System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobility
This work addresses computational efficiency problems for researchers and practitioners in intermodal mobility and system-of-systems engineering, but it appears incremental as it applies existing surrogate methods to a specific domain.
The paper tackles the challenge of exploring novel architectures in system-of-systems modeling, where dedicated physics-based simulations increase computational costs and optimization failures, by proposing surrogate-based optimization algorithms like Bayesian optimization to address these issues.
For developing innovative systems architectures, modeling and optimization techniques have been central to frame the architecting process and define the optimization and modeling problems. In this context, for system-of-systems the use of efficient dedicated approaches (often physics-based simulations) is highly recommended to reduce the computational complexity of the targeted applications. However, exploring novel architectures using such dedicated approaches might pose challenges for optimization algorithms, including increased evaluation costs and potential failures. To address these challenges, surrogate-based optimization algorithms, such as Bayesian optimization utilizing Gaussian process models have emerged.