基于野马优化算法的级联改进型拉曼光纤放大器设计

Design of cascaded improved Raman fiber amplifier based on wild horse optimizer algorithm

摘要:

本文利用掺铒碲酸盐光纤设计了一种多泵浦级联改进型拉曼光纤放大器（Raman fiber amplifier，RFA），并使用野马优化算法（wild horse optimizer，WHO）优化了RFA的各级泵浦参数、传输介质长度以及噪声，以获得最大可能输出增益、低增益平坦度。此外，通过使用灰狼优化、差分进化、免疫优化等其它元启发式技术来优化同一RFA模型，分析不同优化策略下RFA的性能。WHO被证明是一种更好的优化技术，其优化后的模型在1530-1630 nm带宽范围内平均输出增益为44.64 dB，增益平坦度为0.71 dB。此外，还分析了算法对噪声增益的影响，结果表明所设计的RFA能有效降低双向瑞利散射噪声和自发辐射噪声。这证明所设计的RFA具有良好的性能，给下一代光通信网络放大器的设计提供了参考。

Abstract:

In this paper, a multi-pump cascaded improved Raman fiber amplifier (RFA) is designed using erbium-doped tellurite fiber, and the wild horse optimizer (WHO) algorithm is employed to optimize the pump parameters, transmission medium length, and noise of the RFA to achieve maximum possible output gain and low gain flatness. Additionally, the performance of RFA is analyzed under different optimization strategies by using other metaheuristic techniques such as grey wolf optimizer, differential evolution, immune algorithm. WHO is proved to be a better optimization technique, and its optimized model achieving an average output gain of 44.64 dB and a gain flatness of 0.71 dB within the 1530-1630 nm bandwidth range. Furthermore, the impact of the algorithm on the noise gain is analyzed, and the results show that the designed RFA effectively reduces double Rayleigh scattering noise and amplified spontaneous emission noise. This proves that the designed RFA has good performance, providing a reference for the design of next-generation optical communication networks amplifiers.