Particle Swarm Optimization Algorithm

This implementation contains detailed annotations and is well-suited for reference and educational purposes. The code demonstrates the complete particle swarm optimization (PSO) algorithm workflow, including population initialization, velocity updates using inertia weight and acceleration coefficients, position updates with boundary constraints, and fitness evaluation. Key functions include particle initialization with random positions and velocities, global best tracking through fitness comparisons, and parameter tuning mechanisms for convergence control. To further enhance understanding, additional context about swarm intelligence principles, convergence criteria settings, and parameter sensitivity analysis could be included. Practical examples demonstrating applications in function optimization or real-world engineering problems would illustrate the algorithm's practical implementation. By providing expanded explanations of social and cognitive components, neighborhood topologies, and convergence analysis techniques, this resource becomes more comprehensive for researchers and practitioners working with evolutionary algorithms.