MATLAB Implementation of Particle Swarm Optimization (PSO) Algorithm

This article presents a MATLAB implementation of the Particle Swarm Optimization (PSO) algorithm. PSO functions as an iterative optimization technique that initializes a population of random candidate solutions (particles) and refines them through successive generations. The algorithm operates by having particles navigate the problem space while adjusting their velocities based on personal best positions and global swarm intelligence. Key implementation aspects include: - Initialization of particle positions and velocities within defined search boundaries - Fitness evaluation using objective functions specific to the optimization problem - Dynamic velocity updates incorporating inertia weight, cognitive, and social components - Continuous tracking of personal best (pbest) and global best (gbest) positions - Termination conditions based on maximum iterations or convergence thresholds Through this iterative process, the algorithm systematically explores the solution space to converge toward optimal or near-optimal solutions while balancing exploration and exploitation characteristics. The code structure allows customization of swarm parameters, boundary constraints, and objective functions for various optimization applications.