Black Stork Optimization Algorithm
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| Formato: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901994797531136 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>Swarm optimization algorithms, by simulating the collaborative behavior of individuals in nature, exhibit strong adaptability in continuous optimization and complex search problems. However, most existing algorithms suffer from problems such as memory loss, over-convergence, and simplistic stagnation handling mechanisms during the search process. To address these shortcomings, this paper proposes a novel swarm optimization method—the Black Stork Optimization Algorithm. This algorithm is modeled after the low-density foraging, long-term memory, and event-driven migration behavior of black storks in wetland environments, constructing a search mechanism from three levels: behavioral, informational, and structural. The algorithm achieves long-term accumulation of experience in the search space by introducing a wetland memory map, alleviates crowding and conflict among individuals near the optimal area through behavioral inhibition mechanisms, and selectively reconstructs the population structure during long-term stagnation by triggering search strategies through rare events. This method constructs an optimization framework with strong interpretability and scalability without relying on complex mathematical assumptions or additional experimental control, providing a new approach to behavioral modeling of swarm intelligence algorithms</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18366495 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Black Stork Optimization Algorithm Zhang, Jincheng <p><span>Swarm optimization algorithms, by simulating the collaborative behavior of individuals in nature, exhibit strong adaptability in continuous optimization and complex search problems. However, most existing algorithms suffer from problems such as memory loss, over-convergence, and simplistic stagnation handling mechanisms during the search process. To address these shortcomings, this paper proposes a novel swarm optimization method—the Black Stork Optimization Algorithm. This algorithm is modeled after the low-density foraging, long-term memory, and event-driven migration behavior of black storks in wetland environments, constructing a search mechanism from three levels: behavioral, informational, and structural. The algorithm achieves long-term accumulation of experience in the search space by introducing a wetland memory map, alleviates crowding and conflict among individuals near the optimal area through behavioral inhibition mechanisms, and selectively reconstructs the population structure during long-term stagnation by triggering search strategies through rare events. This method constructs an optimization framework with strong interpretability and scalability without relying on complex mathematical assumptions or additional experimental control, providing a new approach to behavioral modeling of swarm intelligence algorithms</span>.</p> |
| title | Black Stork Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.18366495 |