Pilot Whale Optimization Algorithm
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| Formato: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901198792032256 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>Swarm intelligence optimization algorithms have been widely applied in complex nonlinear optimization problems due to their simple structure and strong adaptability. However, most existing biomimetic optimization algorithms still suffer from problems such as passive leader individuals, fixed group structure, static parameters, and limited ability to escape local optima during the search process. To address these shortcomings, this paper proposes a novel pilot whale optimization algorithm. Based on the social behavior of a pilot whale swarm, this algorithm introduces cognitive modeling, dynamic hierarchical partitioning, information entropy-driven search, and a risk-perception decision-making mechanism to construct a swarm optimization framework with dual-timescale evolutionary characteristics. By endowing the leader individual with cognitive attributes such as memory, risk preference, and directional stability, the algorithm enables the search process to have adaptive guidance capabilities; simultaneously, it utilizes group information entropy to characterize search uncertainty, achieving a continuously adjustable balance between exploration and development. Theoretical analysis shows that this method can effectively improve convergence stability while maintaining group diversity and possesses good scalability. This algorithm provides a new interpretable swarm intelligence solution approach for complex optimization problems</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18358027 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Pilot Whale Optimization Algorithm Zhang, Jincheng <p><span>Swarm intelligence optimization algorithms have been widely applied in complex nonlinear optimization problems due to their simple structure and strong adaptability. However, most existing biomimetic optimization algorithms still suffer from problems such as passive leader individuals, fixed group structure, static parameters, and limited ability to escape local optima during the search process. To address these shortcomings, this paper proposes a novel pilot whale optimization algorithm. Based on the social behavior of a pilot whale swarm, this algorithm introduces cognitive modeling, dynamic hierarchical partitioning, information entropy-driven search, and a risk-perception decision-making mechanism to construct a swarm optimization framework with dual-timescale evolutionary characteristics. By endowing the leader individual with cognitive attributes such as memory, risk preference, and directional stability, the algorithm enables the search process to have adaptive guidance capabilities; simultaneously, it utilizes group information entropy to characterize search uncertainty, achieving a continuously adjustable balance between exploration and development. Theoretical analysis shows that this method can effectively improve convergence stability while maintaining group diversity and possesses good scalability. This algorithm provides a new interpretable swarm intelligence solution approach for complex optimization problems</span>.</p> |
| title | Pilot Whale Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.18358027 |