Stochastic Particle Swarm Optimization Algorithm

In the stochastic particle swarm optimization algorithm discussed here, we can execute the algorithm by incorporating the following parameters: wmax = 0.9; % Maximum velocity constraint wmin = 0.01; % Minimum velocity constraint itmax = 100; % Maximum iteration count c1 = 2; % Learning factor (cognitive component) c2 = 2; % Learning factor (social component) W=(wmax-wmin)/itmax; % Adaptive inertia weight for velocity update When initializing the particle swarm with initial positions and velocities, we need to configure the following parameters: xmin = -3; % Parameter minimum boundary xmax = 3; % Parameter maximum boundary N = 100; % Number of particles in the swarm D = 2; % Dimension of the search space (number of parameters) t = 0.01; % Step size for position update The stochastic particle swarm optimization algorithm is an iterative optimization technique that progressively converges toward optimal solutions. During algorithm execution, particles dynamically adjust their velocities and positions using learning factors c1 and c2, enabling systematic exploration of superior parameter combinations. The implementation employs an adaptive inertia weight W that linearly decreases from wmax to wmin over iterations, balancing global exploration and local exploitation. The cognitive component (c1) guides particles toward their personal best positions, while the social component (c2) directs them toward the swarm's global best. Proper configuration of these parameters, including boundary constraints (xmin, xmax) and swarm size (N), significantly influences convergence behavior and optimization performance, allowing customization for specific problem domains.