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Common model analysis and improvement of particle swarm optimizer

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Abstract

Particle swarm optimizer (PSO), a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to (−1, 1). Based on the deduced principle of acceleration factors, a new adaptive PSO algorithmharmonious PSO (HPSO) is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments, HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.

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Authors and Affiliations

  1. School of Information Science Technology, Beijing Institute of Technology, Beijing, 100081, China

    Feng Pan, Jie Chen, Minggang Gan, Guanghui Wang & Tao Cai

Authors
  1. Feng Pan
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  2. Jie Chen
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  3. Minggang Gan
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  4. Guanghui Wang
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  5. Tao Cai
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Additional information

This work was supported by the Teaching and Research Award Program for Outstanding Young Teacher in Higher Education Institute of Ministry of Education of China (NO. 20010248).

Feng PAN received the B.S. degree and Ph.D. degree in Pattern Recognition and Intellectual System from the Beijing Institute of Technology in China. Now he is a lecturer in Beijing Institute of Technology. His currently research interests include intelligent control, evolution computation and artificial intelligence, etc.

Jie CHEN received the B.S. degree, the M.S. degree and the Ph.D. 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 School of E&E, the University of Birmingham, U.K. He is currently a professor of Control Science and Engineering, Beijing Institute of Technology, P.R. China. His main research interests are complicated system multi-object optimization and decision, intelligent control, constrained nonlinear control, optimization methods, etc.

Minggang GAN received the B.S. degree in Control Theory and Control Engineering in 2002, from the Beijing Institute of Technology in China. He is currently a Ph.D. candidate of Pattern Recognition and Intelligent System in Beijing Institute of Technology. His main research is about electromagnetic interference and compatibility of control system. His main research aspects include artificial intelligence, filter, shielding and system electromagnetic interference suppression.

Guanghui WANG received the B.S. degree in electronic and information engineering in 2005, form China University of Geosciences (Beijing) in China. Now he is a M.S. candidate in Pattern Recognition and Intelligent System, in Beijing Institute of Technology in China. His research areas include artificial intelligence and computing 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 in China. His main research focus on the intelligent control, system engineering, nonlinear control and artificial intelligence, etc.

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Pan, F., Chen, J., Gan, M. et al. Common model analysis and improvement of particle swarm optimizer. J. Control Theory Appl. 5, 233–238 (2007). https://doi.org/10.1007/s11768-006-6132-x

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  • DOI: https://doi.org/10.1007/s11768-006-6132-x

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