Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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.
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.
F. van den Bergh. An Analysis of Particle Swarm Optimizers, Ph.D dissertation, Department of Computer Science, University of Pretoria, South Africa, 2002
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.
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.
Yang Xiao. Analysis of Dynamical Systems, Northern Jiao-Tong University press, Beijing, pp. 36–40, 2002.
F. Solis, R. Wets. Minimization by Random Search Techniques. Mathematics of Operations Research, vol. 6, pp. 19–30, 1981.
Author information
Authors and Affiliations
Corresponding author
Additional information
The work was supported by the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutes of MOE, PRC
Jie Chen received the B.S. degree, the M.S. degree and the PHD degree in Control Theory and Control Engineering in 1986, 1993 and 2000, respectively, from the Beijing Institute of Technology. From 1989 to 1990, he was a visiting scholar in California State University, U.S.A. From 1996 to 1997, he was a Research Fellow in the School of E&E, at the university of Birmingham, U.K. He is currently a professor of Control Science and Engineering, at Beijing Institute of Technology, P.R. China.
His main research interests are complicated system multi-object optimization and decisions, intelligent control, constrained nonlinear control, and optimization methods.
Feng Pan received the B.S. degree in Control Theory and Control Engineering in 2000 and the Ph.D degree in Pattern Recognition and Intelligent Systems, from the Beijing Institute of Technology, P.R. China.
His current research interests include servo systems, intelligent control, evolutionary computation and artificial intelligence.
Tao Cai received the B.S. degree and the M.S. degree in Control Theory and Control Engineering in 1993 and 1999 respectively, from the Beijing Institute of Technology, P.R. China.
His main research focus is in intelligent control, system engineering, nonlinear control and artificial intelligence.
Rights and permissions
About this article
Cite this article
Chen, J., Pan, F. & Cai, T. Acceleration factor harmonious particle swarm optimizer. Int J Automat Comput 3, 41–46 (2006). https://doi.org/10.1007/s11633-006-0041-9
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/s11633-006-0041-9