  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.
