发表机构
IRIT, UMR 5505 CNRS, Université Toulouse Capitole, Université de Toulouse; ONERA, DTIS, Université de Toulouse; Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse; German Aerospace Center (DLR), Institute of System Architectures in Aeronautics（图卢兹计算机科学研究所，法国国家科学研究中心联合研究单位5505，图卢兹第一大学，图卢兹大学; 法国国家航空航天研究院，信息处理与系统部，图卢兹大学; 法国国立民航大学-法国国立高等航空航天学院-法国国家航空航天研究院联合会，图卢兹大学; 德国航空航天中心，航空系统架构研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对复杂系统之系统优化难题，提出层次贝叶斯优化框架，结合高斯过程处理离散与异构设计变量，在野火抑制无人机多智能体系统中验证了高效性与鲁棒性。
AI 中文摘要
开发创新性系统架构越来越依赖于先进的建模与优化技术，以构建架构设计过程并定义相应的计算问题。对于复杂的系统之系统（SoS），高保真多物理场和多学科仿真对于捕捉详细行为至关重要。然而，其计算成本高昂以及评估失败的风险使得直接优化面临挑战。为克服这些限制，基于代理模型的方法（如贝叶斯优化）已成为管理昂贵黑盒仿真任务的有效工具。本工作引入了一个层次贝叶斯优化框架，利用高斯过程元建模来处理SoS问题中固有的离散架构选择、条件依赖性和异构设计变量。结果表明，与传统基于代理模型的方法相比，层次化公式提高了搜索效率和鲁棒性，使得在有限仿真预算下能够探索大规模且结构多样的设计空间。我们将该方法应用于一个用于野火抑制的基于飞机的多智能体系统，这是欧盟资助的COLOSSUS项目中开发的一个用例，展示了SoS原理如何协调具有互补角色的异构空中平台，同时支持可持续交通和应急响应任务。我们的框架为SoS架构设计和模型探索提供了一种可扩展的方法论，为航空、可持续交通和面向韧性的系统设计应用提供了可迁移的见解。通过将层次化表示与基于代理模型的优化相结合，本工作是最早将层次贝叶斯优化应用于实际SoS问题的实际演示之一，推动了方法论和实践的发展。
英文摘要
Developing innovative system architectures increasingly relies on advanced modeling and optimization techniques to frame the architecting process and define the corresponding computational problems. For complex System-of-Systems (SoS), high-fidelity multiphysics and multidisciplinary simulations are essential for capturing detailed behaviors. However, their computational expense and the risk of evaluation failures make direct optimization challenging. To overcome these limitations, surrogate-based approaches, like Bayesian optimization, have emerged as effective tools for managing expensive, black-box simulation tasks. This work introduces a hierarchical Bayesian optimization framework that leverages Gaussian process meta-modeling to handle discrete architectural choices, conditional dependencies, and heterogeneous design variables inherent to SoS problems. Results show that the hierarchical formulation improves search efficiency and robustness compared to conventional surrogate-based methods, enabling the exploration of large and structurally diverse design spaces with limited simulation budgets. We apply the approach to an aircraft-based multi-agent system for wildfire suppression, a use case developed within the EU-funded COLOSSUS project that illustrates how SoS principles can coordinate heterogeneous aerial platforms with complementary roles, supporting both sustainable mobility and emergency response missions. Our framework provides a scalable methodology for SoS architecting and model exploration, offering transferable insights for applications in aviation, sustainable mobility, and resilience-oriented system design. By combining hierarchical representations with surrogate-based optimization, this work is among the first practical demonstrations of hierarchical Bayesian optimization applied to real-world SoS problems, advancing both methodology and practice.
DOI：10.21203/rs.3.rs-10496293/v1
