Does it make sense to use bayesian optimization for tuning of hyperparameters of decision tree model? I have not found any article or anything related to this, as BO is usually used for black-box algorithms.
Decision tree with thousands of variables and thousands of nodes would not really be interpretable. Very simple neural network will. There's no clear cut between black-box or not. Moreover it is an optimization algorithm, not an algorithm that works only with black-box functions.
It is perfectly fine to do so.
One might argue that optimising certain structural hyperparameters like the cost function (e.g. Gini index vs Entropy) is an extreme case of cherry-picking - we pick a model that works for a very specific snapshot of the data with total disregard to the problem mechanics. That assertion is probably correct but that said, using BO to optimise "standard" hyperparamters like the minimum number of items on a leaf or the maximum number of leaves, is totally reasonable.