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An accelerated sine mapping whale optimizer for feature selection
An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wo...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582515/ https://www.ncbi.nlm.nih.gov/pubmed/37860760 http://dx.doi.org/10.1016/j.isci.2023.107896 |
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author | Yu, Helong Zhao, Zisong Heidari, Ali Asghar Ma, Li Hamdi, Monia Mansour, Romany F. Chen, Huiling |
author_facet | Yu, Helong Zhao, Zisong Heidari, Ali Asghar Ma, Li Hamdi, Monia Mansour, Romany F. Chen, Huiling |
author_sort | Yu, Helong |
collection | PubMed |
description | An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wormhole search (WS) strengthens the capacity for exploitation. The hybrid design effectively reinforces the optimization capability of SWEWOA. To prove the effectiveness of the design, SWEWOA is performed in two test sets, CEC 2017 and 2022, respectively. The advantage of SWEWOA is demonstrated in 26 superior comparison algorithms. Then a new feature selection method called BSWEWOA-KELM is developed based on the binary SWEWOA and kernel extreme learning machine (KELM). To verify its performance, 8 high-performance algorithms are selected and experimentally studied in 16 public datasets of different difficulty. The test results demonstrate that SWEWOA performs excellently in selecting the most valuable features for classification problems. |
format | Online Article Text |
id | pubmed-10582515 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-105825152023-10-19 An accelerated sine mapping whale optimizer for feature selection Yu, Helong Zhao, Zisong Heidari, Ali Asghar Ma, Li Hamdi, Monia Mansour, Romany F. Chen, Huiling iScience Article An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wormhole search (WS) strengthens the capacity for exploitation. The hybrid design effectively reinforces the optimization capability of SWEWOA. To prove the effectiveness of the design, SWEWOA is performed in two test sets, CEC 2017 and 2022, respectively. The advantage of SWEWOA is demonstrated in 26 superior comparison algorithms. Then a new feature selection method called BSWEWOA-KELM is developed based on the binary SWEWOA and kernel extreme learning machine (KELM). To verify its performance, 8 high-performance algorithms are selected and experimentally studied in 16 public datasets of different difficulty. The test results demonstrate that SWEWOA performs excellently in selecting the most valuable features for classification problems. Elsevier 2023-09-14 /pmc/articles/PMC10582515/ /pubmed/37860760 http://dx.doi.org/10.1016/j.isci.2023.107896 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yu, Helong Zhao, Zisong Heidari, Ali Asghar Ma, Li Hamdi, Monia Mansour, Romany F. Chen, Huiling An accelerated sine mapping whale optimizer for feature selection |
title | An accelerated sine mapping whale optimizer for feature selection |
title_full | An accelerated sine mapping whale optimizer for feature selection |
title_fullStr | An accelerated sine mapping whale optimizer for feature selection |
title_full_unstemmed | An accelerated sine mapping whale optimizer for feature selection |
title_short | An accelerated sine mapping whale optimizer for feature selection |
title_sort | accelerated sine mapping whale optimizer for feature selection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582515/ https://www.ncbi.nlm.nih.gov/pubmed/37860760 http://dx.doi.org/10.1016/j.isci.2023.107896 |
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