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Wavelength Converters Placement in Optical Networks Using Bee Colony OptimizationMARKOVIC, G. Z.
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artificial intelligence, computational complexity, optical fiber networks, wavelength converters, wavelength division multiplexing
wavelength(26), networks(22), optical(19), placement(14), converter(11), communications(9), converters(7), algorithm(7), optimal(6), optimization(5)
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About this article
Date of Publication: 2016-02-28
Volume 16, Issue 1, Year 2016, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.01001
Web of Science Accession Number: 000376995400001
SCOPUS ID: 84960106375
Wavelength converters placement (WCP) in all-optical WDM networks belongs to the class of hard combinatorial optimization problems. So far, this problem has been solved by various heuristic strategies or by application of metaheuristic approaches such as genetic algorithms (GA), particle swarm optimization (PSO), differential evolution (DE), etc. In this paper, we introduce the application of Bee Colony Optimization (BCO) metaheuristic to solve the WCP problem in all-optical WDM networks. Numerous studies prove that BCO is a fast, robust and computationally efficient tool in tackling complex optimization problems. The objective of the proposed BCO-WCP algorithm is to find the best placement of limited number of wavelength converters in given optical network such that the overall network blocking probability is minimized. To evaluate the performances of the BCO-WCP algorithm, numerous simulation experiments have been performed over some realistic optical network examples. The blocking probability performance and computational complexity are compared with optimal solution obtained by exhaustive search (ES) approach as well as with DE and PSO metaheuristics. It will be shown that the BCO-WCP algorithm is not only be able to produce high quality (optimal) solution, but significantly outperforms the computational efficiency of other considered approaches.
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