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.

在这项工作中，我们提出了一种协同进化遗传 (CEGA) 算法来适应光发射功率并基于最大化最小 SNR 裕度来优化信噪比 (SNR) 值。引入的协同进化算法提供了更低的计算复杂度，而不是凸优化和线性规划技术，适用于静态和时间关键的动态网络。考虑到部分频谱利用的网络，利用增强型高斯噪声非线性模型来考虑物理层损伤。为了优化最小 SNR 余量，我们将功率分配问题表述为一个极小极大优化问题。为此，提出了一种双空间遗传算法（GA）来降低计算复杂度。获得的结果表明，引入的协同进化算法在运行时间方面优于常见的优化方法。结果表明，所提出的协同进化算法的计算复杂度明显低于凸和单空间进化方法几个数量级。此外，与平坦的发射功率优化相比，最小 SNR 余量提高了约 2.4 dB。