Hary Mathilde, Koivisto Teemu, Lukasik Sara, Dudley John M, Genty Goëry
Photonics Laboratory, Tampere University, 33104, Tampere, Finland.
Institut FEMTO-ST, Université Bourgogne Franche-Comté CNRS UMR 6174, 25000, Besançon, France.
Sci Rep. 2025 Jan 2;15(1):377. doi: 10.1038/s41598-024-84567-x.
Supercontinuum generation in optical fiber involves complex nonlinear dynamics, making optimization challenging, and typically relying on trial-and-error or extensive numerical simulations. Machine learning and metaheuristic algorithms offer more efficient optimization approaches. We report here an experimental study of supercontinuum spectral shaping by tuning the phase of the input pulses, different optimization approaches including a genetic algorithm, particle swarm optimizer, and simulated annealing. We find that the genetic algorithm and particle swarm optimizer are more robust and perform better, with the particle swarm optimizer converging faster. Our study provides valuable insights for the systematic optimization of supercontinuum and other optical sources.
光纤中的超连续谱产生涉及复杂的非线性动力学，这使得优化具有挑战性，并且通常依赖于试错法或大量的数值模拟。机器学习和元启发式算法提供了更有效的优化方法。我们在此报告一项通过调整输入脉冲的相位对超连续谱进行光谱整形的实验研究，包括遗传算法、粒子群优化器和模拟退火等不同的优化方法。我们发现遗传算法和粒子群优化器更稳健且性能更好，其中粒子群优化器收敛更快。我们的研究为超连续谱及其他光源的系统优化提供了有价值的见解。
