CFP last date
20 December 2024
Reseach Article

A Novel Hybrid Multi–objective BB–BC based Channel Allocation Algorithm to Reduce FWM Crosstalk and its Comparative Study

by Suruchi Bali, Shonak Bansal, Anil Kamboj
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 12
Year of Publication: 2015
Authors: Suruchi Bali, Shonak Bansal, Anil Kamboj
10.5120/ijca2015905702

Suruchi Bali, Shonak Bansal, Anil Kamboj . A Novel Hybrid Multi–objective BB–BC based Channel Allocation Algorithm to Reduce FWM Crosstalk and its Comparative Study. International Journal of Computer Applications. 124, 12 ( August 2015), 38-45. DOI=10.5120/ijca2015905702

@article{ 10.5120/ijca2015905702,
author = { Suruchi Bali, Shonak Bansal, Anil Kamboj },
title = { A Novel Hybrid Multi–objective BB–BC based Channel Allocation Algorithm to Reduce FWM Crosstalk and its Comparative Study },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 12 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 38-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number12/22160-2015905702/ },
doi = { 10.5120/ijca2015905702 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:15.852728+05:30
%A Suruchi Bali
%A Shonak Bansal
%A Anil Kamboj
%T A Novel Hybrid Multi–objective BB–BC based Channel Allocation Algorithm to Reduce FWM Crosstalk and its Comparative Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 12
%P 38-45
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nature is a good source of inspirations for us. The algorithms developed from the nature are most powerful algorithms for optimizing many complex engineering design problems having multiple objectives (multi–objective). This paper presents an hybrid algorithm based on Multi–objective Big bang–Big Crunch (MOBB–BC) nature–inspired optimization algorithm with Genetic crossover and Differential evolution (DE) mutation operators for solving the minimum length ruler called Optimal Golomb ruler (OGR) as channel–allocation problem to reduce four–wave mixing crosstalk (FWM) effects in optical wavelength division multiplexing (WDM) systems. The comparative study of simulation results obtained by proposed hybrid Multi–objective BB–BC (HMOBB–BC) algorithm demonstrates better and efficient generation of OGRs in a reasonable computational time compared to simple BB–BC algorithm and one of the existing nature–inspired algorithms i.e. Genetic algorithm (GA). Also, the proposed hybrid algorithm outperforms the two existing conventional algorithms i.e. Extended quadratic congruence (EQC) and Search algorithm (SA), in terms of ruler length and total channel bandwidth.

