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Pareto Based Bat Algorithm for Multi Objectives Multiple Constraints Optimization in GMPLS Networks

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The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (AMLTA 2018)

Abstract

Modern communication networks offer advance and diverse applications, which require huge usage of network resources while providing quality of services to the users. Advance communication is based on multiple switched networks that cannot be handle by traditional IP (internet protocol) networks. GMPLS (Generalized multiprotocol label switched) networks, an advance version of MPLS (multiprotocol label switched networks), are introduced for multiple switched networks. Traffic engineering in GMPLS networks ensures traffic movement on multiple paths. Optimal path(s) computation can be dependent on multiple objectives with multiple constraints. From optimization prospective, it is an NP (non-deterministic polynomial-time) hard optimization problem, to compute optimal paths based on multiple objectives having multiple constraints. The paper proposed a metaheuristic Pareto based Bat algorithm, which uses two objective functions; routing costs and load balancing costs to compute the optimal path(s) as an optimal solution for traffic engineering in MPLS/GMPLS networks. The proposed algorithm has implemented on different number of nodes in MPLS/GMPLS networks, to analysis the algorithm performance.

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References

  1. Walrand, J., Varaiya, P.: High-Performance Communication Networks. Elsevier Science, San Francisco (1999)

    MATH  Google Scholar 

  2. Mazandu, G.: Traffic Engineering Using Multipath Routing Approaches (2007)

    Google Scholar 

  3. Liu, H., Zhang, X., Wang, D., Xu, G.: An algorithm for end-to-end performance analysis of network based on traffic engineering. J. Electron. (China) 20(4), 293–298 (2003)

    Article  Google Scholar 

  4. Girão-Silva, R., Craveirinha, J., Clímaco, J., Captivo, M.: Multiobjective routing in multiservice MPLS networks with traffic splitting — a network flow approach. J. Syst. Sci. Syst. Eng. 24(4), 389–432 (2015)

    Article  Google Scholar 

  5. Ramadža, I., Ožegović, J., Pekić, V.: Network performance monitoring within MPLS traffic engineering enabled networks. In: 2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE (2015)

    Google Scholar 

  6. Lv, M., Ji, W.: Research on GMPLS traffic engineering mechanism. In: IEEE 13th International Conference on Communication Technology (ICCT). IEEE (2011)

    Google Scholar 

  7. Masood, M., Abuhelala, M., Glesk, I.: A comprehensive study of routing protocols performance with topological changes in standard networks. Int. J. Electron. Electr. Comput. Syst. 5(8), 31–40 (2016)

    Google Scholar 

  8. Farrel, A., Bryskin, I.: GMPLS. Elsevier/Morgan Kaufman, San Francisco (2006)

    Google Scholar 

  9. El-Alfy, E., Mujahid, S., Selim, S.: A Pareto-based hybrid multiobjective evolutionary approach for constrained multipath traffic engineering optimization in MPLS/GMPLS networks. J. Netw. Comput. Appl. 36(4), 1196–1207 (2013)

    Article  Google Scholar 

  10. Erbas, S.C., Erbas, C.: A multiobjective off-line routing model for MPLS networks. In: Proceedings of the 18th International Teletraffic Congress (2003)

    Google Scholar 

  11. Yang, X.S.: A new metaheuristic Bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol. 284. Springer, Heidelberg (2010)

    Google Scholar 

  12. Malakooti, B., Kim, H., Sheikh, S.: Bat intelligence search with application to multi-objective multiprocessor scheduling optimization. Int. J. Adv. Manuf. Technol. 60(9–12), 1071–1086 (2011)

    Google Scholar 

  13. Castelo Damasceno, N., Gabriel Filho, O.: PI controller optimization for a heat exchanger through metaheuristic Bat algorithm, particle swarm optimization, flower pollination algorithm and Cuckoo search algorithm. IEEE Lat. Am. Trans. 15(9), 1801–1807 (2017)

    Article  Google Scholar 

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Correspondence to Mohsin Masood .

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Masood, M., Fouad, M.M., Glesk, I. (2018). Pareto Based Bat Algorithm for Multi Objectives Multiple Constraints Optimization in GMPLS Networks. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-74690-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74689-0

  • Online ISBN: 978-3-319-74690-6

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