A benchmark of kriging-based infill criteria for noisy optimization

Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive comparison of existing kriging-based methods for the optimization of noisy functions is provided. Ten methods are described using a unified formalism, and compared on analytical benchmark problems with different configurations (noise level, maximum number of observations, initial number of observations). It is found that the optimal method depends on the optimization problem, even though some criteria are consistently more efficient than others.