Gray Whale Optimization Algorithm
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866902051217211392 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>Swarm intelligence optimization algorithms, due to their simple structure, strong robustness, and good adaptability to complex nonlinear problems, have been widely used in continuous function optimization, feature selection, and engineering optimization. Gray whale optimization algorithms are a class of metaheuristic algorithms based on the predatory behavior of gray whales in nature, possessing fewer control parameters and better global search capabilities. However, traditional gray whale optimization algorithms still suffer from problems such as homogeneous individual behavior, coarse division between exploration and development stages, and susceptibility to local optima. To address these shortcomings, this paper proposes an adaptive multi-stage gray whale optimization algorithm. This method constructs a multi-stage cognitive evolution mechanism, dividing the search process into an exploration and migration stage, a cooperative predation stage, and a fine-grained memory reinforcement stage, and introduces a stage switching criterion based on swarm information entropy to achieve adaptive adjustment of search behavior. Simultaneously, the algorithm introduces a role-differentiated whale pod model, classifying individuals into lead whales, scout whales, and follower whales to enhance swarm diversity. Furthermore, a cross-generational knowledge distillation migration mechanism and a failure-aware learning mechanism are proposed to improve the algorithm's ability to escape local optima and its convergence stability. Theoretical analysis shows that, while maintaining the original concise structure of the Gray Whale Optimization Algorithm, this algorithm effectively enhances the dynamic adaptability and global exploration capability of the search process</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18358007 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Gray Whale Optimization Algorithm Zhang, Jincheng <p><span>Swarm intelligence optimization algorithms, due to their simple structure, strong robustness, and good adaptability to complex nonlinear problems, have been widely used in continuous function optimization, feature selection, and engineering optimization. Gray whale optimization algorithms are a class of metaheuristic algorithms based on the predatory behavior of gray whales in nature, possessing fewer control parameters and better global search capabilities. However, traditional gray whale optimization algorithms still suffer from problems such as homogeneous individual behavior, coarse division between exploration and development stages, and susceptibility to local optima. To address these shortcomings, this paper proposes an adaptive multi-stage gray whale optimization algorithm. This method constructs a multi-stage cognitive evolution mechanism, dividing the search process into an exploration and migration stage, a cooperative predation stage, and a fine-grained memory reinforcement stage, and introduces a stage switching criterion based on swarm information entropy to achieve adaptive adjustment of search behavior. Simultaneously, the algorithm introduces a role-differentiated whale pod model, classifying individuals into lead whales, scout whales, and follower whales to enhance swarm diversity. Furthermore, a cross-generational knowledge distillation migration mechanism and a failure-aware learning mechanism are proposed to improve the algorithm's ability to escape local optima and its convergence stability. Theoretical analysis shows that, while maintaining the original concise structure of the Gray Whale Optimization Algorithm, this algorithm effectively enhances the dynamic adaptability and global exploration capability of the search process</span>.</p> |
| title | Gray Whale Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.18358007 |