Public health expert heuristic global optimization algorithm

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.