This paper deals with Ant Colony Optimization, a heuristic algorithm with strong robustness and the ability of finding the optimal solution which has been applied to a number of combinatorial optimization (CO) problems, of which the most important one is the traveling salesman problem (TSP). Ants of the colony have the ability to generate shorter feasible tours through information, which is accumulated, in the form of a pheromone trail deposited on the TSP graph’s edges. For solving the TSP problem, ACO is one of the high performance computing methods but still has some drawbacks, which include stagnation behavior, computational time, which is longer, and premature convergence problem.