MATLAB Implementation of PSO (Particle Swarm Optimization) Algorithm

This article presents a MATLAB implementation of the Particle Swarm Optimization (PSO) algorithm, focusing on optimizing a primary benchmark function. The code structure employs velocity and position update equations based on social and cognitive components, using MATLAB's vectorized operations for efficient computation. Parameter adjustment mechanisms (inertia weight, acceleration coefficients) are implemented to enhance convergence performance. While demonstrated with one function, the modular design allows easy adaptation to other optimization problems through function handle replacements. The algorithm's simplicity makes it accessible for beginners, with clear comments explaining key components like particle initialization, fitness evaluation, and global best tracking. This implementation serves as an educational foundation for exploring swarm intelligence algorithms, with suggestions for further enhancements like boundary handling and convergence criteria customization.