Vulture model is a heuristic algorithm often mentioned in spatial intelligent optimization algorithms, inspired by the behavior of vulture hunting in nature. This model is particularly suitable for global optimization in complex search space, and can effectively find the optimal solution to the problem.

In this model, vultures are regarded as individuals in the search process, and they maintain their survival through the perception and strategic adjustment of food resources in the environment. Each individual in the model represents a feasible solution, and the individuals guide the search process by competing and cooperating with each other. Specifically, the vulture's adaptive ability is reflected in its response to environmental information, such as staying in areas where food is abundant or moving quickly to areas with more potential. This feedback-based search method makes the vulture model have strong flexibility and adaptability when dealing with complex optimization problems.

The working steps of vulture model usually include initialization, evaluation, updating and selection. The initialization stage generates a certain number of vulture individuals randomly distributed in the search space, and these individuals represent the initial solution. Then through the evaluation stage, individuals are scored according to the objective function, survival of the fittest, and an excellent solution set is formed. In the update phase, individuals adjust their position based on their position, speed, and environmental feedback to explore a better solution. In the selection stage, it is necessary to determine the next search strategy according to the fitness of the individual, so that the better individual can occupy the dominant position in the whole iteration process.