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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)
Article

Abstract

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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References

  1. [1]
    OSMAN I H. A tabu search procedure based on a random roulette diversification for the weighted maximal planar graph problem[J]. Computers and operations Research, 2006, 33(9): 2526–2546. (in Chinese)MathSciNetCrossRefGoogle Scholar
  2. [2]
    Hegazy T. Optimization of resource allocation and leveling using genetic algorithms[J]. Journal of Construction Engineering and Management, 1999(6): 167–175.CrossRefGoogle Scholar
  3. [3]
    GUO Yan, NING Xuan-xi. Using genetic algorithms for multi-project resource balance[J]. Systems Engineering: Theory and Practice, 2005(10): 78–82. (in Chinese)Google Scholar
  4. [4]
    KENNEDY J, EBERHART R C. Particle swarm optimization[C]// Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ: IEEE Press, 1995: 1942–1948.CrossRefGoogle Scholar
  5. [5]
    EBERHART R C, SHI Y. Particle swarm optimization: Developments, applications and resources[C]// Proceedings of 2001 Congress Evolutionary Computation. Piscataway, NJ: IEEE Press, 2001: 81–86.Google Scholar
  6. [6]
    PARSOPOULOS K E, VRAHATIS M N. Particle swarm optimization method for constrained optimization problems[C]// Intelligent Technologies: from Theory to Applications. Amsterdam: IOS Press, 2002: 214–220.Google Scholar
  7. [7]
    EBERHART R C, KENNEDY J. A new optimizer using particle swarm theory[C]// Proc on 6th International Symposium on Micromachine and Human Science. Piscataway, NJ: IEEE Service Center, 1995, 39–43.CrossRefGoogle Scholar
  8. [8]
    KENNEDY J. The particle swarm: Social adaptation of knowledge[C]// IEEE International Conference on Evolutionary Computation. Piscataway, NJ: IEEE Service Center, 1997: 303–308.Google Scholar
  9. [9]
    WANG Ding-wei. Colony location algorithm for assignment problems[J]. Journal of Control Theory and Applications, 2004, 2(2):111–116.MathSciNetCrossRefGoogle Scholar
  10. [10]
    HOPFIELD J J, TANK D W. Neural computation of decision in optimization problems[J]. Biological Cybernetics, 1985, 52: 141–152.MathSciNetzbMATHGoogle Scholar
  11. [11]
    LI Xiang, YANG Shang-dong, QI Jian-xun, YANG Shu-xia. Improved wavelet neural network combined with particle swarm optimization algorithm and its application[J]. Journal of Central South University of Technology, 2006, 13(3): 256–259.CrossRefGoogle Scholar
  12. [12]
    LI Xiang, CUI Ji-feng, QI Jian-xun, YANG Shang-dong. Energy transmission nodes based on Tabu search and particle swarm hybrid optimization algorithm[J]. Journal of Central South University of Technology, 2007, 14(1): 144–148.CrossRefGoogle Scholar

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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