Bayesian optimization (BO) is a type of black-box method used to optimize a costly objective function for which we have no access to derivatives. In practice, it is frequent that a series of similar problems has to be solved, with the problem data changing moderately between instances. We investigate a transfer learning approach based on BO that reuses information from a previous configuration in order to speed up subsequent optimizations. Our approach involves learning the noise variance to apply to the function values of the previous configuration and adapting the exploration-exploitation trade-off of the acquisition function from the previous configuration. We apply those ideas to the calibration of a beam line in proton therapy where the goal is to find magnet currents to obtain a desired shape for the beam of protons, and for which the calibration has to be repeated for several configurations. We show that reusing information from a previous configuration allows a reduction in the number of iterations by more than 80%, and that using BO is superior to the conventional Nelder-Mead algorithm for black box optimization and transfer learning.