MATLAB Implementation of Artificial Fish Swarm Algorithm with Code Descriptions

The Artificial Fish Swarm Algorithm (AFSA) is an optimization technique based on swarm intelligence, inspired by fish behaviors including foraging, swarming, and following. This algorithm solves optimization problems by simulating these behaviors, offering advantages such as strong global search capability and fast convergence. Implementing AFSA in MATLAB primarily involves these key steps with corresponding code implementations: Initialization of Fish Population: Configure fish count, positions, visual range, and parameters like trial numbers and crowding factors. Each fish's position represents a potential solution to the problem. In MATLAB code, this typically involves creating position matrices using rand() or randn() functions and defining parameter structures. Behavior Simulation: The algorithm simulates three main behaviors through separate function modules: - Foraging Behavior: Fish move toward directions with higher food concentration (better fitness values). Code implementation involves calculating fitness differences and updating positions using vectorized operations. - Swarming Behavior: Fish tend to move toward the center of nearby fish groups to avoid isolation. This is implemented by calculating centroid positions within visual range using mean() or sum() functions. - Following Behavior: Fish follow the best-performing neighbor to accelerate convergence. The code identifies optimal neighbors through min() or max() functions applied to fitness arrays. Position Update: Calculate new positions based on behavior strategies and evaluate fitness values at new locations using objective function calls. If the new position yields better fitness (determined by comparison operators), update the position matrix accordingly. Termination Conditions: The algorithm typically terminates when reaching maximum iterations (controlled by for/while loops) or when fitness values stabilize (monitored through difference thresholds). MATLAB implementation leverages matrix operations for computational efficiency through vectorization. Key functions often include: - arrayfun() or vectorized calculations for parallel fitness evaluations - pdist2() for efficient distance computations between fish - logical indexing for neighbor identification within visual range By adjusting parameters like visual range and step size (implemented as tunable variables), the algorithm's search performance can be optimized for various continuous optimization problems. The code structure typically involves main loops containing behavior selection logic, position updates, and convergence checking mechanisms.