Wavelength Assignment for Physical-Layer-Impaired Optical Networks Using Evolutionary Computation

This paper presents a wavelength assignment algorithm suitable for optical networks mainly impaired by physical layer effects, named the Intelligent Wavelength Assignment algorithm (iWA). The main idea is to determine the wavelength activation order for a first-fit algorithm that balances the impact of the physical layer effects by using a training algorithm inspired by evolutionary concepts. The iWA presents some recently proposed concepts in intelligent optimization algorithms, such as an external archive to store the best solutions. Some different physical layer effects, such as four-wave mixing and residual dispersion, were considered in the tests of our proposal. We tested our proposal for transparent optical networks. However, we believe iWA can be used in other types of network, such as opaque networks and translucent networks. The proposed wavelength assignment algorithm was compared with five other wavelength assignment algorithms for two network topologies in three different scenarios. The iWA algorithm outperformed the other WA algorithms in most cases. The robustness of our proposed algorithm to the load distribution changes was also analyzed.