Ant Colony Optimization using exploratory ants for constructing partial solutions

When Traveling Salesman Problem (TSP) is solved by Ant Colony Optimization (ACO), the round tour that each ant generated is evaluated by its tour length, and each ant lays pheromone based on the evaluated value. Basically, ants select the next city considering pheromone intensity and the closeness of the distance. In making a round tour, however, it is probable that the only far cities remain as the candidates for the next move. In this case, the ant has to select one of the cities unwillingly. By this phenomenon, a useless route is included in the round tour, and the tour is not evaluated appropriately. To solve this problem, we propose a new ACO method using heterogeneous ants for Traveling Salesman Problems. In the proposed method, there exist not only the normal ants but also the exploratory ants which construct partial solutions. In constructing solution phase, the exploratory ant selects the next city from unvisited cities which exist in the neighborhood of the ant. If there is no unvisited city in the neighborhood of the ant, the ant gives up constructing its round tour. We call this method Give-up Ant System (GAS). We confirmed that the search performance improved by the effect of the diversification of search by the exploratory ants.