The Optimal Selection of the Parameters for the Ant Colony Algorithm with Small-Perturbation

An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method

“…The values of the iteration number (S) were identified as S ∈ 1000, 2000, 3000, 4000, 5000} . It can be seen in the literature that these values are used extensively [11,33,41,55,57]. This parameter was included in the optimization in order to learn which iteration value best represented the problem.…”

“…The values of the Q parameter that was to be optimized were identified as Q ∈ 1, 10, 100, 1000, 10000}. These values were identified according to the response of the algorithm obtained as a result of intensive experiments prior to optimization and by utilizing other studies [55][56][57][58][59]. The values of the iteration number (S) were identified as S ∈ 1000, 2000, 3000, 4000, 5000} .…”

“…Parameter Q is a parameter related to the quantity of routes initiated by the ants and it stops the cumulative pheromone levels from being too high or too low [55]. In many studies, it has been stated that the parameters do not have a very important effect on the solutions provided that they are not too small [12,50,55].…”

“…In many studies, it has been stated that the parameters do not have a very important effect on the solutions provided that they are not too small [12,50,55]. This parameter was added to the parameter optimization in order to identify its effect on the ACO.…”