Journal of Central South University of Technology

, Volume 15, Issue 1, pp 141–146 | Cite as

An extended particle swarm optimization algorithm based on coarse-grained and fine-grained criteria and its application

  • Li Xing-mei  (샮탇梅)Email author
  • Zhang Li-hui  (헅솢믔)
  • Qi Jian-xun  (웲建勋)
  • Zhang Su-fang  (헅 쯘芳)


In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.

Key words

particle swarm extended particle swarm optimization algorithm resource leveling 


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Copyright information

© Published by: Central South University Press, Sole distributor outside Mainland China: Springer 2008

Authors and Affiliations

  • Li Xing-mei  (샮탇梅)
    • 1
    Email author
  • Zhang Li-hui  (헅솢믔)
    • 1
  • Qi Jian-xun  (웲建勋)
    • 1
  • Zhang Su-fang  (헅 쯘芳)
    • 1
  1. 1.School of Business AdministrationNorth China Electric Power UniversityBeijingChina

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