References
  1. Chraplyvy, A. R. 1990. Limitations on Lightwave Communications Imposed by Optical–Fiber Nonlinearities, J. Lightwave Technology, Vol. 8, pp. 1548–1557.
  2. Kwong, W. C., and Yang, G. C. 1997. An Algebraic Approach to the Unequal-Spaced Channel-Allocation Problem in WDM Lightwave Systems, IEEE Transactions on Communications, Vol. 45, No.3, pp. 352–359.
  3. Saaid, N. M. 2010. Nonlinear Optical Effects Suppression Methods in WDM Systems with EDFAs: A Review, In Proceedings of the International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia.
  4. Aggarwal, G. P. 2001. Nonlinear Fiber Optics, Edition, Academic Press, San Diego, CA.
  5. Thing, V. L. L., Shum, P., and Rao, M. K. (2004). Bandwidth–Efficient WDM Channel Allocation for Four-Wave Mixing-Effect Minimization, IEEE Transactions on Communications, Vol. 52, No. 12, pp. 2184–2189.
  6. Forghieri, F., Tkach, R. W., Chraplyvy, A. R., and Marcuse, D. 1994. Reduction of Four–Wave Mixing Crosstalk in WDM Systems Using Unequally Spaced Channels. IEEE Photonics Technology Letters, Vol. 6, No. 6, pp. 754–756.
  7. Babcock, W. C. 1953. Intermodulation interference in radio systems, Bell Systems Technical Journal, pp. 63–73.
  8. Sardesai, H. P. 1999. A Simple Channel Plan to Reduce Effects of Nonlinearities In Dense WDM Systems. Lasers and Electro–Optics, (23–28, May–1999), pp. 183–184.
  9. Forghieri, F., Tkach, R. W., and Chraplyvy, A. R. 1995. WDM systems with unequally spaced channels. J. Lightwave Technol., Vol. 13, pp. 889–897.
  10. Hwang, B. and Tonguz, O. K. 1998. A Generalized Suboptimum Unequally Spaced Channel Allocation Technique—Part I: In IM/DD WDM systems. IEEE Trans. Commun., Vol. 46, pp. 1027–1037.
  11. Tonguz, O. K. and Hwang B. 1998. A Generalized Suboptimum Unequally Spaced Channel Allocation Technique—Part II: In coherent WDM systems. IEEE Trans. Commun., Vol. 46, pp. 1186–1193.
  12. Atkinson, M. D., Santoro, N., and Urrutia, J. 1986. Integer sets with distinct sums and differences and carrier frequency assignments for nonlinear repeaters. IEEE Trans. Commun., Vol. COM-34.
  13. Randhawa, R., Sohal, J. S. and Kaler, R. S. 2009. Optimum Algorithm for WDM Channel Allocation for Reducing Four-Wave Mixing Effects. Optik120, pp. 898–904.
  14. http://www.compunity.org/events/pastevents/ewomp204/jaillet_krajecki_pap_ew04.pdf.
  15. Bloom, G. S. and Golomb, S.W. 1977. Applications of Numbered Undirected Graphs. In Proceedings of the IEEE, Vol. 65, No. 4, pp. 562–570.
  16. Thing, V. L. L., Rao, M. K. and Shum, P. 2003. Fractional Optimal Golomb Ruler Based WDM Channel Allocation. In Proceedings of the 8th Opto–Electronics International Journal of Computer Applications (0975 – 8887) Volume 85 – No 9, January 201425and Communication Conference (OECC–2003), Vol. 23, pp. 631-632.
  17. Shearer, J. B. 1998. Some New Disjoint Golomb Rulers. IEEE Transactions on Information Theory, Vol. 44, No. 7, pp. 3151–3153.
  18. http://theinf1.informatik.unijena.de/teaching/ss10/oberseminar-ss10
  19. Robinson, J. P. 1979. Optimum Golomb Rulers. IEEE Transactions on Computers, Vol. C-28, No. 12, (December 1979), pp. 943–944.
  20. Shearer, J. B. 1990. Some New Optimum Golomb Rulers. IEEE Transactions on Information Theory. IT-36, pp. 183–184.
  21. Galinier, P., Jaumard, B., Morales, R. and Pesant, G. 2001. A constraint–Based Approach to the Golomb Ruler Problem. In Proceeding of 3rd International workshop on integration of AI and OR techniques (CP–AI–OR 2001).
  22. Leitao, T. 2004. Evolving the Maximum Segment Length of a Golomb Ruler. Genetic and Evolutionary Computation Conference, USA.
  23. Rankin, W. T. 1993. Optimal Golomb Rulers: An exhaustive parallel search implementation. M.S.thesis,DukeUniversity,Availableathttp://people.ee.duke.edu/~wrankin/golomb/golomb.htm.
  24. Shobhika. 2005. Generation of Golomb Ruler Sequences and Optimization Using Genetic Algorithm. M.Tech. Thesis, Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Deemed University, Patiala.
  25. Soliday, S. W., Homaifar, A. and Lebby, G. L. 1995. Genetic Algorithm Approach to the Search for Golomb Rulers. In Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA–95), Morgan Kaufmann, pp. 528–535.
  26. Robinson, J. P. 2000. Genetic Search for Golomb Arrays. IEEE Transactions on Information Theory, Vol. 46, No. 3, pp. 1170–1173.
  27. Ayari, N., Luong, T. V. and Jemai, A. 2010. A Hybrid Genetic Algorithm for Golomb Ruler Problem. In Proceeding of ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2010), pp.1–4.
  28. Bansal, S., 2014. Optimal Golomb Ruler Sequence Generation for FWM Crosstalk Elimination: Soft Computing Versus Conventional Approaches. Applied Soft Computing Journal (Elsevier), Vol. 22, pp. 443–457.
  29. Bansal, S., Kumar, S., Sharma, H. and Bhalla, P. 2011. Generation of Golomb Ruler Sequences and Optimization Using Biogeography Based Optimization. In Proceedings of 5th International Multi Conference on Intelligent Systems, Sustainable, New and Renewable Energy Technology and Nanotechnology (IISN–2011), Institute of Science and Technology Klawad, Haryana, pp 282–288.
