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
Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
Photonic Network Communications
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
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
Network topology planning using MOEA/D with objective-guided operators
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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Optical networks have attracted much more attention in the last decades due to its huge bandwidth (Tbps). The Wavelength Division Multiplexing (WDM) is a technology that aims to make the most of this networks by dividing each single fiber link into several wavelengths of light or channels. Each channel operates in the range of Gbps; unfortunately, the requirements of the vast majority of current traffic connection requests are a few Mbps, causing a waste of bandwidth at each channel. We can solve this drawback by equipping each optical node with an access station for multiplexing or grooming several low-speed requests onto one single high-speed channel. This problem of grooming low-speed requests is known in the literature as the Traffic Grooming problem. In this work, we formulate the Traffic Grooming problem as a Multiobjective Optimization Problem, optimizing simultaneously the total throughput, the number of transceivers used, and the average propagation delay. We propose the use of the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D). The experiments are conducted on three optical network topologies and diverse scenarios. The results report that the MOEA/D algorithm works more efficiently than other multiobjective approaches and other single-objective heuristics published in the literature.