Artificial Fish Swarm Algorithm: An Autonomous Optimization Model Inspired by Animal Behavior

The Artificial Fish Swarm Algorithm (AFSA) is an intelligent optimization technique inspired by the collective behavior of fish schools. This algorithm simulates the intelligent characteristics displayed during fish behaviors such as foraging, swarming, and chasing, achieving global optimization through simple interactions between autonomous individuals.

In the algorithm implementation, each "artificial fish" operates as an autonomous agent capable of making independent decisions based on local environmental information. The core algorithm typically involves three main behavioral modes:

1. Foraging Behavior: Simulates fish movement toward areas with higher food concentration. Code implementation often uses a stochastic search strategy where fish move toward positions with better fitness values, incorporating random steps to maintain exploration capability.

2. Swarming Behavior: Maintains appropriate distances between individuals to prevent overcrowding or excessive dispersion. This is implemented through distance thresholds and crowding factors in the code, ensuring balanced exploration-exploitation trade-offs.

3. Chasing Behavior: A learning mechanism where individuals follow neighboring agents with better fitness values. The code implementation typically involves comparing fitness values within visual distance and moving toward optimal neighbors.

This algorithm demonstrates particular effectiveness in solving nonlinear, multimodal complex optimization problems. Compared to traditional optimization methods, AFSA exhibits superior global search capability and robustness, effectively avoiding premature convergence to local optima through its distributed decision-making architecture.

The Artificial Fish Swarm Algorithm has been successfully applied in various domains including path planning, parameter optimization, and neural network training, showcasing the unique advantages of swarm intelligence in addressing complex computational challenges. The algorithm's implementation typically involves parameters like visual distance, step size, and crowd factor, which require careful tuning for optimal performance across different problem domains.