Ant Colony Optimization (ACO) is a class ofheuristic search algorithms that have beensuccessfully applied to solving combinationaloptimization (CO) problems. The traveling salesmanproblem (TSP) is among the most importantcombinatorial problems. ACO has very good searchcapability for optimization problems. But it still hassome drawbacks such as stagnation behavior, longcomputational time, and premature convergenceproblem of the basic ACO algorithm on TSP. Thoseproblems will be more obvious when the complexitiesof the considered problems increase. The proposedsystem based on basic ACO algorithm based on wellpositionedthe ants on the initiation and informationentropy which is applied to tuning of the algorithm’sparameters. Then, ACO for TSP has been improvedby incorporating local optimization heuristic.Therefore, the proposed system intends to reachsuperior search performance over traditional ACOalgorithms do
