Genetic evolutionary algorithm for optimal allocation of wavelength converters in WDM optical networks

Genetic evolutionary algorithm for optimal allocation of wavelength converters in WDM optical... In this article, a genetic evolutionary algorithm is proposed for efficient allocation of wavelength converters in WDM optical networks. Since wavelength converters are expensive, it is desirable that each node in WDM optical networks uses a minimum number of wavelength converters to achieve a near-ideal performance. The searching capability of genetic evolutionary algorithm has been exploited for this purpose. The distinguished feature of the proposed approach lies in handling the conflicting circumstances during allocation of wavelength converters considering various practical aspects (e.g., spatial problem, connectivity of a node with other nodes) rather than arbitrarily to possibly improve the overall blocking performance of WDM optical networks. The proposed algorithm is compared with a previous approach to establish its effectiveness and the results demonstrate the ability of the proposed algorithm to efficiently solve the problem of Optimal Wavelength Converters Allocation (OWCA) in practical WDM optical networks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Photonic Network Communications Springer Journals

Genetic evolutionary algorithm for optimal allocation of wavelength converters in WDM optical networks

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Publisher
Springer Journals
Copyright
Copyright © 2008 by Springer Science+Business Media, LLC
Subject
Computer Science; Computer Communication Networks; Electrical Engineering; Characterization and Evaluation of Materials
ISSN
1387-974X
eISSN
1572-8188
D.O.I.
10.1007/s11107-008-0115-4
Publisher site
See Article on Publisher Site

Abstract

In this article, a genetic evolutionary algorithm is proposed for efficient allocation of wavelength converters in WDM optical networks. Since wavelength converters are expensive, it is desirable that each node in WDM optical networks uses a minimum number of wavelength converters to achieve a near-ideal performance. The searching capability of genetic evolutionary algorithm has been exploited for this purpose. The distinguished feature of the proposed approach lies in handling the conflicting circumstances during allocation of wavelength converters considering various practical aspects (e.g., spatial problem, connectivity of a node with other nodes) rather than arbitrarily to possibly improve the overall blocking performance of WDM optical networks. The proposed algorithm is compared with a previous approach to establish its effectiveness and the results demonstrate the ability of the proposed algorithm to efficiently solve the problem of Optimal Wavelength Converters Allocation (OWCA) in practical WDM optical networks.

Journal

Photonic Network CommunicationsSpringer Journals

Published: Feb 23, 2008

References

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