Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Multicriteria Optimization
Metaheuristics for optimization problems in computer communications
Computer Communications
Multiobjective network design for realistic traffic models
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Survivable and delay-guaranteed backbone wireless mesh network design
Journal of Parallel and Distributed Computing
A new approach for designing fault-tolerant WDM networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Multi-objective Design for Routing Wavelength Assignment in WDM Networks
NISS '09 Proceedings of the 2009 International Conference on New Trends in Information and Service Science
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Design methodology for WDM backbone networks using FWM-aware heuristic algorithm
Optical Switching and Networking
Applying MOEAs to solve the static Routing and Wavelength Assignment problem in optical WDM networks
Engineering Applications of Artificial Intelligence
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Routing and wavelength assignment (RWA) is a well-known issue in wavelength division multiplexing (WDM) optical networks. In this paper, we present RWA design for WDM networks by considering multiple design objectives which are maximizing the number of traffic demands to be served and minimizing the number of wavelength channels to be assigned. A hybrid evolutionary computation approach consisting of genetic algorithm for routing allocation with minimum degree first for wavelength assignment (GA-MDF) and the fast non-dominated sorting genetic algorithm (NSGA-II) to search for non-dominated solutions is applied for solving the multi-objective RWA network design problem. The hybrid evolutionary algorithm is used as a meta-heuristic technique for obtaining good solutions for various problem sizes. The obtained results are provided as candidate choices or non-dominated front. We compare the simulation results obtained from the NSGA-II with those obtained from the traditional Weighted-Sum approach. Numerical results show that our hybrid evolutionary computation approach is effective in solving the multi-objective RWA problem. The GA-MDF can outperform the FAR-FF method in both design objectives. In addition, the solutions from the NSGA-II are more diverse on the multi-objective space than those of the Weighted-Sum method. We also apply a Pruned mechanism to help cutting off numerous non-dominated solutions for making decision on the final solution.