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

The Artificial Fish Swarm Algorithm (AFSA) is an optimization technique modeled after the collective behavior of fish schools. This algorithm simulates how individual fish navigate their environment by seeking food sources and avoiding threats through local interactions and information sharing within the group. Key behavioral components implemented in code include: - Preying behavior: Fish move toward areas with higher fitness values - Swarming: Individuals maintain proximity to neighbors while avoiding overcrowding - Following: Fish track peers who discover promising regions The algorithm's decentralized architecture makes it particularly effective for global optimization problems, with implementations typically involving parameters for visual range, step size, and crowding factor. AFSA has demonstrated robust applications across multiple domains including engineering optimization, machine learning hyperparameter tuning, and data mining pattern discovery. Notable advantages include straightforward implementation through basic movement rules, strong global search capabilities that avoid local optima, and minimal requirements for initial problem information. These characteristics establish AFSA as a versatile tool for solving diverse optimization challenges, with Python/Matlab implementations often featuring core functions for fitness evaluation, neighbor detection, and position updating.