Advertisement

Variable Neighborhood Multiobjective Genetic Algorithm for the Optimization of Routes on IP Networks

  • Renata E. Onety
  • Gladston J. P. Moreira
  • Oriane M. Neto
  • Ricardo H. C. Takahashi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)

Abstract

This paper proposes an algorithm to optimize multiple indices of Quality of Service of Multi Protocol Label Switching (MPLS) IP networks. The proposed algorithm, the Variable Neighborhood Multiobjective Genetic Algorithm (VN-MGA), is a Genetic Algorithm based on the NSGA-II, with the particular feature that different parts of a solution are encoded differently, at Level 1 and Level 2. In order to improve the results, both representations are needed. At Level 1, the first part of the solution is encoded, by considering as decision variables, the arrows that form the routes to be followed by each request (whilst the second part of the solution is kept constant), whereas at Level 2, the second part of the solution is encoded, by considering as decision variables, the sequence of requests, and first part is kept constant. The preliminary results shown here indicate that the proposed approach is promising, since the Pareto-fronts obtained by VN-MGA dominate the fronts obtained by fixed-neighborhood encoding schemes. Besides the potential benefits of the application of the proposed approach to the optimization of packet routing in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as generic operators inside general evolutionary computation algorithms.

Keywords

Routing on IP Networks Variable Neighborhood Search Multi-objective Genetic Algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alvarado, C., Herazo, I., Ardila, C., Donoso, Y.: Aplicación de NSGA-II y SPEA-II para la optimización multiobjetivo de redes multicast. Ingeniería y Desarrollo 17, 28–53 (2005)Google Scholar
  2. 2.
    Andrade, A.V.: Provisionamento de Qualidade de Serviço em Redes MPLS utilizando Algoritmos Bio-inspirados em um Ambiente de Trfego Auto-Similar. Ph.D. thesis, UFMG (December 2008)Google Scholar
  3. 3.
    Awduche, D., Chiu, A., Networks, C., Elwalid, A.: Overview and Principles of Internet Traffic Engineering. Request for Comments 3272 (May 2002)Google Scholar
  4. 4.
    Carrano, E.G., Soares, L.A.E., Takahashi, R.H.C., Saldanha, R.R., Neto, O.M.: Electric distribution network multiobjective design using a problem-specific genetic algorithm. IEEE Trans. Power Delivery 21(2), 995–1005 (2006)CrossRefGoogle Scholar
  5. 5.
    Carrano, E.G., Takahashi, R.H.C., Fonseca, C.M., Neto, O.M.: Non-linear network topology optimization - An embedding vector space approach. IEEE Transactions on Evolutionary Computation 14, 206–226 (2010)CrossRefGoogle Scholar
  6. 6.
    Carrano, E.G., Moreira, L.A., Takahashi, R.H.C.: A new memory based variable-length encoding genetic algorithm for multiobjective optimization. In: Proceedings of the 6th Int. Conf. Evolutionary Multicriterion Optimization, EMO 2011 (2011) (submitted)Google Scholar
  7. 7.
    De Giovanni, L., Della Croce, F., Tadei, R.: On the impact of the solution representation for the Internet Protocol Network Design Problem with max-hop constraints. Networks 44(2), 73–83 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 2(6), 182–197 (2002)CrossRefGoogle Scholar
  9. 9.
    Dias, R.A.: Engenharia de Trafego em Redes IP sobre Tecnologia MPLS: Otimizaçao Baseada em Heurısticas. Ph.D. thesis, UFSC (2004)Google Scholar
  10. 10.
    Ehrgott, M.: Multicriteria Optimization. Springer, Heidelberg (2000)CrossRefzbMATHGoogle Scholar
  11. 11.
    Erbas, S., Erbas, C.: A multiobjective off-line routing model for MPLS networks. In: Providing Quality of Service in Heterogeneous Environments-Proc. of the 18th International Teletraffic Congress (ITC-18), vol. 16, pp. 471–480. Citeseer (2003)Google Scholar
  12. 12.
    de Freitas, L.M.B., Montané, F.A.T.: Metaheurísticas vns-vnd e grasp-vnd para problemas de roteamento de veículos com coleta e entrega simultânea. In: XI Simpósio de Pesquisa Operacional e Logística da Marinha (2008)Google Scholar
  13. 13.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Boston (1989)zbMATHGoogle Scholar
  14. 14.
    Hansen, P., Mladenovi, N.: Variable Neighborhood Search: Principles and applications. European Journal of Operational Research 130(3), 449–467 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Maia, N.A., de Errico, L., Caminhas, W.M.: Combining MPLS, Computational Intelligence, and Autonomic Computing into a Self-Managing Traffic Engineering System.. In: Proceedings of the Second Latin American Autonomic Computing Symposium LAACS 2007, pp. 1–6 (2007)Google Scholar
  16. 16.
    Mladenovi, N., Hansen, P.: Variable neighborhood search. Computers & Operations Research 24(11), 1097–1100 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Moraglio, A., Kim, Y.-H., Yoon, Y., Moon, B.-R.: Geometric crossovers for multiway graph partitioning. Evolutionary Computation 15(4), 445–474 (2007)CrossRefGoogle Scholar
  18. 18.
    Paul, P., Raghavan, S.: Survey of QoS routing. In: Proceedings of the International Conference on Computer Communication, vol. 15, p. 50. Citeseer (2002)Google Scholar
  19. 19.
    Perboli, G., Pezzella, F., Tadei, R.: EVE-OPT: a hybrid algorithm for the capacitated vehicle routing problem. Mathematical Methods of Operations Research 68(2), 361–382 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Santos, F.A.: Otimização Multiobjetivo aplicada a alocação dinâmica de rotas em redes de telecomunicações. Master’s thesis, UFMG (March 2009)Google Scholar
  21. 21.
    de Souza Filho, E.M.: Variable Neighborhood Search (VNS) aplicado ao problema de distribuição dutoviária. Master’s thesis, UFRJ - Universidade Federal do Rio de Janeiro (2007)Google Scholar
  22. 22.
    Wang, Z., Crowcroft, J.: Quality-of-service routing for supporting multimedia applications. IEEE Journal on Selected areas in communications 14(7), 1228–1234 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Renata E. Onety
    • 1
  • Gladston J. P. Moreira
    • 2
  • Oriane M. Neto
    • 1
  • Ricardo H. C. Takahashi
    • 3
  1. 1.School of Electrical EngineeringUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Universidade Federal dos Vales do Jequitinhonha e MucuriTeófilo OtoniBrazil
  3. 3.Dep. of MathematicsUniversidade Federal de Minas GeraisBelo HorizonteBrazil

Personalised recommendations