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The future of designing optical networks is focused on the wavelength division multiplexing (WDM) technology. This technology divides the huge bandwidth of an optical fiber into different wavelengths, providing different available channels per link of fiber. However, when it is necessary to establish a set of demands, a problem comes up. This problem is known as a routing and wavelength assignment (RWA) problem. Depending on the traffic pattern, two varieties of a RWA problem have been considered in the literature: static and dynamic. In this paper, we present a comparative study among three multiobjective evolutionary algorithms (MOEAs) based on swarm intelligence to solve the RWA problem in real-world optical networks. Artificial bee colony (ABC) algorithm, gravitational search algorithm (GSA), and firefly algorithm (FA) are the selected evolutionary algorithms, but are adapted to multiobjective domain (MO-ABC, MO-GSA, and MO-FA, respectively). In order to prove the goodness of the swarm proposals, we have compared them with a standard MOEA: fast nondominated sorting genetic algorithm. Finally, we present a comparison among the metaheuristics based on swarm intelligence and several techniques published in the literature, coming to the conclusion that swarm intelligence is very suitable to solve the RWA problem, and presumably that it may obtain such quality results not only in diverse telecommunication optimization problems, but also in other engineering optimization problems.