Particle Swarm Optimization (PSO) is a metaheuristic algorithm inspired by the collective behavior of birds flocking or fish schooling. In PSO, a population of candidate solutions, called particles, moves through the search space to find the optimal solution to a given problem. Each particle adjusts its position based on its own experience and the collective knowledge of the swarm, represented by the best solution found so far. Through iterative updates, particles gradually converge towards the global optimum, guided by their individual velocities and the influence of the best-performing particles in the swarm. This iterative process enables PSO to efficiently explore complex search spaces and find high-quality solutions across various optimization domains.