[1]林振思,刘国买,孙磊.微粒群算法参数设置的正交试验分析[J].福建工程学院学报,2016,14(01):55-61.[doi:10.3969/j.issn.1672-4348.2016.01.013]
　Lin Zhensi,Liu Guomai,Sun Lei.Parameters setting of particle swarm optimization based on orthogonal experiment analysis[J].Journal of FuJian University of Technology,2016,14(01):55-61.[doi:10.3969/j.issn.1672-4348.2016.01.013]
点击复制

微粒群算法参数设置的正交试验分析()
分享到：

《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第14卷
期数:
2016年01期
页码:
55-61
栏目:
出版日期:
2016-02-25

文章信息/Info

Title:
Parameters setting of particle swarm optimization based on orthogonal experiment analysis
作者:
林振思; 刘国买; 孙磊
福建工程学院管理学院
Author(s):
Lin Zhensi;  Liu Guomai;  Sun Lei
School of Management, Fujian University of Technology
关键词:
正交试验设计;  微粒群算法;  参数设置
Keywords:
orthogonal experimental design;  particle swarm optimization;  parameters setting
分类号:
TN911.72;TB 123
DOI:
10.3969/j.issn.1672-4348.2016.01.013
文献标志码:
A
摘要:
微粒群算法(PSO)提出后，由于其优越的性能和易用性而得到了广泛的应用。传统PSO在算法参数设置上主要凭研究者经验进行选择，难免存在主观随意性偏差。采用正交试验设计的方法对PSO算法的w、c1、c2参数设置进行试验分析，从而提出较好的参数设置。通过对4个标准测试函数的实验分析，结果显示当w=1、c1=c2=3时算法有较好的性能。
Abstract:
Particle swarm optimization (PSO) has been widely used in many fields due to its superior performance and ease of implementation. However, the main parameters of PSO, w,c 1 and c 2 , are hard to select. Based on the orthogonal experiment design method, an experiment was conducted on four benchmark functions to search the optimal parameters setting of PSO. The experimental results show that the PSO has better performance when w=1 and c 1 =c 2=3.

参考文献/References:

[1] Kennedy J, Eberhart R C. Particle swarm optimization[C]//Proceedings of the 1995 IEEE International Conference on Neural Networks.Nov.27-Dec.1 1995. Perth, Australia. Piscataway: IEEE Service Center,1995:1942-1948.
[2] Ab Wahab M N, NeftiMeziani S, Atyabi A. A comprehensive review of swarm optimization algorithms[J]. PLoS ONE,2015,10(5):e0122827.
[3] Shi Y, Eberhart R C. A modified particle swarm optimizer[C]//Proceedings of the 1998 IEEE World Congress on Computational Intelligence & The 1998 IEEE International Conference on Evolutionary Computation. Anchorage, 4-9 May 1998. USA. Piscataway: IEEE Service Center,1998:69-73.
[4] Eberhart R C, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization[C]//Proceedings of The 2000 Congress on Evolutionary Computation.16-19 Jul 2000. La Jolla, USA. Piscataway: IEEE Service Center,2000:84-88.
[5] Shi Y, Eberhart R C. Empirical study of particle swarm optimization[C]//Proceedings of The 1999 Congress on Evolutionary Computation.6-9 July 1999. Washington, USA.Piscataway: IEEE Service Center,1999:32-49.
[6] Trelea I C. The particle swarm optimization algorithm: Convergence analysis and parameter selection[J].Information Processing Letters,2003,85(6):317-325.
[7] 朱代根.基于田口方法的PSO算法参数设计[J].科技信息,2011(30):45-46+49.
[8] 高尚,陈建忠.基于均匀设计的粒子群优化算法参数设定[J].石油化工高等学校学报,2007(3):12-15.

相似文献/References:

[1]谢开池,薛醒思.基于改进KNN的案例匹配模块的设计与实现[J].福建工程学院学报,2017,15(04):349.[doi:10.3969/j.issn.1672-4348.2017.04.009]
　Xie Kaichi,Xue Xingsi.Design and implementation of a case matching module based on improved KNN[J].Journal of FuJian University of Technology,2017,15(01):349.[doi:10.3969/j.issn.1672-4348.2017.04.009]

更新日期/Last Update: 2016-02-25