Solving the traveling salesman problem using ant colony metaheuristic, a review

Abstract

This paper presents a software application allowing to solve and compare the key metaheuristic approaches for solving the Traveling Salesman Problem (TSP). The focus is based on Ant Colony Optimization (ACO) and its major hybridization schema. In this work, the hybridization ACO algorithm with local search approach and the impact of parameters while solving TSP are investigated. The paper presents results of an empirical study of the solution quality over computation time for Ant System (AS), Elitist Ant System (EAS), Best-Worst Ant System (BWAS), MAX–MIN Ant System (MMAS) and Ant Colony System (ACS), five well-known ACO algorithms. In addition, this paper describes ACO approach combined with local search approach as 2-Opt and 3-Opt algorithms to obtain the best solution compared to ACO without local search with fixed parameters setting. The simulation experiments results show that ACO hybridized with the local search algorithm is effective for solving TSP and for avoiding the premature stagnation phenomenon of standard ACO.