Chaotic Particle Swarm Optimization Algorithm
Core Technology Particle Swarm Optimization (PSO) is a swarm intelligence algorithm simulating bird flocking behavior, sharing fundamental concepts with other optimization algorithms. In PSO, each particle represents a bird with initial position and velocity, continuously adjusting flight speed and direction during movement to ultimately locate optimal solutions. Chaos refers to complex nonlinear system behavior resembling randomness, highly sensitive to initial conditions, easily escaping local minima with rapid search capabilities. The core concept of Chaotic PSO involves applying chaotic perturbations to the global best (Gbest) particle during each iteration, using the perturbed position for particle updates to prevent position convergence and enable local search around global optima. Implementation typically involves logistic maps for chaos generation and velocity-position update equations with dynamic inertia weights.