Efficient Multi-Information Source Multiobjective Bayesian Optimization
Danial Khatamsaz, Lalith Peddareddygari, Sam Freesun Friedman, Douglas Allaire · AIAA Scitech 2020 Forum · 2020
Multi-objective optimization is often a difficult task owing to the need to balance competing objectives. A typical approach to handling this is to estimate a Pareto frontier in objective space by identifying non-dominated points. This task is typically computationally demanding owing to the need to incorporate information of high enough fidelity to be trusted in design and decision-making processes. In this work, we present a multi-information source framework for enabling efficient multi-objective optimization. The framework allows for the exploitation of all available information and considers both potential improvement and cost. The framework includes ingredients of model fusion, expected hypervolume improvement, and intermediate Gaussian process surrogates. The approach is demonstrated on two test problems and aerostructural wing design problem.