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The hop-constrained minimum cost flow spanning tree problem with nonlinear costs: an ant colony optimization approach

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Abstract

In this work we address the Hop-Constrained Minimum cost Flow Spanning Tree (HMFST) problem with nonlinear costs. The HMFST problem is an extension of the Hop-Constrained Minimum Spanning Tree problem since it considers flow requirements other than unit flows. We propose a hybrid heuristic, based on ant colony optimization and on local search, to solve this class of problems given its combinatorial nature and also that the total costs are nonlinearly flow dependent with a fixed-charge component. We solve a set of benchmark problems available online and compare the results obtained with the ones reported in the literature for a Multi-Population hybrid biased random key Genetic Algorithm (MPGA). Our algorithm proved to be able to find an optimum solution in more than 75 % of the runs, for each problem instance solved, and was also able to improve on many results reported for the MPGA. Furthermore, for every single problem instance we were able to find a feasible solution, which was not the case for the MPGA. Regarding running times, our algorithm improves upon the computational time used by CPLEX and was always lower than that of the MPGA.

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References

  1. Akgun, I., Tansel, B.C.: New formulations of the hop-constrained minimum spanning tree problem via miller-tucker-zemlin constraints. Eur. J. Oper. Res. 212, 263–276 (2011)

    Article  MathSciNet  Google Scholar 

  2. Altiparmak, F., Karaoglan, I.: A genetic ant colony optimization approach for concave cost transportation problems. In: IEEE Congress on Evolutionary Computation, 2007. CEC 2007, pp. 1685–1692 (2007).

  3. Beasley, J.: Or-library. http://www.brunel.ac.uk/deps/ma/research/jeb/orlib/netflowccinfo.html (2010)

  4. Bin, Y., Zhong-Zhen, Y., Baozhen, Y.: An improved ant colony optimization for vehicle routing problem. Eur. J. Oper. Res. 196, 171–176 (2009)

    Article  MATH  Google Scholar 

  5. Burkard, R.E., Dell’Amico, M., Martello, S.: Assignment problems. Siam (2009).

  6. Cordon, O., Herrera, F., Stützle, T.: A review on the ant colony optimization metaheuristic: basis, models and new trends. Mathw. Soft. Comput. 9, 141–175 (2002)

    MATH  Google Scholar 

  7. Dang, C., Sun, Y., Wang, Y., Yang, Y.: A deterministic annealing algorithm for the minimum concave cost network flow problem. Neural Netw. 24, 69–708 (2011)

    Article  Google Scholar 

  8. Dorigo, M., Stützle, T.: Ant colony optimization. MIT Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  9. Fontes, D.B.M.M.: Optimal hop-constrained trees for nonlinear cost flow networks. INFOR 48, 13–21 (2010)

    MathSciNet  Google Scholar 

  10. Fontes, D.B.M.M., Gonçalves, J.F.: Heuristic solutions for general concave minimum cost network flow problems. Networks 50, 67–76 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  11. Fontes, D.B.M.M., Gonçalves, J.F.: A multi-population hybrid biased random key genetic algorithm for hop-constrained trees in nonlinear cost flow networks. Optim. Lett. 7, 1303–1324 (2012)

    Article  Google Scholar 

  12. Fontes, D.B.M.M., Hadjiconstantinou, E., Christofides, N.: Upper bounds for single-source uncapacitated concave minimum-cost network flow problems. Networks 41(4), 221–228 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  13. García-Martínez, C., Cordón, O., Herrera, F.: A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. Eur. J. Oper. Res. 180(1), 116–148 (2007)

    Article  MATH  Google Scholar 

  14. Gouveia, L., Martins, P.: The capacitated minimal spanning tree problem: an experiment with a hop-indexed model. Ann. Oper. Res. 86, 271–294 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  15. Gouveia, L., Paias, A., Sharma, D.: Restricted dynamic programming based neighborhoods for the hop-constrained minimum spanning tree problem. J. Heuristics 17, 23–37 (2011a)

