Acceleration factor harmonious particle swarm optimizer

  • Jie ChenEmail author
  • Feng Pan
  • Tao Cai


A Particle Swarm Optimizer (PSO) exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight w is enhanced to (−1,1). Furthermore a new adaptive PSO algorithm — Acceleration Factor Harmonious PSO (AFHPSO) is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.


Particle swarm optimizer acceleration factor harmonious PSO asymptotic stability global convergence fuzzy control 


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  1. [1]
    J. Kennedy, R. C. Eberhart. Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks, IEEE Service Center, Piscataway, NJ, vol. VI, pp. 1942–1948, 1995.CrossRefGoogle Scholar
  2. [2]
    M. Clerc, J. Kennedy. The particle swarm: explosion stability and convergence in a multi-dimensional complex space. IEEE Transaction on Evolution Computation, vol. 6, no. 1, pp. 58–73, 2002.CrossRefGoogle Scholar
  3. [3]
    F. van den Bergh. An Analysis of Particle Swarm Optimizers, Ph.D dissertation, Department of Computer Science, University of Pretoria, South Africa, 2002Google Scholar
  4. [4]
    Jie Chen, Feng Pan, Tao Cai, Xu-yan Tu. The Stability Analysis of Particle Swarm Optimization without Lipschitz Condition Constrain. Control Theory and Application, vol. 1, no. 1, pp. 86–90, 2004.Google Scholar
  5. [5]
    Feng Pan. Research of Harmonious Particle Swarm Theory, Methods and its Application for Servo System, Ph.D dissertation, Department of Automatic Control, Beijing Institute of Technology, China, 2005.Google Scholar
  6. [6]
    Yang Xiao. Analysis of Dynamical Systems, Northern Jiao-Tong University press, Beijing, pp. 36–40, 2002.Google Scholar
  7. [7]
    F. Solis, R. Wets. Minimization by Random Search Techniques. Mathematics of Operations Research, vol. 6, pp. 19–30, 1981.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences 2006

Authors and Affiliations

  1. 1.Department of Automatic Control, School of Information Science and TechnologyBeijing Institute of TechnologyBeijingPRC

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