
• Idioma: inglés
• Enlaces
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• Resumen

  • Purpose – The purpose of this paper is to show that the theoretical proofs of convergence in solution of ant colony optimization (ACO) algorithms have significant values of theory and application.

    Design/methodology/approach – This paper adapts the basic ACO algorithm framework and proves two important ACO subclass algorithms which are ACObs,τmin  and ACObs,τmin (t).

    Findings – This paper indicates that when the minimums of pheromone trial decay to 0 with the speed of logarithms, it is ensured that algorithms can, at least, get a certain optimal solution. Even if the randomicity and deflection of random algorithms are disturbed infinitesimally, algorithms can obtain optimal solution.

    Originality/value – This paper focuses on the analysis and proof of the convergence theory of ACO subset algorithm to explore internal mechanism of ACO algorithm.