• DocumentCode
    2911834
  • Title

    Response threshold model of aggregation in a swarm: A theoretical and simulative comparison

  • Author

    Bailong, Liu ; Rubo, Zhang ; Changting, Shi

  • Author_Institution
    Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin
  • fYear
    2008
  • fDate
    1-6 June 2008
  • Firstpage
    1103
  • Lastpage
    1109
  • Abstract
    Swarm Intelligence(SI) which is inspired by social animals has been paid more and more attention. It always appeals to the collective behaviors observed in social animals. Aiming at the feature and factors in self-organization of SI system, the aggregation behavior is studied. Firstly the response threshold model of the system is built according to the rules in aggregation. Then the stability of the steady-state solutions of the model is analyzed and the bifurcation of the steady-state solution is obtained. Finally, the effects of the parameter are analyzed based on the theory model. And the Monte Carlo simulations which give certain differences against theory results are also analyzed. All of the theoretical and simulative results show that the aggregation behavior is impacted by the relationship between the swarm size and the response threshold and sensitivity significantly. It is also proved that complex behavior emerges from local interaction of individuals. The work of this paper gives the mechanism in the emergent complex pattern of self-organized aggregation and the factors which affect the system evolution.
  • Keywords
    Monte Carlo methods; artificial intelligence; Monte Carlo simulation; aggregation behavior; response threshold model; steady-state solution bifurcation; swarm intelligence; Animals; Ant colony optimization; Artificial intelligence; Bifurcation; Birds; Mathematical model; Negative feedback; Particle swarm optimization; Stability analysis; Steady-state;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-1822-0
  • Electronic_ISBN
    978-1-4244-1823-7
  • Type

    conf

  • DOI
    10.1109/CEC.2008.4630934
  • Filename
    4630934