In this paper, we propose an evolutionary multiobjective regenerator placement strategy for elastic optical networks (eMORP). The proposed optimization strategy uses the genetic algorithm NSGA-II to determine non-dominated solutions when the call request blocking probability and the total amount of regenerators used in the network are taken into account. In our simulations, we considered the amplified spontaneous emission noise generated by optical amplifiers (in-line, booster, and pre-amplifier) as physical impairment. Two recently proposed heuristics for regenerator assignment have been used, together with the regenerator placement strategies proposed and analyzed in this paper, for comparison purpose. The results obtained for two different network physical topologies state the efficiency of eMORP. Our regenerator placement strategy reduced considerably the call request blocking probability for the same number of regenerators in the network, as well as it acquired efficient solutions with just a fraction of the nodes with regeneration capability in comparison to other heuristics presented in the literature.

In this paper we propose efficient operators for a well known multi-objective evolutionary optimizer, called NSGA II, applied to design all-optical networks regarding the network topology and the device specifications in order to both minimize the capital expenditure to build the network and to maximize the overall network performance. From the experiments, we perceived that it is better to use an uniform crossover, to include preferences a priori and to initialize the individuals emphasizing the network topology.