• DocumentCode
    2787532
  • Title

    Parallel implementation of ant colony optimization on MPP

  • Author

    Chen, Ling ; Sun, Hai-ying ; Wang, Shu

  • Volume
    2
  • fYear
    2008
  • fDate
    12-15 July 2008
  • Firstpage
    981
  • Lastpage
    986
  • Abstract
    An adaptive parallel ant colony algorithm (PACO) is presented. In the algorithm, we propose a strategy for information exchange between processors which make each processor choose its partner to communicate and update the pheromone adaptively. We also propose a method of adjusting the time interval of information exchange adaptively according to the diversity of the solutions so as to increase the ability of search and avoid early convergence. Experimental results show that our algorithm PACO has high convergence speed, high speedup and efficiency.
  • Keywords
    convergence; parallel algorithms; search problems; adaptive parallel ant colony optimization algorithm; adaptive pheromone update; convergence speed; information exchange time interval; massive parallel processors; processor communication; processor information exchange; search ability; traveling salesman problem; Ant colony optimization; Computer science; Convergence; Cybernetics; Fault detection; Frequency; Load management; Machine learning; Software algorithms; Traveling salesman problems; Ant colony optimization; Diversity; Parallel; Traveling Salesman Problem;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2008 International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4244-2095-7
  • Electronic_ISBN
    978-1-4244-2096-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2008.4620547
  • Filename
    4620547