Collective Animal Behavior (CAB) is a modern metaheuristic algorithm designed for global optimization; it is examined in detail in this chapter. Different animals, wildebeest herds, such as bird flocks, fish schools, and locust swarms, display behaviors such as gathering around food, congregating near central locations, or migrating in synchronized patterns over extended distances. These collective actions often provide significant advantages, such as enhancing harvesting efficiency, following optimal migration routes, improving aerodynamics, and evading predators. The algorithm simulates search agents by replicating the interactions of a group of animals based on biological principles of collective movement. This method is compared with leading optimization algorithms, and the results emphasize its remarkable capacity to locate the global optimum across various benchmark functions.