Fine-Tuning the Ant Colony System Algorithm Through Harris’s Hawk Optimizer for Travelling Salesman Problem

Abstract

The ant colony system (ACS) algorithm is one of the metaheuristics in solving combinatorial optimization problems. The control parameters namely, pheromone coefficient, heuristic coefficient, decision rule, and evaporation rate of the pheromone in ACS are important in determining the performance of the algorithm. However, the initialized values of these parameters stay constant during the search process which leads to performance degradation of the algorithm. This paper aims to tune the parameters of the ACS algorithm by using Harris’s hawk optimization (HHO) algorithm in solving the travelling salesman problem (TSP). The proposed hybrid algorithm is called Harris’s hawk optimizer ant colony system (HHO-ACS). The final process of HHO-ACS will output the path distance. The performance of the HHO-ACS has been evaluated by using different symmetric TSP instances of various scale size. The results showed the HHO-ACS provided superior performance compared to other well-known metaheuristics namely black hole, particle swarm optimization, dragonfly, genetic and ant colony optimization algorithms. Thus, it can attain a better solution with higher accuracy. The proposed algorithm was able to achieve best known optimal solution in solving bayg29, att48 and berlin52 instances and near optimal solution in solving bays29, eil51, st70, eil76 and eil101 instances. Compared to other algorithms, the HHO-ACS algorithm showed superior performance in solving the TSP instances which indicates its effectiveness. This is possible because, in the HHO-ACS algorithm, the main control parameters of ACS algorithm namely, the pheromone coefficient, heuristic coefficient, evaporation rate of the pheromone, and decision rule were tuned according to the problem instance at hand. Thus, the HHO-ACS algorithm can be used to solve problems of travelling salesman nature with minimum customization.