• DocumentCode
    2387927
  • Title

    New Computational Model from Ant Colony

  • Author

    Gao Wei

  • Author_Institution
    Wuhan Polytech. Univ., Wuhan
  • fYear
    2007
  • fDate
    2-4 Nov. 2007
  • Firstpage
    640
  • Lastpage
    640
  • Abstract
    The computational model from life system has become a main intelligent algorithm. Ant colony algorithm is a new computational model from mimic the swarm intelligence of ant colony behavior. And it is a very good combination optimization method. To extend the ant colony algorithm, some continuous ant colony algorithms have been proposed. To improve the searching performance, the principles of evolutionary algorithm and immune system have been combined with the typical continuous ant colony algorithm, and one new computational model is proposed here. In this new model, the ant individual is transformed by adaptive Cauchi mutation and thickness selection. To verify the new computational model, the typical functions, such as Schaffer function is used. And then, the results of new algorithm are compared with that of ant colony algorithm and immunized evolutionary programming which is proposed by author. The results show that, the convergent speed and computing precision of new algorithm are all very good.
  • Keywords
    artificial immune systems; artificial life; convergence of numerical methods; evolutionary computation; adaptive Cauchi mutation; combination optimization; computational model; continuous ant colony algorithms; evolutionary algorithm; immune system; immunized evolutionary programming; intelligent algorithm; life system; swarm intelligence; Ant colony optimization; Biochemistry; Computational intelligence; Computational modeling; Evolutionary computation; Genetic mutations; Genetic programming; Immune system; Optimization methods; Particle swarm optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing, 2007. GRC 2007. IEEE International Conference on
  • Conference_Location
    Fremont, CA
  • Print_ISBN
    978-0-7695-3032-1
  • Type

    conf

  • DOI
    10.1109/GrC.2007.26
  • Filename
    4403178