An improved multi-strategy beluga whale optimization for global optimization problems
This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergen...
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ftdoajarticles:oai:doaj.org/article:b5aa457ed72a480e803bb938b12e0358 2023-07-23T04:18:35+02:00 An improved multi-strategy beluga whale optimization for global optimization problems Hongmin Chen Zhuo Wang Di Wu Heming Jia Changsheng Wen Honghua Rao Laith Abualigah 2023-06-01T00:00:00Z https://doi.org/10.3934/mbe.2023592 https://doaj.org/article/b5aa457ed72a480e803bb938b12e0358 EN eng AIMS Press https://www.aimspress.com/article/doi/10.3934/mbe.2023592?viewType=HTML https://doaj.org/toc/1551-0018 doi:10.3934/mbe.2023592 1551-0018 https://doaj.org/article/b5aa457ed72a480e803bb938b12e0358 Mathematical Biosciences and Engineering, Vol 20, Iss 7, Pp 13267-13317 (2023) beluga whale optimization group action dynamic pinhole imaging strategy quadratic interpolation strategy engineering problems Biotechnology TP248.13-248.65 Mathematics QA1-939 article 2023 ftdoajarticles https://doi.org/10.3934/mbe.2023592 2023-07-02T00:36:26Z This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems. Article in Journal/Newspaper Beluga Beluga whale Beluga* Directory of Open Access Journals: DOAJ Articles Mathematical Biosciences and Engineering 20 7 13267 13317 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
beluga whale optimization group action dynamic pinhole imaging strategy quadratic interpolation strategy engineering problems Biotechnology TP248.13-248.65 Mathematics QA1-939 |
spellingShingle |
beluga whale optimization group action dynamic pinhole imaging strategy quadratic interpolation strategy engineering problems Biotechnology TP248.13-248.65 Mathematics QA1-939 Hongmin Chen Zhuo Wang Di Wu Heming Jia Changsheng Wen Honghua Rao Laith Abualigah An improved multi-strategy beluga whale optimization for global optimization problems |
topic_facet |
beluga whale optimization group action dynamic pinhole imaging strategy quadratic interpolation strategy engineering problems Biotechnology TP248.13-248.65 Mathematics QA1-939 |
description |
This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems. |
format |
Article in Journal/Newspaper |
author |
Hongmin Chen Zhuo Wang Di Wu Heming Jia Changsheng Wen Honghua Rao Laith Abualigah |
author_facet |
Hongmin Chen Zhuo Wang Di Wu Heming Jia Changsheng Wen Honghua Rao Laith Abualigah |
author_sort |
Hongmin Chen |
title |
An improved multi-strategy beluga whale optimization for global optimization problems |
title_short |
An improved multi-strategy beluga whale optimization for global optimization problems |
title_full |
An improved multi-strategy beluga whale optimization for global optimization problems |
title_fullStr |
An improved multi-strategy beluga whale optimization for global optimization problems |
title_full_unstemmed |
An improved multi-strategy beluga whale optimization for global optimization problems |
title_sort |
improved multi-strategy beluga whale optimization for global optimization problems |
publisher |
AIMS Press |
publishDate |
2023 |
url |
https://doi.org/10.3934/mbe.2023592 https://doaj.org/article/b5aa457ed72a480e803bb938b12e0358 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Mathematical Biosciences and Engineering, Vol 20, Iss 7, Pp 13267-13317 (2023) |
op_relation |
https://www.aimspress.com/article/doi/10.3934/mbe.2023592?viewType=HTML https://doaj.org/toc/1551-0018 doi:10.3934/mbe.2023592 1551-0018 https://doaj.org/article/b5aa457ed72a480e803bb938b12e0358 |
op_doi |
https://doi.org/10.3934/mbe.2023592 |
container_title |
Mathematical Biosciences and Engineering |
container_volume |
20 |
container_issue |
7 |
container_start_page |
13267 |
op_container_end_page |
13317 |
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1772180984172642304 |