MATLAB Implementation of Artificial Fish Swarm Algorithm

This document presents MATLAB implementations of the Artificial Fish Swarm Algorithm (AFSA), a highly intelligent optimization method gaining widespread application across multiple fields. AFSA simulates natural fish swarm behaviors, particularly foraging patterns, to solve optimization problems through collective intelligence mechanisms. The algorithm can be effectively applied to various domains including image processing, machine learning, and data mining. Key MATLAB implementation aspects typically include fish position initialization, visualization functions for swarm movement tracking, and core behavioral simulations such as prey-seeking, swarming, and following behaviors. Due to its computational efficiency and flexibility in handling constraints, AFSA demonstrates excellent performance in solving complex optimization challenges. Consequently, researchers and engineers are increasingly adopting this algorithm with MATLAB implementations to address diverse real-world problems, leveraging its population-based search strategy and parallel processing capabilities for faster convergence to optimal solutions.