MATLAB Implementation of Particle Swarm Optimization Algorithm

This MATLAB implementation of Particle Swarm Optimization (PSO) is developed with comprehensive Chinese documentation and comments. The program features: - Standard PSO algorithm with inertia weight adjustment - Customizable optimization parameters (swarm size, iteration count, velocity limits) - Modular design allowing easy fitness function replacement - Real-time convergence monitoring and performance visualization - Support for both constrained and unconstrained optimization problems Key functions include: particle_swarm_optimizer.m - Main optimization algorithm with velocity update equations initialize_swarm() - Population initialization with random position/velocity calculate_fitness() - Objective function evaluation module update_best_positions() - Personal and global best tracking plot_convergence() - Real-time optimization progress visualization The code is structured for easy modification and extension, making it suitable for both educational purposes and practical engineering optimization applications. Users can adapt the fitness function to solve specific optimization problems in various domains including machine learning, control systems, and computational finance.