System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobility
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918485483847680 |
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| author | Saves, Paul Bussemaker, Jasper Lafage, Rémi Lefebvre, Thierry Bartoli, Nathalie Diouane, Youssef Morlier, Joseph |
| author_facet | Saves, Paul Bussemaker, Jasper Lafage, Rémi Lefebvre, Thierry Bartoli, Nathalie Diouane, Youssef Morlier, Joseph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobility Saves, Paul Bussemaker, Jasper Lafage, Rémi Lefebvre, Thierry Bartoli, Nathalie Diouane, Youssef Morlier, Joseph Artificial Intelligence Multiagent Systems Systems and Control Optimization and Control 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. |
| title | System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobility |
| topic | Artificial Intelligence Multiagent Systems Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2507.08715 |