Ant Colony Optimization (ACO) is a relatively new meta-heuristic and successful technique in the field of swarm intelligence. This technique was first introduced by Dorigo and his colleagues. This technique is used for many applications especially problems that belong to the combinatorial optimization. ACO algorithm models represent the behavior of real ant colonies in establishing the shortest path between food sources and nests. The ants release pheromone on the ground while walking from their nest to food and then go back to the nest. The ants move according to the richer amount of pheromones on their path and other ants would be followed and will tend to choose a shorter path which would have a higher amount of pheromone. Artificial ants imitate the behavior of real ants, but can solve much more complicated problem than real ants can.