Improved particle swarm optimization algorithms by Alopex and its application in soft sensor modeling

被引：0
|
作者：
Li, Shao-Jun [1 ]
Zhang, Xu-Jie [1 ]
Wang, Hui [1 ]
Qian, Feng [1 ]
机构：
[1] Research Institute of Automation, East China University of Science and Technology, Shanghai 200237, China
来源：
Huadong Ligong Daxue Xuebao /Journal of East China University of Science and Technology | 2006年 / 32卷 / 09期
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摘要：
Particle swarm optimization is a simple stochastic global optimization technique. Its significant feature is simpler expression and less parameters, but it is easily slumped local minima. A particle swarm optimization algorithm improved by Alopex is brought forward. The proposed algorithm sustains diversity in population efficiently and improves the ability of breaking away from local minima. At last the improved algorithm is used to model the soft sensor based on artificial neural networks. The experiment results demonstrate that the proposed algorithm is superior to the original particle swarm optimization algorithm, especially multi-apices function.
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页码：1104 / 1108
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