    Article  MATH  Google Scholar 

  16. Gouveia, L., Requejo, C.: A new lagrangean relaxation approach for the hop-constrained minimum spanning tree problem. Eur. J. Oper. Res. 132, 539–552 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  17. Gouveia, L., Simonetti, L., Uchoa, E.: Modeling hop-constrained and diameter-constrained minimum spanning tree problems as steiner tree problems over layered graphs. Math. Prog. 128, 123–148 (2011b)

    Article  MATH  MathSciNet  Google Scholar 

  18. Kim, D.: Piecewise linear network flow problems. In: Floudas, C.A., Pardalos, P.M. (eds.) Encyclopedia of Optimization. Kluwer Academic, Dordrechtr (2003)

    Google Scholar 

  19. Kiran, M.S., Gunduz, M., Baykan, O.K.: A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum. Appl. Math. Comput. 219, 1515–1521 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  20. Liao, T.W., Kuo, R.J., Hu, J.T.L.: Hybrid ant colony optimization algorithms for mixed discrete-continuous optimization problems. Appl. Math. Comput. 219, 3241–3252 (2012)

    Article  MathSciNet  Google Scholar 

  21. Monteiro, M.S.R.: Ant colony optimization algorithms to solve nonlinear network flow problems. Ph.D. thesis, Faculdade de Economia da Universidade do Porto, Porto, Portugal (2012)

  22. Monteiro, M.S.R., Fontes, D.B.M.M., Fontes, F.A.C.C.: Concave minimum cost network flow problems solved with a colony of ants. J. Heuristics 19, 1–33 (2013a)

    Article  Google Scholar 

  23. Monteiro, M.S.R., Fontes, D.B.M.M., Fontes, F.A.C.C.: Solving hop-constrained MST problems with ACO. Working paper, N493, Universidade do Porto, Faculdade de Economia (2013b)

  24. Mullen, R., Monekosso, D., Barman, S., Remagnino, P.: A review of ant algorithms. Expert. Syst. Appl. 36, 9608–9617 (2009)

    Article  Google Scholar 

  25. Putha, R., Quadrifoglio, L., Zechman, E.: Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Comput-Aided Civ. Infrastruct. Eng. 27, 14–28 (2012)

    Article  Google Scholar 

  26. Stützle, T., Hoos, H.: Max-min ant system and local search for the traveling salesman problem. In: IEEE International Conference On Evolutionary Cmputation (ICEC’97), pp. 309–314. IEEE Press, Piscataway (1997)

  27. Tseng, S., Lin, C., Huang, Y.: Ant colony-based algorithm for constructing broadcasting tree with degree and delay constraints. Expert. Syst. Appl. 35, 1473–1481 (2008)

    Article  Google Scholar 

  28. Wang, H., Shi, Z., Li, S.: Multicast routing for delay variation bound using a modified ant colony algorithm. J. Netw. Comput. Appl. 32, 258–272 (2008)

    Article  Google Scholar 

  29. Yin, P.Y., Wang, J.Y.: Ant colony optimization for the nonlinear resource allocation problem. Appl. Math. Comput. 174, 1438–1453 (2006)

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgments

This work is partially funded by ERDF (FEDER), the COMPETE through the FCT as part of projects PTDC/EGE-GES/099741/2008 and PTDC/EEA-CRO/116014/2009 and the North Portugal Regional Operational Programme (ON.2O Novo Norte) under the National Strategic Reference Framework.

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Correspondence to Dalila B. M. M. Fontes.

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Monteiro, M.S.R., Fontes, D.B.M.M. & Fontes, F.A.C.C. The hop-constrained minimum cost flow spanning tree problem with nonlinear costs: an ant colony optimization approach. Optim Lett 9, 451–464 (2015). https://doi.org/10.1007/s11590-014-0762-6

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  • DOI: https://doi.org/10.1007/s11590-014-0762-6

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