The Vulture model is a swarm intelligence optimization algorithm inspired by natural ecology, which is mainly applied to solve complex optimization problems. The model draws on the behavioral characteristics of vultures during foraging and hunting, simulating how they find food in adverse environments to guide the search process. Vultures are known for their keen observation and strong adaptability, capable of finding prey over a wide area and displaying efficient survival strategies.

In the vulture model, the individual represents the candidate solution in the algorithm, while the entire population forms the understanding space. The core idea of the algorithm is to realize the continuous exploration and optimization of the optimal solution by simulating the foraging behavior of vultures. In the process of finding food, each individual will adjust itself according to the information of other individuals, thereby improving its own fitness. Specifically, individuals assess their own fitness with the environment, and exchange information with each other to optimize search strategies.

The key steps of the model include initializing the population, evaluating fitness, updating the location, and selecting the optimal solution. At initialization, all individuals are randomly distributed in the solution space. The model then guides the individual towards the potential optimal solution by evaluating the individual's fitness value and adding random factors. In this process, the optimization process will continue to iterate and gradually converge to the optimal solution.

The vulture model has a strong global search ability and can effectively avoid falling into local optimal solutions. This is mainly due to the randomness introduced and the information sharing among populations, which enables individuals to effectively explore in a vast solution space.