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The subject of the study is an application of evolutionary algorithms to optical node optimization in dense wavelength division multiplexing optical networks. The wider context of the presented research is in essence an improvement of service flexibility and achieving savings in capital expenditures in DWDM networks. Thus, the main objective of the optimization is to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and filters used in new generation of reconfigurable optical add drop multiplexers, etc. For this purpose a model based on integer programming is proposed. The efficiency of the integer programming based software is compared with that of evolutionary algorithms. The results obtained show that there is a large advantage in using evolutionary algorithms for optimizing large optical networks when compared with integer programming and mixed integer programming, whereby the two latter algorithms fail to find the optimal solution within reasonable computational time. The numerical experiments were carried out for realistic networks of different dimensions and traffic demand sets.
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