MATLAB Source Code for PSO (Particle Swarm Optimization) Algorithm

MATLAB source code for Particle Swarm Optimization (PSO) algorithm, designed to find optimal numerical solutions in solution spaces. Particle Swarm Optimization (PSO) is a population-based optimization algorithm inspired by swarm intelligence, simulating bird flock foraging behavior to locate optimal numerical solutions in solution spaces. The PSO algorithm operates through iterative updates of particle positions and velocities to search for optimal solutions. This algorithm has been widely applied in various domains including engineering optimization, machine learning, and data mining. In MATLAB implementation, the PSO function typically requires defining key components: objective function handle, constraint specifications, and algorithm parameters (swarm size, inertia weight, cognitive/social coefficients). The core algorithm structure involves initializing particle positions/velocities, evaluating fitness functions, updating personal best positions (pbest), and global best positions (gbest) through velocity update equations: v_i(t+1) = w*v_i(t) + c1*r1*(pbest_i - x_i(t)) + c2*r2*(gbest - x_i(t)). The MATLAB PSO implementation returns optimal numerical solutions from the solution space while providing convergence monitoring capabilities. Key functions include particle initialization (psocreationuniform), velocity updates (psoiterate), and boundary handling mechanisms. For researchers interested in finding optimal numerical solutions, this MATLAB PSO source code offers flexible parameter tuning and objective function customization. By adjusting swarm parameters (population size, iteration count) and problem-specific fitness functions, users can explore optimal solutions across different problem domains and achieve improved performance results. We hope this information proves valuable for your optimization projects. Wish you success in your computational experiments!