Fish schooling is a common but fascinating sight in nature, and simulating the motion of fish flocks has been an essential part of recent films and video games. However, compared to the fundamental boids simulation, there are usually more factors to consider in simulating fish flocks, such as obstacle avoidance, predator avoidance, eating food, and mating. Furthermore, different types of fish may exhibit distinct behaviors, which is also not considered in the classical boids simulation. Thus in this project, we focus on exploring a more advanced fish group simulation based on the boids simulation algorithm and design three different types of fish for behavior difference study, including prey, predator, and the pacifist. Each fish species owns a unique behavior pattern, and a natural underwater ecosystem can be formed by combining them. Moreover, we conduct comprehensive experiments to evaluate our proposed behavior modeling and demonstrate a vivid fish group simulation.
