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Four-objective formulations of multicast flows via evolutionary algorithms with quality demands

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Abstract

In this work, we investigate two four-objective formulations of multicast routing problem, in which a tree must be set to deliver data to a subset of destination nodes in a network, optimizing several conflicting objectives. We propose a routing model based on SPEA2 (Strength Pareto Evolutionary Algorithm 2) to handle it, incorporating a heuristic that performs a reconnection step in crossover and mutation operators in order to produce a new tree. Three different heuristics were designed for such step. Experimental results were conducted to assess convergence and diversity goals over well-known instances of the problem, showing that the heuristic which alternates between shortest path and randomness produced the best results on most cases. It was shown that the proposed model compares well with traditional algorithms, namely, Dijkstra’s algorithm and Takahashi-Matsuyama heuristic.

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References

  1. Aneja, Y. P. (1980). An integer programming approach to the steiner problem in graphs. Networks, 10, 167–178.

    Article  Google Scholar 

  2. Aragão, M. P., Ribeiro, C. C., Uchoa, E., & Werneck, R. F. (2001). Hybrid local search for the steiner problem in graphs. In Extended abstracts of the 4th metaheuristics international conference (pp. 429–433).

    Google Scholar 

  3. Awduche, D., Chiu, A., Elwalid, A., Widjaja, I., & Xiao, X. (2002). Overview and principles of Internet traffic engineering.

    Google Scholar 

  4. Beasley, J. (1990). OR-Library: Distributing test problems by electronic mail. Journal of the Operational Research Society, 41, 1069–1072.

    Article  Google Scholar 

  5. Bueno, M. L. P., & Oliveira, G. M. B. (2010). Algorithms to augment diversity and convergence in multiobjective multicast flow routing. In 11th IEEE Brazilian symposium on neural networks (SBRN’10), São Bernardo do Campo, Brasil (pp. 158–163).

    Chapter  Google Scholar 

  6. Bueno, M. L. P., & Oliveira, G. M. B. (2010). Multicast flow routing: evaluation of heuristics and multiobjective evolutionary algorithms. In IEEE Congress on evolutionary computation (CEC’10), Barcelona, Spain (pp. 1–8).

    Chapter  Google Scholar 

  7. Bueno, M. L. P., & Oliveira, G. M. B. (2010). Multiobjective evolutionary algorithms and a combined heuristic for route reconnection applied to multicast flow routing. In 10th IEEE international conference on computer and information technology (CIT’10), Bradford, UK (pp. 464–471).

    Chapter  Google Scholar 

  8. Bueno, M. L. P., & Oliveira, G. M. B. (2010). Pareto-based optimization of multicast flows with QoS and traffic engineering requirements. In 9th IEEE international symposium on network computing and applications (NCA’10), Cambridge, USA (pp. 257–260).

    Chapter  Google Scholar 

  9. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to algorithms (2nd ed.). Cambridge: MIT Press.

    Google Scholar 

  10. Crichigno, J., & Barán, B. (2004). Multiobjective multicast routing algorithm for traffic engineering. In R. P. Luijten, L. A. DaSilva & A. P. J. Engbersen (Eds.), ICCCN (pp. 301–306). New York: IEEE Press.

    Google Scholar 

  11. Cui, X., Lin, C., & Wei, Y. (2003). A multiobjective model for QoS multicast routing based on genetic algorithm. In ICCNMC’03: proceedings of the 2003 international conference on computer networks and mobile computing (p. 49). Washington: IEEE Comput. Soc..

    Google Scholar 

  12. Dreyfus, S. E., & Wagner, R. A. (1971). The steiner problem in graphs. Networks, 1, 195–207.

    Article  Google Scholar 

  13. Fabregat, R., Donoso, Y., Baran, B., Solano, F., & Marzo, J. L. (2005). Multi-objective optimization scheme for multicast flows: a survey, a model and a MOEA solution. In LANC’05: proc. of the 3rd IFIP Latin American conf. on networking (pp. 73–86). New York: ACM.

    Chapter  Google Scholar 

  14. Ghaboosi, N., & Haghighat, A. (2007). Tabu search based algorithms for bandwidth-delay-constrained least-cost multicast routing. Telecommunications Systems, 34, 147–166. doi:10.1007/s11235-007-9031-7.

    Article  Google Scholar 

  15. Goldberg, D. E. (1989). Genetic algorithms in search, optimization & machine learning. Massachusetts: Addison-Wesley.

    Google Scholar 

  16. Karp, R. M. (1972). Reducibility among combinatorial problems. In R. E. Miller & J. W. Thatcher (Eds.), Complexity of computer computations (pp. 85–103).

    Chapter  Google Scholar 

  17. Kompella, V. P., Pasquale, J. C., & Polyzos, G. C. (1993). Multicast routing for multimedia communication. IEEE/ACM Transactions on Networking, 1(3), 286–292.

    Article  Google Scholar 

  18. Oliveira, G. M. B., & Araújo, P. T. (2004) Determining multicast routes with QoS and traffic engineering requirements based on genetic algorithm. In Proc. of the 2004 IEEE conference on cybernetics and intelligent systems, Singapore, December 2004 (Vol. 1, pp. 665–669).

