A Self-Driven and Adaptive Adjusting Teaching Learning Method for Optimizing Optical Multicast Network Throughput

Huanlin Liu; Yifan Xu; Yong Chen; Mingjia Zhang

doi:10.1515/joc-2015-0068

Article

A Self-Driven and Adaptive Adjusting Teaching Learning Method for Optimizing Optical Multicast Network Throughput

Huanlin Liu , Yifan Xu , Yong Chen and Mingjia Zhang

Published/Copyright: October 14, 2015

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From the journal Journal of Optical Communications Volume 37 Issue 3

Abstract

With the development of one point to multiple point applications, network resources become scarcer and wavelength channels become more crowded in optical networks. To improve the bandwidth utilization, the multicast routing algorithm based on network coding can greatly increase the resource utilization, but it is most difficult to maximize the network throughput owing to ignoring the differences between the multicast receiving nodes. For making full use of the destination nodes’ receives ability to maximize optical multicast’s network throughput, a new optical multicast routing algorithm based on teaching-learning-based optimization (MR-iTLBO) is proposed in the paper. In order to increase the diversity of learning, a self-driven learning method is adopted in MR-iTLBO algorithm, and the mutation operator of genetic algorithm is introduced to prevent the algorithm into a local optimum. For increasing learner’s learning efficiency, an adaptive learning factor is designed to adjust the learning process. Moreover, the reconfiguration scheme based on probability vector is devised to expand its global search capability in MR-iTLBO algorithm. The simulation results show that performance in terms of network throughput and convergence rate has been improved significantly with respect to the TLBO and the variant TLBO.

Keywords: optical multicast; multicast routing; self-driven learning; adaptive adjusting learning; network coding; network throughput

PACS: 01.30.Bb

Funding statement: Funding: This research was supported by national nature science foundation of China (NSFC 61275077), by the national program on key basic research project of China (2012CB315803), and by the basic and frontier research program of Chongqing (CSTC 2015jcyjA0075).

References

1. Huang Q, Zhong W. A wavelength-routed multicast packet switch with a shared-FDL buffer. J Lightwave Technol 2010;28:2822–9.10.1109/JLT.2010.2066260Search in Google Scholar

2. Zhang S, Han Peng P, Sui M, et al. A multicast sparse-grooming algorithm based on network coding in WDM networks. J Opt Commun 2015;36:17–23.10.1515/joc-2014-0046Search in Google Scholar

3. Lai CP, Bergman K. Broadband multicasting for wavelength-striped optical packets. J Lightwave Technol 2012;30:1706–18.10.1109/JLT.2012.2188276Search in Google Scholar

4. Ahlswede R, Cai N, Li SY, et al. Network information flow. IEEE Trans Inf Theory 2000;46:1204–16.10.1109/18.850663Search in Google Scholar

5. Liu H, Hu X, Chen Y, et al. Scheduling based on minimal conversion degree with respect to wavelength conversion and coding in optical multicast node. IEEE Commun Lett 2014;18:1935–8.10.1109/LCOMM.2014.2356457Search in Google Scholar

6. Huang S, Wang Y, Liu HL, et al. Multi-source multi-core routing algorithm based on network coding in optical multicast network. J Chongqing Univ Post Telecommun 2014;26:143–9.Search in Google Scholar

7. Liu H, Hu T, Chen Y, et al. Optimisation of layer rate and wavelength allocation based on network coding for multirate optical multicast. IET Commun (COM) 2014;8:1570–7.10.1049/iet-com.2013.0690Search in Google Scholar

8. Huang J, Liang S. Construction of static maximum decodable network coding. Comput Eng 2012;38:108–9.Search in Google Scholar

9. Luo L, Qin T, Luo J, et al. A routing algorithm for network coding multicast based on shareable links. Telecommun Eng 2011;51:79–83.Search in Google Scholar

10. Xing H, Qu R. A nondominated sorting genetic algorithm for bi-objective network coding based multicast routing problems. Inf Sci 2013;233:36–53.10.1016/j.ins.2013.01.014Search in Google Scholar

11. Xing H, Qu R. A compact genetic algorithm for the network coding based resource minimization problem. Appl Intell 2012;36:809–23.10.1007/s10489-011-0298-8Search in Google Scholar

12. Liu HL, Xue X, Li RY, et al. An improved genetic simulated annealing algorithm to optimize coding operations in optical multicast network. J Optoelectron Laser 2014;25:1098–103.Search in Google Scholar

13. Rao RV, Savsani VJ, Vakharia DP. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Des 2011;43:303–15.10.1016/j.cad.2010.12.015Search in Google Scholar

14. Zou F, Wang L, Hei X, et al. Teaching-learning-based optimization with dynamic group strategy for global optimization. Information Science 2014;273:112–31.10.1016/j.ins.2014.03.038Search in Google Scholar

15. Durai S, Subramanian S, Ganesan S. Improved parameters for economic dispatch problems by teaching learning optimization. Int J Electr Power Energy Syst 2015;67:11–24.10.1016/j.ijepes.2014.11.010Search in Google Scholar

16. Rao RV. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 2016;7:19–34.Search in Google Scholar

17. Chen D, Zou F, Wang J, et al. A teaching-learning-based optimization algorithm with producer-scrounger model for global optimization. Soft Comput 2015;19:745–62.10.1007/s00500-014-1298-5Search in Google Scholar

18. Mohankrishna S, Naik A, Satapathy SC, et al. Numerical Optimization of Novel Functions Using vTLBO Algorithm. Proc Int Conf Front Intell Comput: Theory Appl (FICTA) 2014;247:229–47.10.1007/978-3-319-02931-3_27Search in Google Scholar

19. Ali M, Deogun JS. Cost-effective implementation of multicasting in wavelength-routed networks. J Lightwave Technol 2000;18:1628–38.10.1109/50.908667Search in Google Scholar

Received: 2015-8-10

Accepted: 2015-9-1

Published Online: 2015-10-14

Published in Print: 2016-9-1

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Articles in the same Issue

https://doi.org/10.1515/joc-2015-0068

Keywords for this article

optical multicast; multicast routing; self-driven learning; adaptive adjusting learning; network coding; network throughput