MATLAB Implementation of Particle Swarm Optimization Algorithm

This MATLAB program implements Particle Swarm Optimization (PSO) for multi-objective extremum calculation. The algorithm helps identify multiple optimal solutions by simulating collective intelligence through particle movement in search space. Key implementation features include: - Configurable swarm size, inertia weight, and acceleration coefficients - Custom objective function handling through function handles - Position and velocity updating using standard PSO equations: v_i(t+1) = w*v_i(t) + c1*rand()*(pbest_i - x_i(t)) + c2*rand()*(gbest - x_i(t)) - Multi-objective optimization support through Pareto frontier detection - Real-time convergence monitoring and result visualization The program accepts user-defined objective functions and parameter settings, automatically executing optimization iterations to output multiple extremum points. Particle Swarm Optimization mimics biological swarm behavior (like bird flocking or fish schooling) to efficiently explore solution spaces. This implementation provides researchers and engineers with a flexible tool for complex optimization problems, featuring clear code structure and comprehensive documentation for easy customization.