Ant Colony Optimization for the Traveling Salesman Problem Based on Ants with Memory

We propose a new model of ant colony optimization (ACO) to solve the traveling salesman problem (TSP) by introducing ants with memory into the ant colony system (ACS). In the new ant system, the ants can remember and make use of the best-so-far solution, so that the algorithm is able to converge into at least a near-optimum solution quickly. We have tested the algorithm in 3 representational TSP instances and compared the results with the original ACS algorithm. According to the result we make amelioration to the new ant model and test it again. The simulations show that the amended ants with memory improve the converge speed and can find better solutions compared to the original ants.