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Evolutionary algorithms (EAs) are mainly considered for modelling and solving practical complex and NP-hard problems in large-scale search spaces. The aim of this paper is to apply some well-known intelligent optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for solving routing and wavelength assignment (RWA) problem in optical networks which is also known to be an NP-hard problem. The performance of proposed optimization algorithms is compared for convergence speed and solution accuracy. The NSFNET network is considered as test-bench topology and randomly generated connection requests are introduced into network demand matrix. Simulation results demonstrate that the convergence speed of ABC algorithm is much better than other two algorithms to reach near-optimum solution in acceptable processing time. Furthermore, the PSO algorithm has better performance than GA in term of convergence speed.