Genetic algorithms for designing energy-efficient optical transport networks with mixed regenerator placement

We design genetic algorithms (GA) to solve the mixed regenerator placement (MRP) problem of lightpaths with different lengths in optical transport networks, and investigate their performance with numerical simulations. By incorporating a theoretical model that can estimate BER changes hop-by-hop along lightpaths, the GA encodes the placements of 1R/2R/3R at intermediate regeneration sites as genes, and takes quality-of-transmission (QoT) and energy-efficiency as the fitness functions. With a relatively small population size (e.g. 30-50), the algorithms obtain multiple qualified MRP results that can satisfy both the QoT and energy requirements within 32 generations, for lightpaths with lengths up to 28 hops. Two crossover and two mutation operators are investigated within the GA. By adjusting the possibilities of the crossover and mutation intelligently, the adaptive scheme outperforms the uniform one by getting a larger percentage of fit individuals. We also propose two selection operators, and demonstrate an adjustable tradeoff between QoT and energy-efficiency.