Improved ensemble of differential evolution variants.

In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators-ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performan...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Juan Yao, Zhe Chen, Zhenling Liu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/db3c4bfcf1244257a94e54e6e62a33a2
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:db3c4bfcf1244257a94e54e6e62a33a2
record_format	dspace
spelling	oai:doaj.org-article:db3c4bfcf1244257a94e54e6e62a33a22021-12-02T20:17:43ZImproved ensemble of differential evolution variants.1932-620310.1371/journal.pone.0256206https://doaj.org/article/db3c4bfcf1244257a94e54e6e62a33a22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256206https://doaj.org/toc/1932-6203In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators-ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. In this paper, we manage to implement measures into Ensemble of Differential Evolution Variants (EDEV). Firstly, we extend the collecting range of optional external archive of JADE-one of the constituent algorithm in EDEV. Then, we revise and implement the Event-Triggered Impulsive (ETI) control. Finally, Linear Population Size Reduction (LPSR) is used by us. Then, we obtain Improved Ensemble of Differential Evolution Variants (IEDEV). In our experiments, good performers in the CEC competitions on real parameter single objective optimization among population-based metaheuristics, state-of-the-art DE algorithms, or up-to-date DE algorithms are involved. Experiments show that our IEDEV is very competitive.Juan YaoZhe ChenZhenling LiuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256206 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Juan Yao Zhe Chen Zhenling Liu Improved ensemble of differential evolution variants.
description	In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators-ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. In this paper, we manage to implement measures into Ensemble of Differential Evolution Variants (EDEV). Firstly, we extend the collecting range of optional external archive of JADE-one of the constituent algorithm in EDEV. Then, we revise and implement the Event-Triggered Impulsive (ETI) control. Finally, Linear Population Size Reduction (LPSR) is used by us. Then, we obtain Improved Ensemble of Differential Evolution Variants (IEDEV). In our experiments, good performers in the CEC competitions on real parameter single objective optimization among population-based metaheuristics, state-of-the-art DE algorithms, or up-to-date DE algorithms are involved. Experiments show that our IEDEV is very competitive.
format	article
author	Juan Yao Zhe Chen Zhenling Liu
author_facet	Juan Yao Zhe Chen Zhenling Liu
author_sort	Juan Yao
title	Improved ensemble of differential evolution variants.
title_short	Improved ensemble of differential evolution variants.
title_full	Improved ensemble of differential evolution variants.
title_fullStr	Improved ensemble of differential evolution variants.
title_full_unstemmed	Improved ensemble of differential evolution variants.
title_sort	improved ensemble of differential evolution variants.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/db3c4bfcf1244257a94e54e6e62a33a2
work_keys_str_mv	AT juanyao improvedensembleofdifferentialevolutionvariants AT zhechen improvedensembleofdifferentialevolutionvariants AT zhenlingliu improvedensembleofdifferentialevolutionvariants
_version_	1718374376836956160

Improved ensemble of differential evolution variants.

Ejemplares similares