PSO (Kennedy and Eberhart, 1995) is a population based stochastic algorithm inspired by the behaviour of a flock of birds or other such groups of organisms in which a collection of individuals, or particles, “move” in the search space and attempt to locate a target. The motion of any particle is affected by three factors: (i) the best position located till the current iteration by any particle in the swarm (Social adjustment), (ii) the best position located by the particle under consideration till the current iteration (Self adjustment) and (iii) the velocity of the particle in the previous iteration (Inertia). A comparison on few of the important aspects of these algorithms including the number of functional evaluations required in each generation/iteration is provided in Table 1.