What are the advantages of using particle swarm optimization (PSO) over traditional optimization methods, and how does it simulate social behavior to find optimal solutions?

PSO offers advantages such as simplicity, ease of implementation, and the ability to converge to global optima efficiently. It simulates social behavior by treating potential solutions as particles that move through the search space, influenced by their own best position and the global best position found by the swarm. This collaborative approach enables PSO to explore and exploit the search space effectively, making it suitable for complex optimization problems.