MATLAB Implementation of Particle Swarm Optimization (PSO) Algorithm with Code Examples

This article presents an in-depth exploration of the Particle Swarm Optimization (PSO) algorithm and its practical implementation using MATLAB. PSO is a population-based optimization technique inspired by collective intelligence observed in natural phenomena such as bird flocking or fish schooling. The algorithm operates by simulating particles moving through the solution space to collectively discover optimal solutions. The technical discussion covers fundamental PSO components including: - Algorithm原理: Social behavior modeling where each particle adjusts its trajectory based on personal best experience and neighborhood best performance - Implementation步骤: Initialization, velocity updates, position updates, and fitness evaluation cycles - Key parameter configurations: Inertia weight, cognitive/social acceleration coefficients, and swarm size optimization MATLAB implementation aspects detail: 1. Code structure with particle initialization using rand() functions 2. Vectorized operations for efficient velocity and position updates 3. Fitness function integration through function handles 4. Convergence monitoring via iterative plotting commands 5. Practical examples demonstrating constraint handling and multi-objective optimization scenarios The provided MATLAB scripts include commented code sections explaining: - Main optimization loop architecture with while/for iterations - Boundary condition handling using min/max functions - Global best tracking through comparison operations - Performance visualization using plot() and scatter() functions This resource serves as a complete guide for researchers and engineers to understand, customize, and apply PSO algorithms effectively in solving complex optimization problems across various engineering domains.