Wavelength Converters Placement in All Optical Networks Using Particle Swarm Optimization

Wavelength Converters Placement in All Optical Networks Using Particle Swarm Optimization Placement of wavelength converters in an arbitrary mesh network is known to be a NP-complete problem. So far, this problem has been solved by heuristic strategies or by the application of optimization tools such as genetic algorithms. In this paper, we introduce a novel evolutionary algorithm: particle swarm optimization (PSO) to find the optimal solution to the converters placement problem. The major advantage of this algorithm is that does not need to build up a search tree or to create auxiliary graphs in find the optimal solutions. In addition, the computed results show that only a few particles are needed to search the optimal solutions of the placement of wavelength converters problem in an arbitrary network. Experiments have been conducted to demonstrate the effectiveness and efficiency of the proposed evolutionary algorithm. It was found that the efficiency of PSO can even exceed 90% under certain circumstances. In order to further improve the efficiency in obtaining the optimal solutions, four strategic initialization schemes are investigated and compared with the random initializations of PSO particles. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Photonic Network Communications Springer Journals

Wavelength Converters Placement in All Optical Networks Using Particle Swarm Optimization

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Publisher
Springer Journals
Copyright
Copyright © 2005 by Springer Science+Business Media, Inc.
Subject
Computer Science; Computer Communication Networks; Electrical Engineering; Characterization and Evaluation of Materials
ISSN
1387-974X
eISSN
1572-8188
D.O.I.
10.1007/s11107-005-1693-z
Publisher site
See Article on Publisher Site

Abstract

Placement of wavelength converters in an arbitrary mesh network is known to be a NP-complete problem. So far, this problem has been solved by heuristic strategies or by the application of optimization tools such as genetic algorithms. In this paper, we introduce a novel evolutionary algorithm: particle swarm optimization (PSO) to find the optimal solution to the converters placement problem. The major advantage of this algorithm is that does not need to build up a search tree or to create auxiliary graphs in find the optimal solutions. In addition, the computed results show that only a few particles are needed to search the optimal solutions of the placement of wavelength converters problem in an arbitrary network. Experiments have been conducted to demonstrate the effectiveness and efficiency of the proposed evolutionary algorithm. It was found that the efficiency of PSO can even exceed 90% under certain circumstances. In order to further improve the efficiency in obtaining the optimal solutions, four strategic initialization schemes are investigated and compared with the random initializations of PSO particles.

Journal

Photonic Network CommunicationsSpringer Journals

Published: Jan 12, 2005

References

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