An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy
Abstract Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, and premature convergence. In this pape...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2022-08-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-022-00827-1 |
| _version_ | 1797863212167725056 |
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| author | Di Cao Yunlang Xu Zhile Yang He Dong Xiaoping Li |
| author_facet | Di Cao Yunlang Xu Zhile Yang He Dong Xiaoping Li |
| author_sort | Di Cao |
| collection | DOAJ |
| description | Abstract Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, and premature convergence. In this paper, a new enhanced WOA (EWOA), which adopts an improved dynamic opposite learning (IDOL) and an adaptive encircling prey stage, is proposed to overcome the problems. IDOL plays an important role in the initialization part and the algorithm iterative process of EWOA. By evaluating the optimal solution in the current population, IDOL can adaptively switch exploitation/exploration modes constructed by the DOL strategy and a modified search strategy, respectively. On the other hand, for the encircling prey stage of EWOA in the latter part of the iteration, an adaptive inertia weight strategy is introduced into this stage to adaptively adjust the prey’s position to avoid falling into local optima. Numerical experiments, with unimodal, multimodal, hybrid and composition benchmarks, and three typical engineering problems are utilized to evaluate the performance of EWOA. The proposed EWOA also evaluates against canonical WOA, three sub-variants of EWOA, three other common algorithms, three advanced algorithms and four advanced variants of WOA. Results indicate that according to Wilcoxon rank sum test and Friedman test, EWOA has balanced exploration and exploitation ability in coping with global optimization, and it has obvious advantages when compared with other state-of-the-art algorithms. |
| first_indexed | 2024-04-09T22:31:56Z |
| format | Article |
| id | doaj.art-9be4a87fa4564400b26cb61ae09ac971 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-09T22:31:56Z |
| publishDate | 2022-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-9be4a87fa4564400b26cb61ae09ac9712023-03-22T12:43:44ZengSpringerComplex & Intelligent Systems2199-45362198-60532022-08-019176779510.1007/s40747-022-00827-1An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategyDi Cao0Yunlang Xu1Zhile Yang2He Dong3Xiaoping Li4State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and TechnologyState Key Laboratory of ASIC and System, School of Microelectronics, Fudan UniversityShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and TechnologyState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and TechnologyAbstract Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, and premature convergence. In this paper, a new enhanced WOA (EWOA), which adopts an improved dynamic opposite learning (IDOL) and an adaptive encircling prey stage, is proposed to overcome the problems. IDOL plays an important role in the initialization part and the algorithm iterative process of EWOA. By evaluating the optimal solution in the current population, IDOL can adaptively switch exploitation/exploration modes constructed by the DOL strategy and a modified search strategy, respectively. On the other hand, for the encircling prey stage of EWOA in the latter part of the iteration, an adaptive inertia weight strategy is introduced into this stage to adaptively adjust the prey’s position to avoid falling into local optima. Numerical experiments, with unimodal, multimodal, hybrid and composition benchmarks, and three typical engineering problems are utilized to evaluate the performance of EWOA. The proposed EWOA also evaluates against canonical WOA, three sub-variants of EWOA, three other common algorithms, three advanced algorithms and four advanced variants of WOA. Results indicate that according to Wilcoxon rank sum test and Friedman test, EWOA has balanced exploration and exploitation ability in coping with global optimization, and it has obvious advantages when compared with other state-of-the-art algorithms.https://doi.org/10.1007/s40747-022-00827-1Whale optimizationInertia weightDynamic opposite learningGlobal optimization |
| spellingShingle | Di Cao Yunlang Xu Zhile Yang He Dong Xiaoping Li An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy Complex & Intelligent Systems Whale optimization Inertia weight Dynamic opposite learning Global optimization |
| title | An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy |
| title_full | An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy |
| title_fullStr | An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy |
| title_full_unstemmed | An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy |
| title_short | An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy |
| title_sort | enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy |
| topic | Whale optimization Inertia weight Dynamic opposite learning Global optimization |
| url | https://doi.org/10.1007/s40747-022-00827-1 |
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