A weak linked multi-subpopulation kinetic-molecular theory optimization algorithm

被引：2
|
作者：
Fan C.-D. [1 ,2 ]
Liu Y.-N. [1 ]
Zhang J. [1 ,3 ]
Yi L.-Z. [1 ]
Xiao L.-Y. [3 ]
机构：
[1] College of Information Engineering, Xiangtan University, Xiangtan, 411105, Hunan
[2] Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning, 530006, Guangxi
[3] College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Chaotic perturbation; Clustering phenomenon; Kinetic-molecular theory optimization algorithm; Multiple subpopulations; Weak link;
D O I：
10.7641/CTA.2018.70714
中图分类号：
学科分类号：
摘要：
For overcoming the shortcomings of the topology and the 'cluster' phenomenon in the kinetic-molecular theory optimization algorithm (KMTOA), based on chaotic mapping and elite learning strategy, a weak linked multisubpopulation kinetic-molecular theory optimization algorithm (WLMS-KMTOA) is proposed in this paper. WLMS- KMTOA includes two layers. In the lower layer, some subgroups perform heuristic search to improve the convergence rate of WLMS-KMTOA. In the upper layer, WLMS-KMTOA uses the chaotic sequence subpopulation to avoid falling into local optimum, and uses immune local learning subgroup to perform a refined search to improve the convergence accuracy. The simulation results show that WLMS-KMTOA has good performance in solution precision and convergence speed, and can be well applied to the functions with different shift values. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：108 / 119
页数：11
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