Strategy Self-adaptive Differential Evolution Algorithm Based on State Estimation Feedback

被引：0
|
作者：
Wang L.-J. [1 ]
Zhang G.-J. [1 ]
Zhou X.-G. [1 ]
机构：
[1] College of Information Engineering, Zhejiang University of Technology, Hangzhou
来源：
Zhang, Gui-Jun (zgj@zjut.edu.cn) | 2020年 / Science Press卷 / 46期
基金：
中国国家自然科学基金;
关键词：
Abstract convex; Differential evolution (DE); Feedback; Global optimization; State estimation;
D O I：
10.16383/j.aas.2018.c170338
中图分类号：
学科分类号：
摘要：
Inspired by the idea of closed-loop control, a strategy self-adaptive differential evolution (DE) algorithm based on state estimation feedback is proposed, the stage of individual can be self-adaptively determined by designing the state judgment factor, and achieve the feedback adjustment of mutation strategies. Consequently, the algorithm can get a trade-off between the exploration and exploitation. Firstly, the estimation model of evolution state is established based on abstract convex theory, from which the underestimation information is extracted combining with the evolutionary information to design the state judgment factor, so that the evolution state of the current population is estimated. Secondly, according to the feedback information of the state judgment factor, the strategy in different evolution state is adaptively selected to guide the evolution of the population. Therefore, the searching efficiency of the algorithm can be improved. Additionally, experimental results of 20 benchmark functions and CEC2013 test set show that the proposed algorithm is superior to the main-stream differential evolution variants and non-differential evolution algorithms mentioned in this paper in terms of computational cost, convergence speed, and solution quality. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
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页码：752 / 766
页数：14
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