Adaptive Differential Evolution With Information Entropy-Based Mutation Strategy

In order to balance the exploration and exploitation ability of differential evolution (DE), different mutation strategy for different evolutionary stages may be effective. An adaptive differential evolution with information entropy-based mutation strategy (DEIE) is proposed to divide the evolutiona...

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Autores principales:	Liujing Wang, Xiaogen Zhou, Tengyu Xie, Jun Liu, Guijun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Differential evolution information entropy mutation strategy evolutionary stages Markov state model Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e7a2af574f9b49599cb645fb7c31f479
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id	oai:doaj.org-article:e7a2af574f9b49599cb645fb7c31f479
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spelling	oai:doaj.org-article:e7a2af574f9b49599cb645fb7c31f4792021-11-09T00:03:00ZAdaptive Differential Evolution With Information Entropy-Based Mutation Strategy2169-353610.1109/ACCESS.2021.3119616https://doaj.org/article/e7a2af574f9b49599cb645fb7c31f4792021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9568913/https://doaj.org/toc/2169-3536In order to balance the exploration and exploitation ability of differential evolution (DE), different mutation strategy for different evolutionary stages may be effective. An adaptive differential evolution with information entropy-based mutation strategy (DEIE) is proposed to divide the evolutionary process reasonably. In DEIE, the number of Markov states deduced from the crowding strategy is determined first and then the transition matrix between states is inferred from the historical evolutionary information. Based on the above-mentioned knowledge, the Markov state model is constructed. The evolutionary process is divided into exploration and exploitation stages dynamically using the information entropy derived from the Markov state model. Consequently, stage-specific mutation operation is employed adaptively. Experiments are conducted on CEC 2013, 2014, and 2017 benchmark sets and classical benchmark functions to assess the performance of DEIE. Moreover, the proposed approach is also used to solve the protein structure prediction problem efficiently.Liujing WangXiaogen ZhouTengyu XieJun LiuGuijun ZhangIEEEarticleDifferential evolutioninformation entropymutation strategyevolutionary stagesMarkov state modelElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146783-146796 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Differential evolution information entropy mutation strategy evolutionary stages Markov state model Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Differential evolution information entropy mutation strategy evolutionary stages Markov state model Electrical engineering. Electronics. Nuclear engineering TK1-9971 Liujing Wang Xiaogen Zhou Tengyu Xie Jun Liu Guijun Zhang Adaptive Differential Evolution With Information Entropy-Based Mutation Strategy
description	In order to balance the exploration and exploitation ability of differential evolution (DE), different mutation strategy for different evolutionary stages may be effective. An adaptive differential evolution with information entropy-based mutation strategy (DEIE) is proposed to divide the evolutionary process reasonably. In DEIE, the number of Markov states deduced from the crowding strategy is determined first and then the transition matrix between states is inferred from the historical evolutionary information. Based on the above-mentioned knowledge, the Markov state model is constructed. The evolutionary process is divided into exploration and exploitation stages dynamically using the information entropy derived from the Markov state model. Consequently, stage-specific mutation operation is employed adaptively. Experiments are conducted on CEC 2013, 2014, and 2017 benchmark sets and classical benchmark functions to assess the performance of DEIE. Moreover, the proposed approach is also used to solve the protein structure prediction problem efficiently.
format	article
author	Liujing Wang Xiaogen Zhou Tengyu Xie Jun Liu Guijun Zhang
author_facet	Liujing Wang Xiaogen Zhou Tengyu Xie Jun Liu Guijun Zhang
author_sort	Liujing Wang
title	Adaptive Differential Evolution With Information Entropy-Based Mutation Strategy
title_short	Adaptive Differential Evolution With Information Entropy-Based Mutation Strategy
title_full	Adaptive Differential Evolution With Information Entropy-Based Mutation Strategy
title_fullStr	Adaptive Differential Evolution With Information Entropy-Based Mutation Strategy
title_full_unstemmed	Adaptive Differential Evolution With Information Entropy-Based Mutation Strategy
title_sort	adaptive differential evolution with information entropy-based mutation strategy
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/e7a2af574f9b49599cb645fb7c31f479
work_keys_str_mv	AT liujingwang adaptivedifferentialevolutionwithinformationentropybasedmutationstrategy AT xiaogenzhou adaptivedifferentialevolutionwithinformationentropybasedmutationstrategy AT tengyuxie adaptivedifferentialevolutionwithinformationentropybasedmutationstrategy AT junliu adaptivedifferentialevolutionwithinformationentropybasedmutationstrategy AT guijunzhang adaptivedifferentialevolutionwithinformationentropybasedmutationstrategy
_version_	1718441433783861248

Adaptive Differential Evolution With Information Entropy-Based Mutation Strategy

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