Eberhart and Kennedy proposed a meta-heuristic method called Particle Swarm Optimization (PSO). PSO is inspired by a easy mathematical model of the swarm intelligence of fish and birds. This optimizer examines the dedicated problem search space to find the most excellent settings or parameters needed to maximize a specific objective. In other words, particle swarm is a collection of candidate solutions to the problem optimization that may flow in the search parameter space called trajectories which is determined by the best performance of itself and its neighbors. The PSO can be run to maximize and minimize problems. That means the minimum or maximum value of a process can be found. A task of minimizing W function can be explained by the following :

Given W : Sn → S Find xS ˇ n such that W (x)ˇ ≤ W (x), ∀x ∈ Sn