Routing, Modulation Level and Spectrum Allocation in EONs via Genetic Algorithm Under Different Assignment Criteria

This work investigates the impact of ordering the demands using different criteria when a Genetic Algorithm (GA) is used to solve the Routing, Modulation Level, and Spectrum Assignment (RMLSA) problem in Elastic Optical Networks (EONs). The algorithm is configured to minimize blocking and spectral fragmentation ratios. The individuals encode the routes for the demands and the resources are allocated over the routes (and the network) depending on their rates, the number of spectral slices required, or randomly. We propose to analyze the performance statistically. The results demonstrate that the three assignment schemes present similar performances. The 90<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentiles for all measured parameters are very similar, indicating that ordering the input demands for assigning the resources does not significantly affect the performance of the proposed GA, highlighting the effectiveness of the proposed GA. Nevertheless, depending on the trade-offs to fulfill, one may be more advisable than the others in certain cases.