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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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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. |
format | Online Article Text |
id | pubmed-6776435 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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