| 摘要: |
|
| 关键词: |
| DOI： |
| Received:July 26, 2006Revised:April 03, 2007 |
| 基金项目: |
|
| Common model analysis and improvement of particle swarm optimizer |
| Feng PAN, Jie CHEN, Minggang GAN, Guanghui WANG, Tao CAI |
| (School of Information Science Technology, Beijing Institute of Technology, Beijing 100081, China) |
| 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. |
| Key words: Particle swarm optimizer Asymptotic stability Global convergence System identification Akaike information criteria |
