• DocumentCode
    2254676
  • Title

    An improved particle swarm optimization algorithm for geometric constraint solving problem

  • Author

    Cao, Chun-hong ; Zhang, Chang-sheng ; Wang, Li-Min

  • Author_Institution
    Collge of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
  • Volume
    4
  • fYear
    2010
  • fDate
    11-14 July 2010
  • Firstpage
    1335
  • Lastpage
    1838
  • Abstract
    Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. The constraint problem can be transformed to an optimization problem. We can solve the problem by an improved PSO algorithm (IPSO), which is based on the “alldifferent” constraint. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a particle is going to stagnate, the mutation operator is used to search its neighborhood. The experiment indicates that the algorithm can be used to solve geometric constraint problem effectively.
  • Keywords
    constraint theory; genetic algorithms; geometry; nonlinear equations; particle swarm optimisation; alldifferent constraint; genetic operator; geometric constraint solving problem; improved PSO algorithm; mutation operator; nonlinear equation; optimization problem; particle swarm optimization; Algorithm design and analysis; Convergence; Equations; Mathematical model; Optimization; Particle swarm optimization; Schedules; Fitness computation; Geometric constraint; Particle swarm optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-1-4244-6526-2
  • Type

    conf

  • DOI
    10.1109/ICMLC.2010.5580958
  • Filename
    5580958