Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-State Routing

  • Vitor Pereira
  • Pedro Sousa
  • Paulo Cortez
  • Miguel Rio
  • Miguel RochaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9019)


Traffic Engineering (TE) approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.


Multi-objective evolutionary algorithms Traffic engineering NSGA SPEA Intra-domain routing OSPF 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Altin, A., Fortz, B., Thorup, M., Ümit, H.: Intra-domain traffic engineering with shortest path routing protocols. Annals of Operations Research 204(1), 56–95 (2013)CrossRefGoogle Scholar
  2. 2.
    Awduche, D., Malcolm, J., Agogbua, J., O’Dell, M., McManus, J.: Requirements for Traffic Engineering Over MPLS. RFC 2702 (Informational), September 1999Google Scholar
  3. 3.
    Claise, B.: RFC 3954 - Cisco Systems NetFlow Services Export Version 9, October 2004Google Scholar
  4. 4.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  5. 5.
    Dijkstra, E.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Evangelista, P., Maia, P., Rocha, M.: Implementing metaheuristic optimization algorithms with jecoli. In: Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications, ISDA ’09, pp. 505–510. IEEE Computer Society, Washington, DC, USA (2009)Google Scholar
  7. 7.
    Fortz, B.: Internet traffic engineering by optimizing ospf weights. In: Proceedings of IEEE INFOCOM, pp. 519–528 (2000)Google Scholar
  8. 8.
    Fortz, B., Thorup, M.: Optimizing ospf/is-is weights in a changing world. IEEE Journal on Selected Areas in Communications 20(4), 756–767 (2002)CrossRefGoogle Scholar
  9. 9.
    Iannaccone, G., Chuah, C., Mortier, R., Bhattacharyya, S., Diot, C.: Analysis of link failures in an ip backbone. In: Proceedings of the 2Nd ACM SIGCOMM Workshop on Internet Measurment, IMW ’02, pp. 237–242. ACM, New York, NY, USA (2002)Google Scholar
  10. 10.
    Medina, A., Lakhina, A., Matta, I., Byers, J.: Brite: Universal topology generation from a users perspective. Technical report, Boston, MA, USA (2001)Google Scholar
  11. 11.
    Moy, J.: OSPF Version 2. RFC 2328 (Standard), April 1998. Updated by RFC 5709Google Scholar
  12. 12.
    Oran, D.: OSI IS-IS Intra-domain Routing Protocol. Technical report, IETF, February 1990Google Scholar
  13. 13.
    Pereira, Vitor, Rocha, Miguel, Cortez, Paulo, Rio, Miguel, Sousa, Pedro: A Framework for Robust Traffic Engineering Using Evolutionary Computation. In: Doyen, Guillaume, Waldburger, Martin, Čeleda, Pavel, Sperotto, Anna, Stiller, Burkhard (eds.) AIMS 2013. LNCS, vol. 7943, pp. 1–12. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  14. 14.
    Rocha, M., Sousa, P., Cortez, P., Rio, M.: Quality of Service Constrained Routing Optimization Using Evolutionary Computation. Applied Soft Computing 11(1), 356–364 (2011)CrossRefGoogle Scholar
  15. 15.
    Tan, K.C., Lee, T.H., Khor, E.F.: Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons. Artif. Intell. Rev. 17(4), pp. 251–290, June 2002Google Scholar
  16. 16.
    Zitzler, E., Laumanns, M., Thiele, L.: Spea 2: Improving the strength pareto evolutionary algorithm. Technical report (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vitor Pereira
    • 1
  • Pedro Sousa
    • 1
  • Paulo Cortez
    • 2
  • Miguel Rio
    • 3
  • Miguel Rocha
    • 4
    Email author
  1. 1.Centro Algoritmi/Department of InformaticsUniversity of MinhoBragaPortugal
  2. 2.Centro Algoritmi/Department of Information SystemsUniversity of MinhoBragaPortugal
  3. 3.Department of Electronic and Electrical EngineeringUniversity College LondonLondonUK
  4. 4.Centre Biological Engineering/Department of InformaticsUniversity of MinhoBragaPortugal

Personalised recommendations