Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix

Differential evolution (DE) is a robust algorithm of global optimization which has been used for solving many of the real-world applications since it was proposed. However, binomial crossover does not allow for a sufficiently effective search in local space. DE’s local search performance is therefor...

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Autores principales:	Changshou Deng, Xiaogang Dong, Yucheng Tan, Hu Peng
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/83f46ae47e28438a8036703804ac9c21
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spelling	oai:doaj.org-article:83f46ae47e28438a8036703804ac9c212021-11-08T02:35:33ZEnhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix1687-527310.1155/2021/8930980https://doaj.org/article/83f46ae47e28438a8036703804ac9c212021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8930980https://doaj.org/toc/1687-5273Differential evolution (DE) is a robust algorithm of global optimization which has been used for solving many of the real-world applications since it was proposed. However, binomial crossover does not allow for a sufficiently effective search in local space. DE’s local search performance is therefore relatively poor. In particular, DE is applied to solve the complex optimization problem. In this case, inefficiency in local research seriously limits its overall performance. To overcome this disadvantage, this paper introduces a new local search scheme based on Hadamard matrix (HLS). The HLS improves the probability of finding the optimal solution through producing multiple offspring in the local space built by the target individual and its descendants. The HLS has been implemented in four classical DE algorithms and jDE, a variant of DE. The experiments are carried out on a set of widely used benchmark functions. For 20 benchmark problems, the four DE schemes using HLS have better results than the corresponding DE schemes, accounting for 80%, 75%, 65%, and 65% respectively. Also, the performance of jDE with HLS is better than that of jDE on 50% test problems. The experimental results and statistical analysis have revealed that HLS could effectively improve the overall performance of DE and jDE.Changshou DengXiaogang DongYucheng TanHu PengHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Changshou Deng Xiaogang Dong Yucheng Tan Hu Peng Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix
description	Differential evolution (DE) is a robust algorithm of global optimization which has been used for solving many of the real-world applications since it was proposed. However, binomial crossover does not allow for a sufficiently effective search in local space. DE’s local search performance is therefore relatively poor. In particular, DE is applied to solve the complex optimization problem. In this case, inefficiency in local research seriously limits its overall performance. To overcome this disadvantage, this paper introduces a new local search scheme based on Hadamard matrix (HLS). The HLS improves the probability of finding the optimal solution through producing multiple offspring in the local space built by the target individual and its descendants. The HLS has been implemented in four classical DE algorithms and jDE, a variant of DE. The experiments are carried out on a set of widely used benchmark functions. For 20 benchmark problems, the four DE schemes using HLS have better results than the corresponding DE schemes, accounting for 80%, 75%, 65%, and 65% respectively. Also, the performance of jDE with HLS is better than that of jDE on 50% test problems. The experimental results and statistical analysis have revealed that HLS could effectively improve the overall performance of DE and jDE.
format	article
author	Changshou Deng Xiaogang Dong Yucheng Tan Hu Peng
author_facet	Changshou Deng Xiaogang Dong Yucheng Tan Hu Peng
author_sort	Changshou Deng
title	Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix
title_short	Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix
title_full	Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix
title_fullStr	Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix
title_full_unstemmed	Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix
title_sort	enhanced differential evolution algorithm with local search based on hadamard matrix
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/83f46ae47e28438a8036703804ac9c21
work_keys_str_mv	AT changshoudeng enhanceddifferentialevolutionalgorithmwithlocalsearchbasedonhadamardmatrix AT xiaogangdong enhanceddifferentialevolutionalgorithmwithlocalsearchbasedonhadamardmatrix AT yuchengtan enhanceddifferentialevolutionalgorithmwithlocalsearchbasedonhadamardmatrix AT hupeng enhanceddifferentialevolutionalgorithmwithlocalsearchbasedonhadamardmatrix
_version_	1718443200745570304

Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix

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