Simulation-based engineering design: solving parameter inference and multi-objective optimization problems on a shared simulation budget

In recent years, the use of virtual engineering design processes has become more prevalent within industry. This increase has been facilitated by the availability of cost-effective computational machinery on which to run complex simulations of alternative candidate designs. Nevertheless it is frequently the case that, when working with complex problems, the number of simulation-based design evaluations available is limited. Within both industry and academia, it is usual for the stages of simulation model calibration and model-based optimization to be considered as separate consecutive steps rather than as a combined process. However, there is no guarantee that this approach makes the most efficient use of the available function evaluations. This work presents a new alternating methodology that aims to make more efficient use of the evaluation budget, through switching back and forth between the stages of calibration and optimization. To assess the effectiveness of the method, a new benchmark problem is introduced that contains both model parameters to be estimated and design variables to be selected. The new alternating method is found to possess improved calibration and comparable optimization performance in comparison to the sequential method on a budget of 5000 evaluations.