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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.16875165 |
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| _version_ | 1866902141887578112 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>This paper proposes a novel meta-heuristic optimization algorithm, the Breakthrough Red Fox Optimizer (RFBO). This algorithm simulates the agile hunting behavior of red foxes in natural environments and combines olfactory memory enhancement, chaotic policy switching, group collaborative differential learning, and multi-sensory fusion mechanisms to solve complex, high-dimensional, non-convex optimization problems. Through mathematical modeling and algorithmic analysis, RFBO demonstrates its groundbreaking advantages in global search capability, convergence speed, and multi-feature adaptability</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16875165 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Red Fox Breakthrough Optimization, RFBO Zhang, Jincheng <p><span>This paper proposes a novel meta-heuristic optimization algorithm, the Breakthrough Red Fox Optimizer (RFBO). This algorithm simulates the agile hunting behavior of red foxes in natural environments and combines olfactory memory enhancement, chaotic policy switching, group collaborative differential learning, and multi-sensory fusion mechanisms to solve complex, high-dimensional, non-convex optimization problems. Through mathematical modeling and algorithmic analysis, RFBO demonstrates its groundbreaking advantages in global search capability, convergence speed, and multi-feature adaptability</span>.</p> |
| title | Red Fox Breakthrough Optimization, RFBO |
| url | https://doi.org/10.5281/zenodo.16875165 |