Towards an Efficient Global Multidisciplinary Design Optimization algorithm

This article proposes a new surrogate based Multidisciplinary Design Optimization algorithm. The main idea is to replace each disciplinary solver involved in a non linear Multidisciplinary Analysis by Gaussian Process surrogate models. Although very natural, this approach creates difficulties as the non linearity of the Multidisciplinary Analysis leads to a non Gaussian model of the objective function. However, in order to follow the path of classical Bayesian optimization such as the Efficient Global Optimization algorithm, a dedicated model of the non Gaussian random objective function is proposed. Then, an expected improvement criterion is proposed to enrich the disciplinary Gaussian processes in an iterative procedure, that we call Efficient Global Multidisciplinary Design Optimization (EGMDO). Such an adaptive approach allows to focus the computational budget on areas of the design space relevant only with respect to the optimization problem. The obtained reduction of the number of solvers evaluations is illustrated on a classical MDO test case and on an engineering test case.