Paper Info
Title
An improved multiobjective approach inspired by the flashing behaviour of fireflies for Traffic Grooming in optical WDM networks.
Abstract
Nowadays, the traffic demands in optical networks are low-speed traffic requests (low bandwidth requirement of a few Mbps) that employ the huge capacity of a fiber channel (Gbps), causing a waste of bandwidth as a result. Fortunately, by using electronic grooming nodes, we can multiplex (groom) several low-speed demands onto one channel in order to optimize the available resources in an optical network. The problem of grooming low-speed traffic requests is known in the literature as the Traffic Grooming problem and is considered an NP-hard optimization problem. In this work, we use both multiobjective optimization and evolutionary computation with the aim of facing this optical networking problem. The selected evolutionary algorithm is based in the behaviour of fireflies, the Firefly Algorithm (FA). In order to optimize more than one conflicting objective function of the Traffic Grooming problem simultaneously, we have modified the standard FA to the multiobjective domain (MO-FA). After carrying out different experiments with diverse real-world optical networks, comparing the results of the MO-FA with other multiobjective approaches and different standard heuristics for this problem, we can conclude saying that the new version of the MO-FA is an effective approach for dealing with this telecommunication problem. (C) 2014 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2014
10.1016/j.asoc.2014.03.046
Applied Soft Computing
Keywords
Field
DocType
Optical networks,Traffic Grooming,Multiobjective optimization,Firefly algorithm
Mathematical optimization,Evolutionary algorithm,Simulation,Evolutionary computation,Computer network,Multi-objective optimization,Firefly algorithm,Bandwidth (signal processing),Optical networking,Optimization problem,Mathematics,Traffic grooming
Journal
Volume
ISSN
Citations 
21
1568-4946
2
PageRank 
References 
Authors
0.36
32
3
Name
Order
Citations
PageRank
Alvaro Rubio-Largo19813.00
Miguel A. Vega-Rodríguez2741113.05
David L. González-Álvarez310712.72