Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Proceedings of the 4th international IFIP/ACM Latin American conference on Networking
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A survey of multicasting protocols for broadcast-and-select single-hop networks
IEEE Network: The Magazine of Global Internetworking
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One of the most favorable technology for exploiting the huge bandwidth of optical networks is known as Wavelength Division Multiplexing (WDM). Given a set of demands, the problem of setting up all connection requests is known as Routing and Wavelength Assignment (RWA) problem. In this work, we suggest the use of computational swarm intelligent for solving the RWA problem. A new heuristic based on the law of gravity and mass interactions (Gravitational Search Algorithm, GSA) is chosen for this purpose, but adapted to a multiobjective context (MO-GSA). To test the performance of theMO-GSA, we have used a real-world topology, the Nippon Telegraph and Telephone (NTT, Japan). network and six sets of demands. After performing several comparisons with other approaches published in the literature, we can conclude that this algorithm outperforms the results obtained by other authors.