论文标题
使用基于人工神经网络的替代模型对SLS 2.0的动态孔的多目标优化
Multiobjective optimization of the dynamic aperture for SLS 2.0 using surrogate models based on artificial neural networks
论文作者
论文摘要
现代同步子源存储环，例如瑞士光源升级（SLS 2.0），在其弧段中使用多弯曲的Achromats来实现前所未有的光彩。这种性能是以增加的焦点要求为代价的，这反过来又需要更强大的六重奏和更高阶的多极领域来进行补偿，并导致动态孔径和/或能量接受度大大减少。在本文中，为了增加这两个量，多目标遗传算法（MOGA）与众所周知的跟踪代码Tracy的修改版本结合使用。作为第一种方法，使用了大规模平行的MOGA实现。与手动获得的解决方案相比，这种方法可产生很好的结果。但是，它需要长时间的计算时间。作为第二种方法，在优化中使用了基于人工神经网络的替代模型。这改善了计算时间，但结果质量降低了。作为第三种方法，在优化期间对替代模型进行了重新训练。这样可以确保与第一种方法获得的溶液质量相当，同时还提供了数量级的加速顺序。最后，显示了SLS 2.0的良好候选解决方案并进一步分析。
Modern synchrotron light source storage rings, such as the Swiss Light Source upgrade (SLS 2.0), use multi-bend achromats in their arc segments to achieve unprecedented brilliance. This performance comes at the cost of increased focusing requirements, which in turn require stronger sextupole and higher-order multipole fields for compensation and lead to a considerable decrease in the dynamic aperture and/or energy acceptance. In this paper, to increase these two quantities, a multi-objective genetic algorithm (MOGA) is combined with a modified version of the well-known tracking code tracy. As a first approach, a massively parallel implementation of a MOGA is used. Compared to a manually obtained solution this approach yields very good results. However, it requires a long computation time. As a second approach, a surrogate model based on artificial neural networks is used in the optimization. This improves the computation time, but the results quality deteriorates. As a third approach, the surrogate model is re-trained during the optimization. This ensures a solution quality comparable to the one obtained with the first approach while also providing an order of magnitude speedup. Finally, good candidate solutions for SLS 2.0 are shown and further analyzed.