    Google Scholar 

  19. Oliveira, G. M. B., & Vita, S. S. B. V. (2009). A multi-objective evolutionary algorithm with ϵ-dominance to calculate multicast routes with QoS requirements. In 2009 IEEE Congress on evolutionary computation (CEC’2009), Norway (pp. 1–9).

    Google Scholar 

  20. Prathombutr, P., Stach, J. F., & Park, E. K. (2005). An algorithm for traffic grooming in wdm optical mesh networks with multiple objectives. Telecommunications Systems, 28(3–4), 369–386.

    Article  Google Scholar 

  21. Prieto, J., Barán, B., & Crichigno, J. (2006). Multitree-multiobjective multicast routing for traffic engineering. In M. Bramer (Ed.), IFIP AI (Vol. 217, pp. 247–256). Berlin: Springer.

    Google Scholar 

  22. Ravi, R. (1993). Steiner trees and beyond: approximation algorithms for network design. Ph.D. thesis, Departament of Computer Science—Brown University, Providence, Rhode Island.

  23. Ravikumar, C. P., & Bajpai, R. (1998). Source-based delay-bounded multicasting in multimedia networks. Computer Communications, 21(2), 126–132.

    Article  Google Scholar 

  24. Rouskas, G. N., & Baldine, I. (1997). Multicast routing with end-to-end delay and delay variation constraints. In Selected areas in communications, IEEE journal on communications (USA) (Vol. 15 of 3, pp. 346–356). New York: IEEE Press.

    Google Scholar 

  25. Rudolph, G. (1999). Evolutionary search under partially ordered fitness sets. In Proceedings of the international symposium on information science innovations in engineering of natural and artificial intelligent systems (ISI 2001) (pp. 818–822). Rochester: ICSC Academic Press.

    Google Scholar 

  26. Siegel, E. D. (1999). Designing quality of service solutions for the enterprise. New York: Wiley

    Google Scholar 

  27. Skorin-Kapov, N., & Kos, M. (2006). A grasp heuristic for the delay-constrained multicast routing problem. Telecommunications Systems, 32(1), 55–69.

    Article  Google Scholar 

  28. Takahashi, H., & Matsuyama (1980). An approximate solution for the steiner problem in graphs. Mathematica Japonica, 24, 573–577.

    Google Scholar 

  29. Tanenbaum, A. S. (2002). Computer networks (4th ed.). New York: Prentice Hall.

    Google Scholar 

  30. Van Veldhuizen, D. A. (1999). Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. Ph.D. thesis, Wright-Patterson AFB, OH.

  31. Vita, S. S. B. V. (2009). Multiobjective genetic algorithms applied to multicast routing with quality of service. Master’s thesis, Universidade Federal de Uberlândia—Programa de Pós-Graduação em Ciência da Computação.

  32. Wang, H., Meng, X., Li, S., & Xu, H. (2010). A tree-based particle swarm optimization for multicast routing. Computer Networks, 54(15), 2775–2786.

    Article  Google Scholar 

  33. Wang, Z., & Crowcroft, J. (1996). Quality of service routing for supporting multimedia applications. IEEE Journal on Selected Areas in Communications, 14, 1228–1234.

    Article  Google Scholar 

  34. Xu, Y., & Qu, R. (2011). Solving multi-objective multicast routing problems by evolutionary multi-objective simulated annealing algorithms with variable neighbourhoods. Journal of the Operational Research Society, 62(2), 313–325.

    Article  Google Scholar 

  35. Zhengying, W., Bingxin, S., & Erdun, Z. (2001). Bandwidth-delay-constrained least-cost multicast routing based on heuristic ga. Computer Communications, 24(7–8), 685–692.

    Article  Google Scholar 

  36. Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation, 8, 173–195.

    Article  Google Scholar 

  37. Zitzler, E., Laumanns, M., & Bleuler, S. (2004). A tutorial on evolutionary multiobjective optimization. In X. Gandibleux et al. (Eds.), Lecture notes in economics and mathematical. Metaheuristics for multiobjective optimisation. Berlin: Springer.

    Google Scholar 

  38. Zitzler, E., Laumanns, M., & Thiele, L. (2001) SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In EUROGEN 2001.

    Google Scholar 

  39. Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3, 257–271.

    Article  Google Scholar 

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Acknowledgements

The authors are grateful for the financial support provided by Brazilian agencies CAPES, CNPq and FAPEMIG.

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Authors and Affiliations

  1. School of Computer Science, Federal University of Uberlândia, Av. João Naves de Ávila 2160, Bloco B. Uberlândia, Minas Gerais, 38400-902, Brazil

    Marcos L. P. Bueno & Gina M. B. Oliveira

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  1. Marcos L. P. Bueno
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  2. Gina M. B. Oliveira
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Correspondence to Marcos L. P. Bueno.

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Bueno, M.L.P., Oliveira, G.M.B. Four-objective formulations of multicast flows via evolutionary algorithms with quality demands. Telecommun Syst 55, 435–448 (2014). https://doi.org/10.1007/s11235-013-9797-8

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