  The generation of cold atom clouds is a complex process which involves the
optimization of noisy data in high dimensional parameter spaces. Optimization
can be challenging both in and especially outside of the lab due to lack of
time, expertise, or access for lengthy manual optimization. In recent years, it
was demonstrated that machine learning offers a solution since it can optimize
high dimensional problems quickly, without knowledge of the experiment itself.
In this paper we present results showing the benchmarking of nine different
optimization techniques and implementations, alongside their ability to
optimize a Rubidium (Rb) cold atom experiment. The investigations are performed
on a 3D $^{87}$Rb molasses with 10 and 18 adjustable parameters, respectively,
where the atom number obtained by absorption imaging was chosen as the test
problem. We further compare the best performing optimizers under different
effective noise conditions by reducing the Signal-to-Noise ratio of the images
via adapting the atomic vapor pressure in the 2D+ MOT and the detection laser
frequency stability.
