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Self-adaptive dual-strategy differential evolution algorithm

Exploration and exploitation are contradictory in differential evolution (DE) algorithm. In order to balance the search behavior between exploitation and exploration better, a novel self-adaptive dual-strategy differential evolution algorithm (SaDSDE) is proposed. Firstly, a dual-strategy mutation o...

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Detalles Bibliográficos
Autores principales: Duan, Meijun, Yang, Hongyu, Wang, Shangping, Liu, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776435/
https://www.ncbi.nlm.nih.gov/pubmed/31581225
http://dx.doi.org/10.1371/journal.pone.0222706
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author Duan, Meijun
Yang, Hongyu
Wang, Shangping
Liu, Yu
author_facet Duan, Meijun
Yang, Hongyu
Wang, Shangping
Liu, Yu
author_sort Duan, Meijun
collection PubMed
description Exploration and exploitation are contradictory in differential evolution (DE) algorithm. In order to balance the search behavior between exploitation and exploration better, a novel self-adaptive dual-strategy differential evolution algorithm (SaDSDE) is proposed. Firstly, a dual-strategy mutation operator is presented based on the “DE/best/2” mutation operator with better global exploration ability and “DE/rand/2” mutation operator with stronger local exploitation ability. Secondly, the scaling factor self-adaption strategy is proposed in an individual-dependent and fitness-dependent way without extra parameters. Thirdly, the exploration ability control factor is introduced to adjust the global exploration ability dynamically in the evolution process. In order to verify and analyze the performance of SaDSDE, we compare SaDSDE with 7 state-of-art DE variants and 3 non-DE based algorithms by using 30 Benchmark test functions of 30-dimensions and 100-dimensions, respectively. The experiments results demonstrate that SaDSDE could improve global optimization performance remarkably. Moreover, the performance superiority of SaDSDE becomes more significant with the increase of the problems’ dimension.
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spelling pubmed-67764352019-10-11 Self-adaptive dual-strategy differential evolution algorithm Duan, Meijun Yang, Hongyu Wang, Shangping Liu, Yu PLoS One Research Article Exploration and exploitation are contradictory in differential evolution (DE) algorithm. In order to balance the search behavior between exploitation and exploration better, a novel self-adaptive dual-strategy differential evolution algorithm (SaDSDE) is proposed. Firstly, a dual-strategy mutation operator is presented based on the “DE/best/2” mutation operator with better global exploration ability and “DE/rand/2” mutation operator with stronger local exploitation ability. Secondly, the scaling factor self-adaption strategy is proposed in an individual-dependent and fitness-dependent way without extra parameters. Thirdly, the exploration ability control factor is introduced to adjust the global exploration ability dynamically in the evolution process. In order to verify and analyze the performance of SaDSDE, we compare SaDSDE with 7 state-of-art DE variants and 3 non-DE based algorithms by using 30 Benchmark test functions of 30-dimensions and 100-dimensions, respectively. The experiments results demonstrate that SaDSDE could improve global optimization performance remarkably. Moreover, the performance superiority of SaDSDE becomes more significant with the increase of the problems’ dimension. Public Library of Science 2019-10-03 /pmc/articles/PMC6776435/ /pubmed/31581225 http://dx.doi.org/10.1371/journal.pone.0222706 Text en © 2019 Duan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Duan, Meijun
Yang, Hongyu
Wang, Shangping
Liu, Yu
Self-adaptive dual-strategy differential evolution algorithm
title Self-adaptive dual-strategy differential evolution algorithm
title_full Self-adaptive dual-strategy differential evolution algorithm
title_fullStr Self-adaptive dual-strategy differential evolution algorithm
title_full_unstemmed Self-adaptive dual-strategy differential evolution algorithm
title_short Self-adaptive dual-strategy differential evolution algorithm
title_sort self-adaptive dual-strategy differential evolution algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776435/
https://www.ncbi.nlm.nih.gov/pubmed/31581225
http://dx.doi.org/10.1371/journal.pone.0222706
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