Channel power optimization in WDM systems using co-evolutionary genetic algorithm

In this work, we present a co-evolutionary genetic (CEGA) algorithm to adapt the optical launch powers and optimize the signal-to-noise ratio (SNR) values based on maximizing the minimum SNR margin. The introduced co-evolutionary algorithm provides lower computational complexity rather than convex optimization and linear programming techniques, applicable for both static and time-critical dynamic networking. The enhanced Gaussian noise nonlinear model is exploited to take the physical-layer impairments into account, considering networks with partial spectrum utilization. To optimize the minimum SNR margin, we formulate the power allocation problem as a minimax optimization problem. To this end, a two-space genetic algorithm (GA) is proposed to reduce the computational complexity. The obtained results demonstrate that the introduced co-evolutionary algorithm outperforms the common optimization methods in terms of run time. It is shown that the computational complexity of proposed co-evolutionary algorithm is significantly lower than convex and single-space evolutionary approaches by several orders of magnitude. Moreover, the minimum SNR margin is improved by about 2.4 dB compared to a flat launch power optimization.