MATLAB Implementation of Artificial Fish Swarm Algorithm with Code Description

This document provides a comprehensive explanation of the principles and characteristics of the Artificial Fish Swarm Algorithm (AFSA). AFSA is an optimization technique that simulates three fundamental fish behaviors: foraging, swarming, and chasing. The algorithm's foundation lies in observing how fish naturally locate nutrient-rich areas in water bodies, either independently or by following other fish, with the highest fish concentration typically indicating the most nutrient-dense regions. First, let's examine the foraging behavior. Normally, fish swim randomly in water, but when they detect food, they quickly move toward directions with increasing food concentration. In code implementation, this is typically achieved through random position updates combined with gradient-based movement toward better solutions using fitness evaluation functions. Second, we explore the swarming behavior. For survival and protection against threats, fish naturally form groups during movement. The swarming mechanism follows three key rules implemented through distance calculations and position updates: Separation Rule prevents overcrowding by maintaining minimum distances between artificial fish using Euclidean distance checks; Alignment Rule ensures directional consistency by averaging movement vectors of neighboring fish; Cohesion Rule maintains group integrity by steering fish toward the centroid of nearby companions using center-of-mass calculations. Finally, the chasing behavior occurs when one or several fish discover food, prompting nearby companions to follow quickly toward the food source. In algorithm terms, this translates to tracking the best-performing solutions in the population through leader-follower mechanisms. AFSA possesses several distinctive characteristics: 1) Fast convergence speed making it suitable for real-time applications, implemented through efficient population update mechanisms; 2) Rapid feasible solution generation for scenarios with moderate precision requirements using simplified fitness evaluations; 3) Broad applicability since it doesn't require strict mechanistic models or precise problem descriptions, leveraging heuristic search patterns. When implementing AFSA, termination conditions must be properly configured. Common stopping criteria include: 1) Checking if the mean square error of consecutive solutions falls below a specified tolerance threshold; 2) Verifying if the artificial fish population density in certain regions reaches a predetermined ratio through population distribution analysis; 3) Determining if obtained values consistently exceed identified extrema over multiple iterations. Through this explanation, we aim to provide deeper understanding of AFSA's theoretical foundation and practical implementation considerations.