• DocumentCode
    1732179
  • Title

    A share historical and global best particle swarm optimization algorithm

  • Author

    Zhigang, Lian ; Keyi, Hu ; Zhibin, Jiang ; Dongbiao, Zheng

  • Author_Institution
    Electron. & Inf. Sch., Shanghai DianJi Univ., Shanghai, China
  • Volume
    1
  • fYear
    2011
  • Firstpage
    526
  • Lastpage
    530
  • Abstract
    This article advances a share historical and global best particle swarm optimization algorithm (SGHPSO). In SGHPSO model, particles fully inherit the information of historical and global optimum particles in previous operation, which increases the search efficiency of particles. Ten typical nonlinear functions are given to test the efficiency of the improved algorithm. Simulation results clearly demonstrate superiority of the improved algorithm.
  • Keywords
    particle swarm optimisation; SGHPSO; nonlinear functions; share historical and global best particle swarm optimization algorithm; Medical services; Historical; PSO; global; inherit;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Network Technology (ICCSNT), 2011 International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-1-4577-1586-0
  • Type

    conf

  • DOI
    10.1109/ICCSNT.2011.6182012
  • Filename
    6182012
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1732179