Cooperative coevolution algorithm with covariance analysis for differential evolution

被引：0
|
作者：
Wang B. [1 ,2 ]
Ren L. [1 ]
Wang X. [1 ]
Cao Y. [1 ]
机构：
[1] Faculty of Computer Science and Engineering, Xi’an University of Technology, Xi’an
[2] Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an
来源：
Tongxin Xuebao/Journal on Communications | 2023年 / 44卷 / 01期
基金：
中国国家自然科学基金;
关键词：
cooperative coevolution; correlation; covariance analysis; differential evolution; large-scale optimization problem;
D O I：
10.11959/j.issn.1000-436x.2023005
中图分类号：
学科分类号：
摘要：
With the increase of the number of decision variables, cooperative coevolution algorithm is easy to fall into local optimization in the process of searching the global optimal solution in large-scale high-dimensional optimization problems. Based on this, a cooperative coevolution algorithm with covariance analysis for differential evolution was proposed. After the optimization problems were grouped according to the correlation between the decision variables, the correlation between the internal variables of the subcomponents would affect the population evolution process. In the process of subcomponent optimization, covariance was used to calculate the characteristic vector of population distribution, and the correlation between variables was eliminated through coordinate rotation, which effectively avoided falling into local optimization in the process of population search and speeded up the optimization speed of the algorithm. Comparative experiments were carried out on the CEC 2014 test suite. The experimental results show that the proposed algorithm is feasible. © 2023 Editorial Board of Journal on Communications. All rights reserved.
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页码：189 / 199
页数：10
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