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Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies

The black widow spider optimization algorithm (BWOA) had the problems of slow convergence speed and easily to falling into local optimum mode. To address these problems, this paper proposes a multi-strategy black widow spider optimization algorithm (IBWOA). First, Gauss chaotic mapping is introduced...

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Autores principales: Wan, Chenxin, He, Bitao, Fan, Yuancheng, Tan, Wei, Qin, Tao, Yang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689898/
https://www.ncbi.nlm.nih.gov/pubmed/36421495
http://dx.doi.org/10.3390/e24111640
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author Wan, Chenxin
He, Bitao
Fan, Yuancheng
Tan, Wei
Qin, Tao
Yang, Jing
author_facet Wan, Chenxin
He, Bitao
Fan, Yuancheng
Tan, Wei
Qin, Tao
Yang, Jing
author_sort Wan, Chenxin
collection PubMed
description The black widow spider optimization algorithm (BWOA) had the problems of slow convergence speed and easily to falling into local optimum mode. To address these problems, this paper proposes a multi-strategy black widow spider optimization algorithm (IBWOA). First, Gauss chaotic mapping is introduced to initialize the population to ensure the diversity of the algorithm at the initial stage. Then, the sine cosine strategy is introduced to perturb the individuals during iteration to improve the global search ability of the algorithm. In addition, the elite opposition-based learning strategy is introduced to improve convergence speed of algorithm. Finally, the mutation method of the differential evolution algorithm is integrated to reorganize the individuals with poor fitness values. Through the analysis of the optimization results of 13 benchmark test functions and a part of CEC2017 test functions, the effectiveness and rationality of each improved strategy are verified. Moreover, it shows that the proposed algorithm has significant improvement in solution accuracy, performance and convergence speed compared with other algorithms. Furthermore, the IBWOA algorithm is used to solve six practical constrained engineering problems. The results show that the IBWOA has excellent optimization ability and scalability.
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spelling pubmed-96898982022-11-25 Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies Wan, Chenxin He, Bitao Fan, Yuancheng Tan, Wei Qin, Tao Yang, Jing Entropy (Basel) Article The black widow spider optimization algorithm (BWOA) had the problems of slow convergence speed and easily to falling into local optimum mode. To address these problems, this paper proposes a multi-strategy black widow spider optimization algorithm (IBWOA). First, Gauss chaotic mapping is introduced to initialize the population to ensure the diversity of the algorithm at the initial stage. Then, the sine cosine strategy is introduced to perturb the individuals during iteration to improve the global search ability of the algorithm. In addition, the elite opposition-based learning strategy is introduced to improve convergence speed of algorithm. Finally, the mutation method of the differential evolution algorithm is integrated to reorganize the individuals with poor fitness values. Through the analysis of the optimization results of 13 benchmark test functions and a part of CEC2017 test functions, the effectiveness and rationality of each improved strategy are verified. Moreover, it shows that the proposed algorithm has significant improvement in solution accuracy, performance and convergence speed compared with other algorithms. Furthermore, the IBWOA algorithm is used to solve six practical constrained engineering problems. The results show that the IBWOA has excellent optimization ability and scalability. MDPI 2022-11-11 /pmc/articles/PMC9689898/ /pubmed/36421495 http://dx.doi.org/10.3390/e24111640 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wan, Chenxin
He, Bitao
Fan, Yuancheng
Tan, Wei
Qin, Tao
Yang, Jing
Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies
title Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies
title_full Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies
title_fullStr Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies
title_full_unstemmed Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies
title_short Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies
title_sort improved black widow spider optimization algorithm integrating multiple strategies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689898/
https://www.ncbi.nlm.nih.gov/pubmed/36421495
http://dx.doi.org/10.3390/e24111640
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