Abstract
We proposed a new algorithm for packet routing problems using chaotic neurodynamics and analyze its statistical behavior. First, we construct a basic neural network which works in the same way as the Dijkstra algorithm that uses information of shortest path lengths from a node to another node in a computer network. When the computer network has a regular topology, the basic routing method works well. However, when the computer network has an irregular topology, it fails to work, because most of packets cannot be transmitted to their destinations due to packet congestion in the computer network. To avoid such an undesirable problem, we extended the basic neural network to employ chaotic neurodynamics. We confirm that our proposed method exhibits good performance for complex networks, such as scale-free networks.
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References
Bertsekas, D., Gallager, R.: Data Networks. Prentice Hall, Englewood Cliffs (1987)
Hasegawa, M., Ikeguchi, T., Aihara, K.: Exponential and chaotic neurodynamics tabu searches for quadratic assignment problems. Control and Cybernetics 29, 773–788 (2000)
Hasegawa, M., Ikeguchi, T., Aihara, K.: Combination of chaotic neurodynamics with the 2-opt algorithm to solve traveling salesman problems. Physical Reveiw Letters 79, 2344–2347 (1997)
Hasegawa, M., Ikeguchi, T., Aihara, K.: Solving large scale traveling salesman problems by chaotic neurodynamics. Neural Networks 15, 271–283 (2002)
Hasegawa, M., Ikeguchi, T., Aihara, K., Itoh, K.: A novel chaotic search for quadratic assignment problems. European J. Oper. Res. 139, 543–556 (2002)
Glover, F.: Tabu search I. ORSA Journal on Computing 1, 190–206 (1989)
Glover, F.: Tabu search II. ORSA Journal on Computing 2, 4–32 (1990)
TSPLIB, http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB995
Mardhana, E., Ikeguchi, T.: Neurosearch: A program library for neural network driven search meta-heuristics. In: Proceedings of 2003 IEEE International Symposium on Circuits and Systems, May 2003, vol. V, pp. 697–700 (2003)
Matsuura, T., Ikeguchi, T., Horio, Y.: Tabu search and chaotic search for extracting motifs from DNA sequences. In: Proceedings of the 6th Metaheuristics International Conference, August 2005, pp. 677–682 (2005)
Ikeguchi, T.: Combinatorial optimization with chaotic dynamics. In: Proceedings of 2005 RISP International Workshop on Nonlinear Circuits and Signal Processing, March 2005, pp. 263–266 (2005)
Hoshino, T., Kimura, T., Ikeguchi, T.: Solving vehicle routing problems with soft time window. Tech. Rep of IEICE 105, 17–22 (2006) (in Japanese)
Aihara, K., Tanabe, T., Toyoda, M.: Chaotic neural network. Physics Letters A 144, 333–340 (1990)
Barábsi, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)
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Kimura, T., Ikeguchi, T. (2006). Chaotic Dynamics for Avoiding Congestion in the Computer Network. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_44
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DOI: https://doi.org/10.1007/11875581_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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