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Modern synchrotron light source storage rings, such as the Swiss Light Sourceupgrade (SLS 2.0), use multi-bend achromats in their arc segments to achieveunprecedented brilliance. This performance comes at the cost of increasedfocusing requirements, which in turn require stronger sextupole andhigher-order multipole fields for compensation and lead to a considerabledecrease in the dynamic aperture and/or energy acceptance. In this paper, toincrease these two quantities, a multi-objective genetic algorithm (MOGA) iscombined with a modified version of the well-known tracking code tracy. As afirst approach, a massively parallel implementation of a MOGA is used. Comparedto a manually obtained solution this approach yields very good results.However, it requires a long computation time. As a second approach, a surrogatemodel based on artificial neural networks is used in the optimization. Thisimproves the computation time, but the results quality deteriorates. As a thirdapproach, the surrogate model is re-trained during the optimization. Thisensures a solution quality comparable to the one obtained with the firstapproach while also providing an order of magnitude speedup. Finally, goodcandidate solutions for SLS 2.0 are shown and further analyzed.

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