Pheromone modification strategy for the dynamic travelling salesman problem with weight changes

Ant colony optimization (ACO) algorithms have proved to be able to adapt in problems that change dynamically. One of the key issues for ACO when a change occurs is that the pheromone trails generated in the previous environment will not be compatible with the new environment. Therefore, the optimization process may be biased from the pheromone trails of the previous environment and fail to search for the newly generated global optimum. In this paper, we consider the dynamic travelling salesman problem (DTSP) in which the weights of the arcs are modified. A pheromone strategy that utilizes change-related information and regulates heuristically the pheromone trails of the affected arcs is proposed. From the experimental results the heuristic-based pheromone strategy performs statistically significant better in most DTSP test cases than other peer ACO algorithms.