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.
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The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Kefi, S., Rokbani, N., Alimi, A.M. (2017). Solving the Traveling Salesman Problem Using Ant Colony Metaheuristic, A Review. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_42
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DOI: https://doi.org/10.1007/978-3-319-52941-7_42
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