  Bayesian optimization methods have been successfully applied to black box
optimization problems that are expensive to evaluate. In this paper, we adapt
the so-called super effcient global optimization algorithm to solve more
accurately mixed constrained problems. The proposed approach handles
constraints by means of upper trust bound, the latter encourages exploration of
the feasible domain by combining the mean prediction and the associated
uncertainty function given by the Gaussian processes. On top of that, a
refinement procedure, based on a learning rate criterion, is introduced to
enhance the exploitation and exploration trade-off. We show the good potential
of the approach on a set of numerical experiments. Finally, we present an
application to conceptual aircraft configuration upon which we show the
superiority of the proposed approach compared to a set of the state-of-the-art
black box optimization solvers. Keywords: Global Optimization, Mixed
Constrained Optimization, Black box optimization, Bayesian Optimization,
Gaussian Process.
