Surrogate-based automated hyperparameter optimization for expensive automotive crashworthiness optimization

Abstract In the automotive industry, solving crashworthiness optimization problems efficiently is crucial to minimize time and cost investment on expensive function evaluations, e.g., using simulation runs. Nonetheless, automotive crashworthiness optimization is time-consuming and challenging even with domain knowledge, due to the fact that crash problems are typically high-dimensional, nonlinear, and discontinuous. In this work, we propose an automated hyperparameter optimization (HPO) approach for expensive black-box optimization (BBO) problems that can assist practitioners to solve automotive crash problems efficiently using optimally configured optimization algorithms. Precisely, the landscape characteristics of BBO problems, e.g., quantified using exploratory landscape analysis (ELA), are analyzed to identify cheap-to-evaluate representative functions that belong to the same optimization problem class. Based on these representative functions, algorithm configurations can be optimally fine-tuned at a relatively low computational cost. Using three optimization algorithms, consisting of modular covariance matrix adaptation evolutionary strategy (CMA-ES), modular differential evolution (DE), and Bayesian optimization (BO), we evaluate the potential of our approach based on the black-box optimization benchmarking (BBOB) suite and an automotive side crash problem. Since the optimal configurations identified using our approach can perform well on most of the BBOB functions, we believe that our approach can generalize well to BBO problems with similar optimization complexity. For the automotive side crash problem, the BO configuration fine-tuned using our approach can outperform the default BO configuration as well as the conventional response surface method (RSM), in terms of the best-found-solution and convergence speed. Furthermore, better solutions can be identified using the proposed approach compared to successive RSM (SRSM), when dealing with complex crash functions and a limited function evaluation budget. With appropriate extensions, we are confident that our approach can be applied to other real-world expensive BBO domains beyond automotive crashworthiness optimization.