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This is a Quantitative Study study.

October 1, 2025Applied SciencesOpen Access

A Multidisciplinary Robust Collaborative Optimization Method Under Parameter Uncertainty Based on the Double-Layer EI–Kriging Model

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Authors

ZQZeyang QiuCentral South University ZWZhe WeiShenyang Institute of Automation MCMo ChenBulacan State University

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Implication

A new multidisciplinary robust collaborative optimization method improves convergence and robustness, addressing parameter uncertainty in high-cost MDO scenarios.

Key Points

The MRCO-PU method improved robust feasibility from 0.49 to 1.00 in a mathematical case, enhancing performance under uncertainty.
Specific energy consumption reduced by 6.3% in the engineering case while maintaining robust feasibility, showcasing operational efficiency.
The approach combines collaborative optimization with a double-layer EI–Kriging model for effective robustness in multi-constraint conditions.
This work highlights a significant advancement in multidisciplinary robust optimization for high-end equipment under uncertain conditions.

Cite This Study

Qiu et al. (2025) studied this question.

synapsesocial.com/papers/68dd89e6fe798ba2fc49824f https://doi.org/10.3390/app151910549

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Implication

A new multidisciplinary robust collaborative optimization method improves convergence and robustness, addressing parameter uncertainty in high-cost MDO scenarios.

A Multidisciplinary Robust Collaborative Optimization Method Under Parameter Uncertainty Based on the Double-Layer EI–Kriging Model

Discussion

Member takes

Implication

Key Points

Cite This Study

A Multidisciplinary Robust Collaborative Optimization Method Under Parameter Uncertainty Based on the Double-Layer EI–Kriging Model

Discussion

Member takes

Implication

Key Points

Cite This Study

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