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J4  2011, Vol. 45 Issue (12): 2099-2102    DOI: 10.3785/j.issn.1008-973X.2011.12.005
赵成业, 闫正兵, 刘兴高
浙江大学工业控制技术国家重点实验室,控制科学与工程学系,浙江 杭州 310027
Improved adaptive parameter particle swarm optimization algorithm
ZHAO Cheng-ye, YAN Zheng-bing, LIU Xing-gao
State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering,
Zhejiang University, Hangzhou 310027, China
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A modified adaptive parameter particle swarm optimization (MAPSO) algorithm was proposed to enhance the convergence speed and optimization accuracy of the standard particle swarm optimization (PSO) algorithm. The algorithm is based on evolutionary factor calculation and evolutionary status estimate, and introduces parameter control and mutation operator to improve the convergence speed and the global optimization ability. Benchmark test comparison between two existing algorithms and the MAPSO algorithm was carried out. The experimental results show that the MAPSO algorithm can get better solution on 4 in 6 benchmark test functions and can converge faster on 5 in 6 benchmark test functions than the other two algorithms. The results reveal the advantage of the proposed algorithm in different optimization indexes, which is supposed to have a promising potential in solving the optimization problems of the practical process engineering.

出版日期: 2011-12-01
:  TP2  


通讯作者: 刘兴高,男,教授,博导.     E-mail:
作者简介: 赵成业(1986—),男,硕士生,从事智能方法应用研究.通信联系人:刘兴高,男,教授,博导.E-mail:
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赵成业, 闫正兵, 刘兴高. 改进的变参数粒子群优化算法[J]. J4, 2011, 45(12): 2099-2102.

ZHAO Cheng-ye, YAN Zheng-bing, LIU Xing-gao. Improved adaptive parameter particle swarm optimization algorithm. J4, 2011, 45(12): 2099-2102.


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