Abstract

The application of the Ant Colony Optimization (ACO) algorithm to solve the the Traveling Salesman Problem (TSP) has been extensively studied by scientists worldwide. However, implementing the algorithm faces challenges due to the randomness in the departure and movement times of ants, as well as the limitations of computer hardware. These factors reduce the algorithm's convergence ability and overall effectiveness. This paper proposes an approach to implement the algorithm by utilizing discrete event simulation (DES). Artificial ants are modeled to depart and move completely randomly, closely mimicking the behavior of natural ants. This approach accelerates the algorithm's convergence, minimizes the likelihood of falling into local optima, and enhances overall performance. The simulation results clearly demonstrate the advantages of this method.