• DocumentCode
    3380245
  • Title

    A fine tuning hybrid particle swarm optimization algorithm

  • Author

    Tang, Jun ; Zhao, Xiaojuan

  • Author_Institution
    Dept. of Inf. Eng., Hunan Urban Constr. Coll., XiangTan, China
  • fYear
    2009
  • fDate
    13-14 Dec. 2009
  • Firstpage
    296
  • Lastpage
    299
  • Abstract
    Particle swarm optimization (PSO) has shown its good performance in many optimization problems. This paper introduces a new approach called hybrid particle swarm optimization like algorithm (HPSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (ePSO). The performance of all the three PSO algorithms is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms.
  • Keywords
    extrapolation; mathematical operators; particle swarm optimisation; PSO algorithm; extrapolated particle swarm optimization; fine tuning operators; hybrid particle swarm optimization algorithm; Acceleration; Algorithm design and analysis; Biomedical engineering; Educational institutions; Equations; Genetic algorithms; Genetic mutations; Optimal control; Particle swarm optimization; Performance analysis; PSO; cross-over operator; mutation operators; particle swarm optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    BioMedical Information Engineering, 2009. FBIE 2009. International Conference on Future
  • Conference_Location
    Sanya
  • Print_ISBN
    978-1-4244-4690-2
  • Electronic_ISBN
    978-1-4244-4692-6
  • Type

    conf

  • DOI
    10.1109/FBIE.2009.5405908
  • Filename
    5405908