Canadian Lynx Optimization Algorithm
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Zenodo
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901161365209088 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>To address the problems of premature convergence, rapid loss of search diversity, and insufficient response to environmental changes that traditional swarm intelligence optimization algorithms often suffer from in complex, multi-modal, and high-dimensional continuous optimization problems, this paper proposes a novel swarm intelligence optimization method—the Canada lynx optimization algorithm. Based on the hunting behavior and ecological characteristics of the Canada lynx, this algorithm achieves an adaptive balance between exploration and exploitation capabilities by constructing an ecological feedback-driven search mechanism, a multi-scale jumping hunting strategy, a memory-inherited migration mechanism, and an energy-driven individual control model. The algorithm introduces a group-level ecological feedback index to characterize the search environment state and dynamically adjusts the probability of individual behaviors accordingly, thus avoiding the search rigidity problem caused by fixed parameters. Theoretical analysis shows that the algorithm has good global search capabilities and stable convergence characteristics, providing a new approach to solving complex optimization problems</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18366216 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Canadian Lynx Optimization Algorithm Zhang, Jincheng <p><span>To address the problems of premature convergence, rapid loss of search diversity, and insufficient response to environmental changes that traditional swarm intelligence optimization algorithms often suffer from in complex, multi-modal, and high-dimensional continuous optimization problems, this paper proposes a novel swarm intelligence optimization method—the Canada lynx optimization algorithm. Based on the hunting behavior and ecological characteristics of the Canada lynx, this algorithm achieves an adaptive balance between exploration and exploitation capabilities by constructing an ecological feedback-driven search mechanism, a multi-scale jumping hunting strategy, a memory-inherited migration mechanism, and an energy-driven individual control model. The algorithm introduces a group-level ecological feedback index to characterize the search environment state and dynamically adjusts the probability of individual behaviors accordingly, thus avoiding the search rigidity problem caused by fixed parameters. Theoretical analysis shows that the algorithm has good global search capabilities and stable convergence characteristics, providing a new approach to solving complex optimization problems</span>.</p> |
| title | Canadian Lynx Optimization Algorithm |
| url | https://doi.org/10.5281/zenodo.18366216 |