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International Conference on Hybrid Intelligent Systems

HIS 2016: Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) pp 421–430Cite as

Solving the Traveling Salesman Problem Using Ant Colony Metaheuristic, A Review

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 552))

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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References

  1. Laporte, G.: The Traveling Salesman Problem – an overview of exact and approximate algorithms. Eur. J. Oper. Res. 59, 231–247 (1992)

    Article  MATH  Google Scholar 

  2. Christofides, N.: The vehicle routing problem. RAIRO – Oper. Res. 10, 55–70 (1976)

    MathSciNet  MATH  Google Scholar 

  3. Wang, C., Mu, D., Zhao, F., Sutherland, J.W.: A parallel simulated annealing method for the vehicle routing problem with simultaneous pickup delivery and time windows. Comput. Ind. Eng. 83, 111–122 (2015)

    Article  Google Scholar 

  4. Alba, E.: Parallel metaheuristics: a new class of algorithms. In: Wiley-Interscience (2005)

    Google Scholar 

  5. Glover, F.: Tabu search. ORSA J. Comput. 1(3), 190–206 (1989)

    Article  MATH  Google Scholar 

  6. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  7. Dorigo, M., Stutzle, T.: Ant Colony Optimization. Bradford Company, Scituate (2004)

    MATH  Google Scholar 

  8. Yang, X., Wang, J.S.: Application of improved ant colony optimization algorithm on traveling salesman problem. In: Chinese Control and Decision Conference, pp. 2156–2160 (2016)

    Google Scholar 

  9. Rokbani, N., Momasso, A.L., Alimi, A.M.: AS-PSO, Ant supervised by PSO meta-heuristic with application to TSP. In: Proceedings Engineering & Technology, vol. 4, pp. 148–152 (2013)

    Google Scholar 

  10. Rokbani, N., Abraham, A., Alimi, A M.: Fuzzy ant supervised by PSO and simplified ant supervised PSO applied to TSP. In: The 13th International Conference on Hybrid Intelligent Systems (HIS), pp. 251–255 (2013)

    Google Scholar 

  11. Kefi S., Rokbani N., Krömer P., Alimi A.M.: A new ant supervised-PSO variant applied to traveling salesman problem. In: The 15th International Conference on Hybrid Intelligent Systems (HIS), pp. 87–101 (2015)

    Google Scholar 

  12. Kefi, S., Rokbani, N., Krömer, P., Alimi, A.M.: Ant supervised by PSO and 2-opt algorithm, AS-PSO-2Opt, applied to traveling salesman problem. In: IEEE International conference on System Man and Cybernetics SMC (2016)

    Google Scholar 

  13. Kefi, S., Rokbani, N., Alimi, M.A.: Hybrid metaheuristic optimization based on ACO and standard PSO applied to traveling salesman problem. Int. J. Comput. Sci. Inform. Secur. 14(7), 802–823 (2016)

    Google Scholar 

  14. Reinelt, G.: TSPLIB-a traveling salesman problem library. ORSA J. Comput. 3, 376–384 (1991)

    Article  MATH  Google Scholar 

  15. Stützle, T., Hoos, H.: The MAX-MIN ant system and local search for the traveling salesman problem. In: International Conference on Evolutionary Computing and Evolutionary Program, pp. 13–19 (1997)

    Google Scholar 

  16. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  17. Gambardella, L.M., Dorigo, M.: Ant Colony System hybridized with a new local search for the sequential ordering problem. Inform. J. Comput. 12(3), 237–255 (2000)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

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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Authors and Affiliations

  1. REGIM-Lab: Research Groups in Intelligent Machines, University of Sfax, ENIS, Sfax, Tunisia

    Sonia Kefi, Nizar Rokbani & Adel M. Alimi

  2. High Institute of Applied Sciences and Technology of Sousse, University of Sousse, Sousse, Tunisia

    Nizar Rokbani

Authors
  1. Sonia Kefi
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  2. Nizar Rokbani
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  3. Adel M. Alimi
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Corresponding author

Correspondence to Sonia Kefi .

Editor information

Editors and Affiliations

  1. (MIR Labs), Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  2. Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. ENIS, University of Sfax, Sfax, Tunisia

    Adel M. Alimi

  4. Technopolis Rabat-Shore Rocade, International University of Rabat, Sala el Jadida, Morocco

    Ghita Mezzour

  5. Inst Applied Dept of Electronics, Taffala, University of Sousse, Sousse, Tunisia

    Nizar Rokbani

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52940-0

  • Online ISBN: 978-3-319-52941-7

  • eBook Packages: EngineeringEngineering (R0)

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