The leopard model is a computational model based on biological inspiration, which is mainly used to solve complex optimization problems. The core idea is derived from the leopard's hunting strategy in nature, emphasizing the dynamic adjustment of speed, flexibility and strategy. The leopard's hunting process shows how to find the best prey in a changing environment, so the leopard's model is widely used in artificial intelligence, machine learning and other computing fields.

In the leopard model, the behavior and interactions of individuals are designed to mimic the decision-making process of leopards when hunting. Each individual (the "leopard") moves through the search space to find the optimal solution. They adjust their direction based on their own experience and the experience of other individuals, and this information sharing and cooperation allows the entire system to explore solutions more efficiently.

The key of this model is to balance local search and global search capabilities. In local search, Leopards fine-tune around known good solutions to find better ones; On the other hand, in a global search, the leopard will try to explore further areas to avoid getting caught in the local optimal solution. This dual search mechanism enables the leopard model to better adapt to different environments and needs when dealing with complex problems.

Leopard model is widely used in many fields, including data mining, image processing, optimization algorithm, etc. In data mining, the leopard model can effectively discover the potential patterns and relationships in data. In the field of image processing, it can be used to optimize the process of image recognition and classification. In the optimization algorithm, leopard model is often used as the best solution of multi-dimensional complex function, showing strong computing power.