ACO is a relatively novel meta-heuristic technique and has been successfully used in manyapplications especially problems in combinatorial optimization. ACO algorithm models the behavior of realant colonies in establishing the shortest path between food sources and nests. Ants can communicate withone another through chemicals called pheromones in their immediate environment. The ants releasepheromone on the ground while walking from their nest to food and then go back to the nest. The ants moveaccording to the amount of pheromones, the richer the pheromone trail on a path is, the more likely it wouldbe followed by other ants. So a shorter path has a higher amount of pheromone in probability, ants will tendto choose a shorter path. Through this mechanism, ants will eventually find the shortest path. Artificial antsimitate the behavior of real ants, but can solve much more complicated problem than real ants can.ACO has been widely applied to solving various combinatorial optimization problems such as TravelingSalesman Problem (TSP), Job-shop Scheduling Problem (JSP), Vehicle Routing Problem (VRP), QuadraticAssignment Problem (QAP), etc. Although ACO has a powerful capacity to find out solutions tocombinational optimization problems, it has the problems of stagnation and premature convergence and theconvergence speed of ACO is very slow. Those problems will be more obvious when the problem sizeincreases. Therefore, several extensions and improvements versions of the original ACO algorithm wereintroduced over the years. Various adaptations: dynamic control of solution construction, mergence oflocal search, a strategy is to partition artificial ants into two groups: scout ants and common ants 11and new pheromone updating strategies using candidate lists strategies are studied toimprove the quality of the final solution and lead to speedup of the algorithm. All these studies havecontributed to the improvement of the ACO to some extents, but they have little obvious effect on increasingthe convergence speed and obtaining the global optimal solution. In the proposed system, the mainmodifications introduced by ACO are the following. First, to avoid search stagnation and ACO is moreeffective if ants are initially placed on different cities. Second, information entropy is introduced which isadjust the algorithm’s parameters. Additionally, the best performing ACO algorithms for the TSP improvethe solutions generated by the ants using local search algorithms.