• DocumentCode
    3373094
  • Title

    Diversity Guaranteed Ant Colony Algorithm Based on Immune Strategy

  • Author

    Qin, Ling ; Chen, Yixin ; Luo, Jianli ; Chen, Ling ; Guo, Jing

  • Author_Institution
    Dept. of Comput. Sci., Nanjing Univ. of Aeronaut. & Astronaut.
  • Volume
    2
  • fYear
    2006
  • fDate
    20-24 June 2006
  • Firstpage
    217
  • Lastpage
    223
  • Abstract
    A diversity guaranteed ant colony algorithm is presented by simulating the behavior of biological immune system. The algorithm adopts the immunogenic methods immune selection, immune memory, immune metabolism, density control and isolation niche technique. In each iteration of the algorithm, the solutions of the ants are selected to have crossover and mutation operations according to their quality and the distribution of the solutions. The mutation probability is determined by the diversity of the solutions. Experimental results on the traveling salesman problem show that our algorithm can obtain high quality of solutions, high convergence speed. It can avoid the stagnation and premature phenomena and has strong capability of optimization
  • Keywords
    artificial life; convergence; optimisation; biological immune system; convergence speed; density control; diversity guaranteed ant colony algorithm; immune memory; immune metabolism; immune selection; immunogenic methods; isolation niche technique; mutation probability; premature phenomena; traveling salesman problem; Ant colony optimization; Biological system modeling; Computational modeling; Computer science; Educational institutions; Engineering management; Genetic mutations; Immune system; Learning systems; Traveling salesman problems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Computational Sciences, 2006. IMSCCS '06. First International Multi-Symposiums on
  • Conference_Location
    Hanzhou, Zhejiang
  • Print_ISBN
    0-7695-2581-4
  • Type

    conf

  • DOI
    10.1109/IMSCCS.2006.214
  • Filename
    4673705