Bayesian Algorithm for Collaborative Optimization with Application to Aircraft Design
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910195508051968 |
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| author | Belhafnaoui, Mohamed Ali Diouane, Youssef |
| author_facet | Belhafnaoui, Mohamed Ali Diouane, Youssef |
| contents | Collaborative Optimization (CO) is a multidisciplinary design optimization (MDO) framework that decomposes large-scale engineering problems into parallel, independently solvable subsystems coordinated by a system-level optimizer. Its practical utility is limited by the high frequency of expensive black-box disciplinary evaluations arising from the bi-level consistency constraints. This paper introduces BACO, a Bayesian Algorithm for Collaborative Optimization, which replaces the direct black-box calls at both levels with Gaussian process (GP) surrogates and acquisition function maximization. At the subsystem level, an acquisition function subject to GP-predicted feasibility constraints identifies the next evaluation point. At the system level, the same surrogate framework enforces consistency through predicted discrepancy constraints. This architecture reduces the number of true black-box evaluations required per major iteration. BACO is benchmarked against state-of-the-art CO variants on a Scalable MDO problem over 50 randomized instances. On this problem, BACO consistently achieves lower objective values and drives both constraint violation and interdisciplinary discrepancy to near-zero within the evaluation budget, outperforming all three CO variants across all tested DoE sizes. Further validation is conducted on a coupled aero-structural wing optimization problem based on the Common Research Model (CRM) geometry, where BACO identifies a feasible solution within 886 of 1000 allocated evaluations, recovering results physically consistent with active bending stress and tip deflection constraints. The BACO software, the state-of-the-art CO solvers, as well as standard MDO benchmarking problems are open-source and publicly available at https://moebehfn.github.io/mdotoolbox/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_05474 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bayesian Algorithm for Collaborative Optimization with Application to Aircraft Design Belhafnaoui, Mohamed Ali Diouane, Youssef Optimization and Control Collaborative Optimization (CO) is a multidisciplinary design optimization (MDO) framework that decomposes large-scale engineering problems into parallel, independently solvable subsystems coordinated by a system-level optimizer. Its practical utility is limited by the high frequency of expensive black-box disciplinary evaluations arising from the bi-level consistency constraints. This paper introduces BACO, a Bayesian Algorithm for Collaborative Optimization, which replaces the direct black-box calls at both levels with Gaussian process (GP) surrogates and acquisition function maximization. At the subsystem level, an acquisition function subject to GP-predicted feasibility constraints identifies the next evaluation point. At the system level, the same surrogate framework enforces consistency through predicted discrepancy constraints. This architecture reduces the number of true black-box evaluations required per major iteration. BACO is benchmarked against state-of-the-art CO variants on a Scalable MDO problem over 50 randomized instances. On this problem, BACO consistently achieves lower objective values and drives both constraint violation and interdisciplinary discrepancy to near-zero within the evaluation budget, outperforming all three CO variants across all tested DoE sizes. Further validation is conducted on a coupled aero-structural wing optimization problem based on the Common Research Model (CRM) geometry, where BACO identifies a feasible solution within 886 of 1000 allocated evaluations, recovering results physically consistent with active bending stress and tip deflection constraints. The BACO software, the state-of-the-art CO solvers, as well as standard MDO benchmarking problems are open-source and publicly available at https://moebehfn.github.io/mdotoolbox/. |
| title | Bayesian Algorithm for Collaborative Optimization with Application to Aircraft Design |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2605.05474 |