Multiobjective optimization of the dynamic aperture for SLS 2.0 using surrogate models based on artificial neural networks

TL;DR

This paper presents a multi-objective optimization approach for the SLS 2.0 storage ring's dynamic aperture, combining genetic algorithms with surrogate models based on neural networks to balance solution quality and computational efficiency.

Contribution

It introduces a re-trained surrogate model approach that achieves high-quality solutions with significantly reduced computation time for optimizing the dynamic aperture.

Findings

Re-trained surrogate models match the quality of extensive genetic algorithm runs.

The combined approach provides good candidate solutions for SLS 2.0.

Significant speedup in optimization process compared to traditional methods.

Abstract

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.