Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design
This work addresses the challenge of computationally expensive multi-objective optimization in engineering domains like aircraft design, representing an incremental improvement over existing methods.
The authors tackled the problem of expensive multi-objective optimization by extending the SEGOMOE method with regularized infill criteria, achieving a 20x reduction in function evaluations compared to NSGA-II in an aircraft design application.
Bayesian optimization is an advanced tool to perform ecient global optimization It consists on enriching iteratively surrogate Kriging models of the objective and the constraints both supposed to be computationally expensive of the targeted optimization problem Nowadays efficient extensions of Bayesian optimization to solve expensive multiobjective problems are of high interest The proposed method in this paper extends the super efficient global optimization with mixture of experts SEGOMOE to solve constrained multiobjective problems To cope with the illposedness of the multiobjective inll criteria different enrichment procedures using regularization techniques are proposed The merit of the proposed approaches are shown on known multiobjective benchmark problems with and without constraints The proposed methods are then used to solve a biobjective application related to conceptual aircraft design with ve unknown design variables and three nonlinear inequality constraints The preliminary results show a reduction of the total cost in terms of function evaluations by a factor of 20 compared to the evolutionary algorithm NSGA-II.