A biased random-key genetic algorithm to maximize the number of accepted lightpaths in WDM optical networks

Julliany S. Brandão (), Thiago F. Noronha () and Celso C. Ribeiro ()
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Julliany S. Brandão: Universidade Federal Fluminense (UFF)
Thiago F. Noronha: Universidade Federal de Minas Gerais (UFMG)
Celso C. Ribeiro: Universidade Federal Fluminense (UFF)

Journal of Global Optimization, 2016, vol. 65, issue 4, No 8, 813-835

Abstract: Abstract Given a set of lightpath requests, the problem of routing and wavelength (RWA) assignment in wavelength division multiplexing (WDM) optical networks consists in routing a subset of these requests and assigning a wavelength to each of them, such that two lightpaths that share a common link are assigned to different wavelengths. There are many variants of this problem in the literature. We focus in the variant in which the objective is to maximize the number of requests that may be accepted, given a limited set of available wavelengths. This problem is called max-RWA and it is of practical and theoretical interest, because algorithms for this variant can be extended for other RWA problems that arise from the design of WDM optical networks. A number of exact algorithms based on integer programming formulations have been proposed in the literature to solve max-RWA, as well as algorithms to provide upper bounds to the optimal solution value. However, the algorithms based on the state-of-the-art formulations in the literature cannot solve the largest instances to optimality. For these instances, only upper bounds to the value of the optimal solutions are known. The literature on heuristics for max-RWA is short and focus mainly on solving small size instances with up to 27 nodes. In this paper, we propose new greedy constructive heuristics and a biased random-key genetic algorithm, based on the best of the proposed greedy heuristics. Computational experiments showed that the new heuristic outperforms the best ones in literature. Furthermore, for the largest instances in the literature where only upper bounds to the value of the optimal solutions are known, the average optimality gap of the best of the proposed heuristics is smaller than 4 %.

Keywords: Random-key genetic algorithms; Metaheuristics; Optical networks; Routing and wavelength assignment (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10898-015-0389-x

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