MATLAB Toolbox for Particle Swarm Optimization (PSO) Algorithm

This document presents the MATLAB toolbox for Particle Swarm Optimization (PSO) algorithm along with comprehensive usage instructions. This toolbox serves as a powerful computational framework for solving diverse optimization problems across various domains. It incorporates multiple customizable features and parameter configurations that allow flexible adaptation to specific optimization requirements. The toolbox provides core functions for initializing particle positions and velocities, updating particle movement based on social and cognitive components, and implementing convergence criteria. Key functions include pso_optimize() for main algorithm execution, fitness_evaluation() for objective function calculation, and swarm_visualization() for real-time optimization progress monitoring. Through this toolbox, users can efficiently implement PSO algorithms while obtaining accurate and reliable optimization results. Beyond detailed algorithm implementation specifications, we provide practical usage examples demonstrating parameter tuning techniques, constraint handling methods, and multi-objective optimization approaches. These examples illustrate common implementation patterns such as inertia weight adjustment strategies, neighborhood topology configurations, and hybrid optimization techniques. By studying this documentation, users will gain thorough understanding of the PSO toolbox's capabilities and achieve successful implementation in their respective projects. The toolbox supports both standard PSO variants and customized algorithm modifications through modular function architecture.