PSO Particle Swarm Optimization Algorithm Source Code

PSO (Particle Swarm Optimization) algorithm is an optimization technique based on swarm intelligence, inspired by the social behavior of bird flocks or fish schools. The algorithm efficiently searches for optimal solutions in the solution space by simulating the information-sharing mechanism among individuals within a swarm.

The core concept of PSO involves maintaining a population of "particles" (candidate solutions), where each particle records its personal historical best position and the group's known best position. In each iteration, particles update their positions by adjusting velocity vectors: - Inertia component: Preserves a portion of the particle's velocity from the previous iteration - Cognitive component: Drives the particle toward its personal best position - Social component: Guides the particle toward the swarm's current best position

A typical implementation includes the following key parameters: - Population size (number of particles) - Inertia weight (balances global and local search) - Acceleration constants (weights for cognitive and social components) - Maximum iteration count

The algorithm is suitable for continuous space optimization problems and has wide applications in neural network training and engineering parameter tuning. Compared to evolutionary methods like genetic algorithms, PSO generally requires fewer parameters to adjust, converges faster, but may get trapped in local optima. Advanced improvements include introducing adaptive weights and hybrid strategies combining other optimization methods.