Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A novel generic graph model for traffic grooming in heterogeneous WDM mesh networks
IEEE/ACM Transactions on Networking (TON)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Photonic Network Communications
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Traffic grooming in an optical WDM mesh network
IEEE Journal on Selected Areas in Communications
Network Dimensioning under Scheduled and Random Lightpath Demands in All-Optical WDM Networks
IEEE Journal on Selected Areas in Communications - Part Supplement
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Nowadays, the bandwidth requirements of the majority of traffic connection requests are in the range of Mbps. However, in optical networks each physical link is able to operate in the range of Gbps causing a huge waste of bandwidth as a result. Fortunately, using access station at each node of the optical network, several low-speed traffic requests may be multiplexed onto one high-speed channel. Multiplexing or grooming these low-speed requests is known in the literature as the Traffic Grooming problem - an NP-hard problem. Therefore, in this paper we propose the use of Evolutionary Computation for solving this telecommunication problem. The selected algorithm is an approach inspired by the flash pattern and characteristics of fireflies, the Firefly Algorithm (FA), but adapted to the multiobjective domain (MO-FA). After performing several experiments and comparing the results obtained by the MO-FA with those obtained by other approaches published in the literature, we can conclude that it is a good approach for solving this problem.