Public health expert heuristic global optimization algorithm
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2025
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| _version_ | 1866902059715919872 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>This paper proposes a global optimization algorithm inspired by the decision-making logic of public health experts. When addressing health crises, public health experts achieve optimal intervention through multi-layered decision-making, risk assessment, information dissemination, resource allocation, and feedback adjustment. This paper abstracts these strategies into a search mechanism for the optimization algorithm, introducing five unique mechanisms: hierarchical decision-making, epidemic-like information dissemination, delayed incubation period updates, adaptive resource allocation with resource constraints, and dynamic risk-aware weighting. This results in a dynamically adaptive optimization method that combines global exploration with local exploitation. This paper provides a complete mathematical description of the algorithm, along with its mechanism design and iterative process, aiming to provide a new heuristic strategy framework for complex optimization problems</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16891829 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Public health expert heuristic global optimization algorithm Zhang, Jincheng <p><span>This paper proposes a global optimization algorithm inspired by the decision-making logic of public health experts. When addressing health crises, public health experts achieve optimal intervention through multi-layered decision-making, risk assessment, information dissemination, resource allocation, and feedback adjustment. This paper abstracts these strategies into a search mechanism for the optimization algorithm, introducing five unique mechanisms: hierarchical decision-making, epidemic-like information dissemination, delayed incubation period updates, adaptive resource allocation with resource constraints, and dynamic risk-aware weighting. This results in a dynamically adaptive optimization method that combines global exploration with local exploitation. This paper provides a complete mathematical description of the algorithm, along with its mechanism design and iterative process, aiming to provide a new heuristic strategy framework for complex optimization problems</span>.</p> |
| title | Public health expert heuristic global optimization algorithm |
| url | https://doi.org/10.5281/zenodo.16891829 |