• DocumentCode
    1637663
  • Title

    Hybrid Algorithm Combining Ant Colony Optimization Algorithm with Genetic Algorithm

  • Author

    Shang, Gao ; Xinzi, Jiang ; Kezong, Tang

  • Author_Institution
    Jiangsu Univ. of Sci. & Technol., Zhenjiang
  • fYear
    2007
  • Firstpage
    701
  • Lastpage
    704
  • Abstract
    By use of the properties of ant colony algorithm and genetic algorithm, a hybrid algorithm is proposed to solve the traveling salesman problems. First, it adopts genetic algorithm to give information pheromone to distribute. Second, it makes use of the ant colony algorithm to get several solutions through information pheromone accumulation and renewal. Finally, by using across and mutation operation of genetic algorithm, the effective solutions are obtained. Compare with the simulated annealing algorithm, the standard genetic algorithm, the standard ant colony algorithm, and statistics initial ant colony algorithm, all the 16 hybrid algorithms are proved effective. Especially the hybrid algorithm with across strategy B and mutation strategy B is a simple and effective better algorithm than others.
  • Keywords
    genetic algorithms; travelling salesman problems; ant colony optimization algorithm; genetic algorithm; hybrid algorithm; information pheromone; mutation strategy; traveling salesman problems; Ant colony optimization; Genetic algorithms; Genetic mutations; Information processing; Laboratories; Simulated annealing; Statistics; Traveling salesman problems; Ant Colony Algorithm; Genetic Algorithm; Traveling Salesman Problem;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference, 2007. CCC 2007. Chinese
  • Conference_Location
    Hunan
  • Print_ISBN
    978-7-81124-055-9
  • Electronic_ISBN
    978-7-900719-22-5
  • Type

    conf

  • DOI
    10.1109/CHICC.2006.4346773
  • Filename
    4346773