Skip to main content
SpringerLink
Log in

Wind Driven Based Heuristic Solution for Multiobjective Traffic Grooming in Optical Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

One of the major problem faced by the optical networks is traffic grooming. The large size of data and the speedy transmission leads to traffic grooming. In this research paper, the wind driven optimization (WDO) technique has been used for optimizing the traffic grooming in the optical network. The paper describes the model of optical systems, WDM concepts, optimization and its components. Then, the issue of traffic grooming is discoursed in paper that includes information on its types, parameters, objective function, variables, and related constraints. The results achieved with the implementation of WDO algorithm proved to be cost effective. Two multiobjective indicators are used in the study. Results have been compared with multiobjective evolution algorithm based on decomposition (MOEA/D) and DEPT, i.e., differential evolution with Pareto tournaments in terms of multi-objective indicator hypervolume. Hypervolume is the effective quantative parameter that calculate the proximity of the calculated solution to the true pareto front. Multiobjective indicator set coverage values have also been evaluated using WDO and are compared with the setcoverage values obtained with the use of shortest path routing and first fit wavelength algorithm. Results obtained were quite promising in terms of hypervolume and setcoverage values both. Thus, WDO algorithm can be stated as optimizer as compared to existing techniques DEPT, MOEA/D and shortest path routing and first fit wavelength assignment method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Riziotis, C., & Vasilakos, A. V. (2007). Computational intelligence in photonics technology and optical networks. A survey and future perspectives. Information Sciences,177(23), 5292–5315. https://doi.org/10.1016/j.ins.2007.06.012.

    Article  Google Scholar 

  2. Wason, A., & Kaler, R. S. (2010). Routing and wavelength assignment in wavelength-routed all optical WDM networks. Optik,121(16), 1478–1486. https://doi.org/10.1016/j.ijleo.2009.02.012.

    Article  Google Scholar 

  3. Largo, A. R., Vega-Rodriguez, M. A., Gomez-Pulido, J. A., & Sanchez-Perez, J. M. (2013). Multiobjective metaheuristics for traffic grooming in optical networks. IEEE Transactions on Evolutionary Computation,17(4), 457–473. https://doi.org/10.1109/TEVC.2012.2204064.

    Article  Google Scholar 

  4. Zhang, Q., Xie, W., She, Q., Wang, X., Palacharla, P., & Sekiya, M. (2013). RWA for network virtualization in optical WDM networks. In Optical fiber communication conference and the national fiber optic engineers conference (pp. 17–21). Sacramento, CA: IEEE. https://doi.org/10.1364/NFOEC.2013.JTh2A.65.

  5. Pradhan, A. K., Singhi, S., & De, T. (2017). Multicast dynamic traffic grooming using bin packing method in WDM mesh networks. Optical Switching and Networking,23(1), 40–51. https://doi.org/10.1016/j.osn.2016.08.004.

    Article  Google Scholar 

  6. Chen, M. T., Lin, B. M. T., & Tseng, S. S. (2011). Ant colony optimization for dynamic routing and wavelength assignment in WDM networks with sparse wavelength conversion. Engineering Applications of Artificial Intelligence,24(2), 295–305. https://doi.org/10.1016/j.engappai.2010.05.010.

    Article  Google Scholar 

  7. Wason, A., & Kaler, R. S. (2011). Generic routing and wavelength assignment algorithm for a wavelength-routed WDM network. Optik,122(12), 1100–1106. https://doi.org/10.1016/j.ijleo.2010.06.048.

    Article  Google Scholar 

  8. Farahmand, F., Huang, X., & Jue, J. P. (2004). Efficient online traffic grooming algorithm in WDM mesh network with drop and continue node architecture. In Proceedings of international conference on broadband networks (pp. 180–189). Sacramento, CA. https://doi.org/10.1109/BROADNETS.2004.37.

  9. Largo, A. R., Zhang, Q., & Vega-Rodríguez, M. A. (2013). MOEA/D for traffic grooming in WDM optical networks. In GECCO 2013—Proceedings of the 2013 genetic and evolutionary computation conference (pp. 663–670). Amsterdam. https://doi.org/10.1145/2463372.2463443.

  10. Vignac, B., Jaumard, B., & Vanderbeck, F. (2009). Hierarchial optimization procedure for traffic grooming in WDM optical networks. In Proceedings of international conference on optical network design and modelling (pp. 1–6). Braunschweig.

  11. Xu, Z., Huang, J., Zhou, Z., Ding, Z., Ma, T., & Wang, J. (2013). A novel grooming algorithm with adaptive weight and load balancing for dynamic holding time aware traffic in optical networks. Optical Fiber Technology,19(5), 392–399. https://doi.org/10.1016/j.yofte.2013.05.003.

    Article  Google Scholar 

  12. Mohamed, T. A. A., & Khalaf, G. A. F. M. (2013). Online traffic grooming using timing information in WDM–TDM networks. Ain Shams Engineering Journal,4(1), 55–63. https://doi.org/10.1016/j.asej.2012.06.007.

