MATLAB Implementation of Improved Particle Swarm Optimization Algorithm

This work presents an improved Particle Swarm Optimization (PSO) algorithm designed to enhance global convergence performance and search velocity. Our proposed methodology incorporates multiple heuristic techniques to strengthen the algorithm's exploration capabilities. Through dynamic adjustment of particle velocity and position vectors, we achieve more comprehensive solution space exploration and obtain superior solutions within reduced computation time. Key implementation aspects include: - Adaptive inertia weight strategy that automatically adjusts based on iteration progress and population diversity metrics - Velocity updating mechanism with boundary constraint handling to maintain search stability - Position update function with periodic reinitialization for escaping local optima - Fitness evaluation framework integrating both exploration and exploitation phases The algorithm's MATLAB implementation features: 1. Main optimization function managing swarm initialization and iterative updates 2. Velocity calculation module with cognitive and social component balancing 3. Adaptive parameter controller using nonlinear weight decay functions 4. Convergence monitoring system with stagnation detection and restart triggers Experimental validation across multiple benchmark optimization problems confirms the algorithm's robust performance, demonstrating significant potential for global search applications and complex optimization scenarios. The implementation shows particular effectiveness in high-dimensional problems where traditional PSO variants tend to premature convergence.