Particle Swarm Optimization (PSO) is a stochastic optimization technique that mimics swarm intelligence to find an optimal point in a search space as defined by an objective function. The particle swarm consists of individual agents (particles) that explore the search space, utilizing information gained during their search and "communicating" with other particles in the swarm . This concept can be defined by a few simple equations. The N particles in the swarm are defined by their position x and velocity v. The potential surface of the search space can be evaluated by the objective function f . The movement of the i-th particle at iteration step k is influenced by the best point it has yet discovered