Modified genetic algorithm based on state-space model and its convergence analysis

被引：0

作者：

Qi Z. ^{[1
]}

Li M.-J. ^{[1
]}

Mo H. ^{[1
]}

Xiao Y.-H. ^{[1
]}

Liu F. ^{[1
]}

机构：

[1] College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Absorbing markov process; Attaining-state set; Evolutionary algorithm; Genetic algorithm based on state-space model; Global convergence;

D O I：

10.7641/CTA.2020.90986

中图分类号：

学科分类号：

摘要：

Genetic algorithm based on state-space model (GABS) is an innovative real-coded simulated evolutionary algorithm, which has good results in solving engineering optimization problems. The GABS has no theoretical foundation as a support. We therefore established a mathematical model based on absorbing Markov processes for GABS. The analysis of GABS from the perspective of attaining-state set indicated that GABS is not globally convergent. A modified genetic algorithm based on state-space model (MGABS) was therefore proposed. There are two mutation strategies in MGABS, which not only expand the attaining-state set and enrich the population diversity, but also accelerate the convergence speed and accuracy. The conclusion that MGABS has global convergence was obtained. Finally, 16 benchmark functions were taken as case study to verify the global convergence of MGABS. The results show that the MGABS has obvious advantages over the other three algorithms in terms of comprehensive performance. This paper therefore provides theoretical basis for the application of algorithm in engineering. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：2115 / 2122

页数：7

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