Comparison of Particle Swarm Optimization and Artificial Fish Swarm Algorithm

This document presents a complete MATLAB implementation for comparing Particle Swarm Optimization (PSO) and Artificial Fish Swarm Algorithm (AFSA). The program includes key algorithmic components such as particle position updates with velocity calculations for PSO, and fish behaviors including prey, swarm, and follow operations for AFSA. Through this implementation, we gain deeper insights into the distinct characteristics of both algorithms, with PSO demonstrating efficient global search capabilities through social learning mechanisms, while AFSA exhibits strong local optimization through its biological inspiration. The performance analysis covers convergence speed, solution quality, and parameter sensitivity, with detailed benchmarking on standard test functions. We also discuss practical application domains where each algorithm excels, along with implementation details covering population initialization, fitness evaluation, and termination criteria. The documentation addresses the program's limitations regarding scalability and parameter tuning requirements. Overall, this work provides a thorough comparative study of PSO and AFSA, offering valuable insights for researchers and practitioners in optimization algorithms.