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浙江大学学报(工学版)
J4  2011, Vol. 45 Issue (12): 2099-2102    DOI: 10.3785/j.issn.1008-973X.2011.12.005
自动化技术     
改进的变参数粒子群优化算法
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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摘要：

为了提高标准粒子群优化(PSO)算法在收敛速度和优化精度上的性能,提出一种改进的变参数粒子群优化(MAPSO)算法.该方法以进化状态因子计算策略和进化状态估计模型为基础,引入了算法参数控制和变异算子,提高了算法的收敛速度和全局优化能力.在多个基准单峰和多峰优化问题上,对已有的2种算法和MAPSO算法进行了测试和比较，结果表明：在优化精度上,MAPSO算法在6个基准测试函数的4个测试函数上都优于另2种算法；在收敛速度方面,MAPSO算法在5个测试函数上都优于其他2个算法，体现了MAPSO算法在多个性能指标上的优越性.
Abstract:

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  
基金资助:

国家自然科学基金资助项目(50876093)；浙江省杰出青年科学基金资助项目(R4100133)；浙江省科技厅国际合作资助项目(2009C3408)；国家“863”高技术研究发展计划资助项目(2006AA05Z226).
通讯作者: 刘兴高,男,教授,博导.     E-mail: lxg@zju.edu.cn
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: lxg@zju.edu.cn
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引用本文:

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.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2011.12.005    或    https://www.zjujournals.com/eng/CN/Y2011/V45/I12/2099

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