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Abstract
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The core networks of current telecommunication infrastructures are typically engineered as multi-layer networks. The uppermost layer is defined by the virtual topology, which determines the logical connections between core network routers. This topology is realized by optical paths in the lower layer, which is defined by optical fiber connections between network nodes. Minimizing the hardware cost incurred by these optical paths for a given set of traffic demands is a common combinatorial optimization problem in network planning, often approached by Mixed-Integer Linear Programming. However, increasing network densities and the introduction of additional constraints will impact tractability of future network problems. In order to provide a more scalable method, we suggest a Genetic Algorithm-based approach that optimizes the virtual topology and subsequently derives the remaining parameters from it. Our genetic encoding utilizes a combination of spanning trees and augmentation links to quickly form meaningful topologies. We compare the results of our approach to known linear programming solutions in simple scenarios and to a competing heuristic based on Simulated Annealing in large-scale problems.
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Reference entry
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Bauknecht, U.
A Genetic Algorithm Approach to Virtual Topology Design for Multi-Layer Communication Networks
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, July 2021, pp. 928-936
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