Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems
Abstract Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. Ho...
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| 格式: | Article |
| 語言: | English |
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SpringerOpen
2024-01-01
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| 叢編: | Journal of Big Data |
| 主題: | |
| 在線閱讀: | https://doi.org/10.1186/s40537-023-00864-8 |
| _version_ | 1827388375361912832 |
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| author | Jiaxu Huang Haiqing Hu |
| author_facet | Jiaxu Huang Haiqing Hu |
| author_sort | Jiaxu Huang |
| collection | DOAJ |
| description | Abstract Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods. |
| first_indexed | 2024-03-08T16:16:56Z |
| format | Article |
| id | doaj.art-01c1bf13d60b45e2bcb87b270e3c61d2 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-08T16:16:56Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-01c1bf13d60b45e2bcb87b270e3c61d22024-01-07T12:29:51ZengSpringerOpenJournal of Big Data2196-11152024-01-0111115510.1186/s40537-023-00864-8Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problemsJiaxu Huang0Haiqing Hu1School of Economics and Management, Xi’an University of TechnologySchool of Economics and Management, Xi’an University of TechnologyAbstract Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods.https://doi.org/10.1186/s40537-023-00864-8Beluga whale optimizationQuasi-oppositional based learningThe adaptive and spiral predation strategiesNelder-Mead simplex searchEngineering design |
| spellingShingle | Jiaxu Huang Haiqing Hu Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems Journal of Big Data Beluga whale optimization Quasi-oppositional based learning The adaptive and spiral predation strategies Nelder-Mead simplex search Engineering design |
| title | Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems |
| title_full | Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems |
| title_fullStr | Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems |
| title_full_unstemmed | Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems |
| title_short | Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems |
| title_sort | hybrid beluga whale optimization algorithm with multi strategy for functions and engineering optimization problems |
| topic | Beluga whale optimization Quasi-oppositional based learning The adaptive and spiral predation strategies Nelder-Mead simplex search Engineering design |
| url | https://doi.org/10.1186/s40537-023-00864-8 |
| work_keys_str_mv | AT jiaxuhuang hybridbelugawhaleoptimizationalgorithmwithmultistrategyforfunctionsandengineeringoptimizationproblems AT haiqinghu hybridbelugawhaleoptimizationalgorithmwithmultistrategyforfunctionsandengineeringoptimizationproblems |