PSO (Particle Swarm Optimization) Algorithm Toolbox

This article introduces the PSO (Particle Swarm Optimization) algorithm, which serves as a highly valuable toolbox for optimization tasks. As a swarm intelligence-based optimization technique, PSO draws inspiration from the foraging behavior of insect colonies. The algorithm operates by simulating the movement of particle swarms through a search space to locate optimal solutions. Implementation typically involves initializing a population of particles with random positions and velocities, then iteratively updating their movements based on individual and collective best-known positions. Key functions include calculating fitness values, updating velocity vectors using inertia weights and acceleration coefficients, and maintaining personal/global best positions. PSO finds extensive applications across multiple domains including machine learning (for hyperparameter tuning), data mining (feature selection), and complex optimization problems. By leveraging parallel search capabilities and simple implementation logic, PSO enables more efficient resolution of complex optimization challenges with improved solution quality compared to traditional methods. The algorithm's MATLAB implementation typically requires less than 100 lines of core code, making it accessible for various engineering applications.