Particle Swarm Optimization is a population-based metaheuristic inspired by social behavior of bird flocking and fish schooling. Each particle represents a candidate solution that moves through the search space with velocity influenced by: (1) its own best position found so far (cognitive component); (2) the best position found by the entire swarm (social component); (3) inertia from previous velocity. Update equations: vᵢ = ωvᵢ + c₁r₁(pbestᵢ - xᵢ) + c₂r₂(gbest - xᵢ) and xᵢ = xᵢ + vᵢ. PSO is simple to implement, requires few parameters, and is effective for continuous optimization, neural network training, and engineering design problems.