    Article  Google Scholar 

  13. Pradhan, A. K., & De, T. (2013). Multicast traffic grooming based light-tree in WDM Mesh networks. Procedia Technology,10, 900–909. https://doi.org/10.1016/j.protcy.2013.12.436.

    Article  Google Scholar 

  14. Bermond, J. C., Braud, L., & Coudert, D. (2007). Traffic grooming on the path. Theoretical Computer Science,384(2–3), 139–151. https://doi.org/10.1016/j.tcs.2007.04.028.

    Article  MathSciNet  MATH  Google Scholar 

  15. Antonakopoulos, S., & Zhang, L. (2011). Approximation algorithms for grooming in optical network design. Theoretical Computer Science,412(29), 3738–3751. https://doi.org/10.1016/j.tcs.2011.03.034.

    Article  MathSciNet  MATH  Google Scholar 

  16. Shinde, S., Patil, S. H., & Gulwani, M. (2015). Modified multiobjective metaheuristics for space traffic grooming in optical WDM Mesh networks. Procedia Computer Science,57, 980–987.

    Article  Google Scholar 

  17. Donado, F. S., Caro, L. F., Oliveira, J. C., Fabregat, R., & Marzo, J. L. (2007). G+: Enhanced traffic grooming in WDM Mesh networks using lighttours. IEEE Journal on Selected Areas in Communications,25(5), 1034–1047. https://doi.org/10.1109/JSAC.2007.070615.

    Article  Google Scholar 

  18. Yao, W., & Ramamurthy, B. (2005). Survivable traffic grooming with path protection at the connection level in WDM mesh networks. Journal of Lightwave Technology,23(10), 2846–2853. https://doi.org/10.1109/JLT.2005.856269.

    Article  Google Scholar 

  19. Chiu, A. L., & Modiano, E. H. (2000). Traffic grooming algorithms for reducing electronic multiplexing costs in WDM ring networks. Journal of Lightwave Technology,18(1), 2–12. https://doi.org/10.1109/50.818901.

    Article  Google Scholar 

  20. Kaur, S., & Kumar, A. (2015). Application of wind driven optimization algorithm for clustering in WSN to achieve energy efficiency. International Journal of Scientific Research and Development,3(6), 374–377.

    Google Scholar 

  21. Siddique, N., & Adeli, H. (2015). Nature inspired computing: An overview and some future directions. Cognitive Computation,7(6), 706–714.

    Article  Google Scholar 

  22. Bao, Z., Zhou, Y., Li, L., & Ma, M. (2015). A hybrid global optimization algorithm based on wind driven optimization and differential evolution. Mathematical Problems in Engineering,2015, 1–20. https://doi.org/10.1155/2015/389630.

    Article  Google Scholar 

  23. Bayraktar, Z., Komurcu, M., Bossard, J. A., & Werner, D. H. (2013). The wind-driven optimization technique and its application in electromagnetics. IEEE Transactions on Antennas and Propagation,61(5), 2745–2757. https://doi.org/10.1109/TAP.2013.2238654.

    Article  MathSciNet  MATH  Google Scholar 

  24. Zhu, K., & Mukherjee, B. (2002). Traffic grooming in an optical WDM mesh network. IEEE Journal on Selected Areas in Communications,20(1), 122–133. https://doi.org/10.1109/49.974667.

    Article  Google Scholar 

  25. Konda, V. R., & Chow, T. Y. (2001). Algorithm for traffic grooming in optical networks to minimize the number of transceivers. In IEEE workshop on high-performance switching and routing. Dallas, TX. https://doi.org/10.1109/hpsr.2001.923635.

  26. Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation,11(6), 712–731. https://doi.org/10.1109/TEVC.2007.892759.

    Article  Google Scholar 

  27. Miettinen, K. M. (1999). Nonlinear multiobjective optimization. Dordrecht: Kluwer.

    MATH  Google Scholar 

  28. Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation,3(4), 257–271. https://doi.org/10.1109/4235.797969.

    Article  Google Scholar 

  29. Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation,8(2), 173–195. https://doi.org/10.1162/106365600568202.

    Article  Google Scholar 

  30. Tan, Y. Y., Jiao, Y. C., Li, H., & Wang, X. K. (2013). MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives. Computers and Operations Research,40(6), 1648–1660. https://doi.org/10.1016/j.cor.2012.01.001.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

I would like to thank the Dean RIC, Inder Kumar Gujral Punjab Technical University Jalandhar (Kapurthala) for providing each and every facility for carrying out this research work.

Author information

Authors and Affiliations

  1. I. K. Gujral Punjab Technical University Jalandhar, Kapurthala, Punjab, India

    Harpreet Kaur

  2. Department of Electronics and Communication Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India

    Munish Rattan

Authors
  1. Harpreet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Munish Rattan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harpreet Kaur.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, H., Rattan, M. Wind Driven Based Heuristic Solution for Multiobjective Traffic Grooming in Optical Networks. Wireless Pers Commun 110, 1475–1491 (2020). https://doi.org/10.1007/s11277-019-06796-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06796-y

Keywords

Search

Navigation