CFP last date
20 May 2024
Reseach Article

Study of Path Optimization in Packet Switching Network using Neural Network

Published on November 2012 by Zaiba Ishrat, Sarvesh Kumar Sharma
Issues and Challenges in Networking, Intelligence and Computing Technologies
Foundation of Computer Science USA
ICNICT - Number 1
November 2012
Authors: Zaiba Ishrat, Sarvesh Kumar Sharma
4462d815-7b5f-4dbb-b81c-616c10367e7b

Zaiba Ishrat, Sarvesh Kumar Sharma . Study of Path Optimization in Packet Switching Network using Neural Network. Issues and Challenges in Networking, Intelligence and Computing Technologies. ICNICT, 1 (November 2012), 23-25.

@article{
author = { Zaiba Ishrat, Sarvesh Kumar Sharma },
title = { Study of Path Optimization in Packet Switching Network using Neural Network },
journal = { Issues and Challenges in Networking, Intelligence and Computing Technologies },
issue_date = { November 2012 },
volume = { ICNICT },
number = { 1 },
month = { November },
year = { 2012 },
issn = 0975-8887,
pages = { 23-25 },
numpages = 3,
url = { /specialissues/icnict/number1/9016-1017/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Issues and Challenges in Networking, Intelligence and Computing Technologies
%A Zaiba Ishrat
%A Sarvesh Kumar Sharma
%T Study of Path Optimization in Packet Switching Network using Neural Network
%J Issues and Challenges in Networking, Intelligence and Computing Technologies
%@ 0975-8887
%V ICNICT
%N 1
%P 23-25
%D 2012
%I International Journal of Computer Applications
Abstract

The problem of finding the optimal path between two nodes is a well known problem in network analysis. Optimal routing has been widely studied for interconnection networks this paper work considers the problem of finding the optimal path. A Genetic algorithm based strategy is proposed and the algorithm has been developed to find the Optimal Path. This paper work presents a neural network based approach to the shortest path routing problem. Weights adjustment of the neurons has been used for solving the problem of optimum path. This paper presents the back propagation algorithm to solve the problem of optimum path in multi layer feed forward (MLFF) network. Even though shortest path routing algorithms are already well established, there are researchers who are trying to find alternative methods to find shortest paths through a network. One such alternative is to use of neural network.

References
  1. E. W. Dijkstra, "A note on two papers in connection with graphs," Numeriske Mathematics 1 pp. 269-271 1959.
  2. D. Eppstein, "Finding the k shortest paths," SIAM journal on Computing 28(2) pp. 653-674 1998.
  3. R. W Floyd, "Algorithm97: Shortest paths, "Communications of the ACM 5 pp. 345-357 1962.
  4. Baransel C,Dobosiewicz W, Gburzynski p, "Routing in multihop packet switching networks:Gb/s challenges,"IEEE Network 9(3) pp. 38-61 1995.
  5. Suk-Gwon C, "Fair integration of routing and flow control in communication networks,"IEEE Trans Commun 40(4) pp. 821-34 1992.
  6. K. B. Kumar and J. Jaffe, "Routing to multiple destinations in computer networks", IEEE Transactions on Communications, vol. 31,no. 3, pp. 343-351, 1983.
  7. M. K. Ali and F. Kamoun, "Neural networks for shortest path computation and routing in computer networks," IEEE Trans. Neural Networks, vol. 4, pp. 941-954, Nov. 1993.
  8. D. C. Park and S. E. Choi, "A neural network based multi-destination routing algorithm for communication network," in Proc. Joint Conf. Neural Networks, 1998, pp. 1673-1678.
  9. C. W. Ahn, R. S. Ramakrishna, C. G. Rang, and I. C. Choi, "Shortest path routing algorithm using hopfield neural neural network," Electron. Lett. , vol. 37, no. 19, pp. 1176-1178, Sept. 2001.
  10. M. Munemoto, Y. Takai, and Y. Sato, "A migration scheme for the genetic adaptive routing algorithm," in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, 1998, pp. 2774-2779.
  11. J. Inagaki, M. Haseyama, and H. Kitajima, "A genetic algorithm for de-termining multiple routes and its applications," in Proc. IEEE Int. Symp. Circuits and Systems, 1999, pp. 137-140.
  12. Y. Leung, G. Li, and Z. B. Xu, "A genetic algorithm for the multiple destination routing problems," IEEE Trans. Evol. Comput, vol. 2, pp. 150-161, Nov. 1998.
  13. G. Syswerda, "Uniform crossover in genetic algorithms," in Proc. 3rd Int. Conf. Genetic Algorithms. San Mateo, CA: Morgan Kaufmann, 1989, pp. 2-9.
  14. M. Parsa and Q. Zhu, "An iterative algorithm for delay-constrained minimum-cost multicasting", IEEE/ACM Transactions on Networking, vol. 6, no. 4, pp. 461 – 474, 1998.
  15. Neural Networks,Fuzzy logic and Ganetic algorithm by S. Rajasekaran and G. A. Vijjylakashmi pai H. Rauch and T. Winarske, "Neural networks for routing communication traffic," IEEE Cont. Syst. Mag. , pp. 26–30, Apr. 1988.
  16. P. Soueres and J. -P. Laumond, "Shortest paths synthesis for a car-like robot," IEEE Trans. Automat. Contr. , vol. 41, pp. 672–688, 1996.
  17. E. L. Lawler, Combinatorial Optimization: Networks and Matroids. New York: Holt, Rinehart, and Winston, 1976, pp. 59–108.
  18. J. J. Hopfield and D. W. Tank, "Neural computation of decisions in optimization problems," Biol. Cybern. , vol. 52, pp. 141–152, 1985.
Index Terms

Computer Science
Information Sciences

Keywords

Shortest Path Neural Network Optimization Packet Switching Mlff Activation Function Learning Rates Algorithms