Landscape Analysis Based vs. Domain-Specific Optimization for Engineering Design Applications: A Clear Case

Traditional methods in black-box optimization often prescribe general-purpose algorithms for broad problem categories, which overlooks the significant variability in optimization landscapes that can exist even within a single domain. We address this gap by demonstrating the efficacy of landscape analysis techniques in guiding the choice and tuning of optimization algorithms for distinct landscape features. This study focuses on two real-world problem domains characterized by black-box simulation-based optimization: automotive control system calibration (anti-lock braking system) and automotive crashworthiness optimization. Through comparative analysis of computer experiments in these domains, landscape analysis techniques are employed for both visualization and explanation purposes. The results reveal substantial variations in landscape features across different instances within the same problem domain. Consequently, we advocate for a paradigm shift towards learning and applying optimizers tailored to specific landscape feature spaces rather than applying a one-size-fits-all approach to an entire problem domain. This research not only enhances the understanding of landscape variability in optimization problems but also paves the way for more efficient and effective optimization processes in complex, real-world scenarios.