  30. Bansal, S., Kumar, S., Sharma, H. and Bhalla, P. 2011. Golomb Ruler Sequences Optimization: A BBO Approach. International Journal of Computer Science and Information Security (IJCSIS), Pittsburgh, PA, USA, Vol. 9, No. 5, pp. 63–71.
  31. Kumar S., Bansal S. and Bhalla P. 2012. Optimal Golomb Ruler Sequence Generation for FWM Crosstalk Elimination: A BB–BC Approach. In Proceedings of 6th International Multi Conference on Intelligent Systems, Sustainable, New and Renewable Energy Technology and Nanotechnology (IISN–2012), Institute of Science and Technology Klawad–133105, Haryana, India, pp. 255–262.
  32. Bansal S., Kumar S. and Bhalla P. 2013. A Novel Approach to WDM Channel Allocation: Big Bang–Big Crunch Optimization. In the proceeding of Zonal Seminar on Emerging Trends in Embedded System Technologies (ETECH-2013) organized by The Institution of Electronics and Telecommunication Engineers (IETE), Chandigarh Centre, Chandigarh, pp. 80–81.
  33. Bansal, S. and Singh, K., 2014. A Novel Soft–Computing Algorithm for Channel Allocation in WDM Systems. International Journal of Computer Applications (IJCA), Vol. 85, No. 9, pp. 19–26.
  34. Bansal, S., Chauhan, R. and Kumar, P., 2014. A Cuckoo Search based WDM Channel Allocation Algorithm. International Journal of Computer Applications (IJCA), Vol. 96, No. 20, pp. 6–12.
  35. Jain, P., Bansal, S., Singh, A. K. and Gupta, N., 2015. Golomb Ruler Sequences Optimization for FWM Crosstalk Reduction: Multi–population Hybrid Flower Pollination Algorithm. Progress in Electromagnetics Research Symposium (PIERS), Prague, Czech Republic, pp. 2463–2467.
  36. Horn, J., Nafbliotis, N., and Goldberg, D. E. 1994. A Niched Pareto Genetic Algorithm for Multiobjective Optimization. Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence, Proceedings of the first IEEE Conference on, Vol. 1, pp 82-87.
  37. Yang, X.–S., Karamanoglu, M., and He., X. S. 2014. Flower Pollination Algorithm: A Novel Approach for Multi-objective Optimization. Engineering Optimization, Vol. 46, Issue 9, pp. 1222–1237, doi: 10.1080/0305215x.2013.832237.
  38. Dimitromanolakis, A. 2002. Analysis of the Golomb Ruler and the Sidon Set Problems, and Determination of Large, Near-Optimal Golomb Rulers. Master's Thesis, Department of Electronic and Computer Engineering, Technical University of Crete.
  39. Dollas, A., Rankin, W. T., and McCracken, D. 1998. A New Algorithm for Golomb Ruler Derivation and Proof of the 19 Mark Rulers. IEEE Transactions on Information Theory, Vol. 44, No. 1, pp. 379–382.
  40. Project OGR. http://www.distributed.net/OGR.
  41. Cotta, C., Dotu, I., Fernandez, Antonio J., and Hentenryck, Pascal V. 2007. Local Search-Based Hybrid Algorithms for Finding Golomb Rulers. Kluwer Academic Publishers, Boston, Vol. 12, Issue 3, pp. 263–291.
  42. http://mathworld.wolfram.com/PerfectRuler.html
  43. http://mathworld.wolfram.com/GolombRuler.html
  44. Afshar, M. H., and Motaei, I. 2011. Constrained Big Bang-Big Crunch Algorithm For Optimal Solution of Large Scale Reservoir Operation Problem, International Journal of Optimization In Civil Engineering, pp. 357–375.
  45. Tabakov, P. Y. 2011. Big Bang–Big Crunch Optimization Method in Optimum Design of Complex Composite Laminates, World Academy of Science, Engineering and Technology, Vol. 77, pp. 835–839.
  46. Ahmadi, S. and Sedighizadeh, M. 2014. An Efficient Hybrid Big Bang-Big Crunch Algorithm for Reconfiguration of Distribution System for Loss Reduction, in Conference and exhibition on Electricity Distribution, Vol 14-E-aaa-0000.
  47. Erol, O. K. and Eksin, I. 2006. A New Optimization Method: Big Bang–Big Crunch. Advances in Engineering Software, Vol.37, pp. 106–111.
  48. Yesil, E. and Urbas, L. 2010. Big Bang–Big Crunch Learning Method for Fuzzy Cognitive Maps, World Academy of Science, Engineering and Technology 71, pp. 815–824.
  49. Zandi, Z., Afjei, E., and M. Sedighizadeh. Hybrid Big Bang–Big Crunch Optimization Based Optimal Reactive Power Dispatch for Voltage Stability Enhancement. In Electrical and Computer Engineering Department, Shahid Beheshti University, G.C., Velenjak, Tehran, Iran, Vol. 47, No.2, pp. 537–546.
  50. Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, USA.
  51. Price, K., Storn, R. and Lampinen. J. 2005. Differential Evolution–A Practical Approach to Global Optimization. Springer, Berlin, Germany.
  52. Storn, R., and Price, K. V. 1997. Differential Evolution—A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces. Journal of Global Optimization, Vol. 11, No. 4, pp. 341–359.
  53. http://in.mathworks.com/help/matlab/index.html.
  54. Shearer, J. B., 2001. Golomb Ruler Table. Mathematics Department, IBM Research. Available at http://www.research.ibm.com/people/s/shearer/grtab.html.
  55. Colannino, J. 2003. Circular and Modular Golomb Rulers.
  56. Shearer, J. B. Smallest Known Golomb Rulers. Mathematics Department, IBM Research. Available at http://www.research.ibm.com/people/s/shearer/gropt.html
Index Terms

Computer Science
Information Sciences

Keywords

Channel spacing Genetic algorithm Hybrid Multi–objective Big bang–Big Crunch optimization algorithm Optimal Golomb ruler.