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A multidisciplinary collaborative robust optimization method based on weighted allocation of discrepancy information
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AI
DOI：10.1080/0305215x.2026.2666309.png)
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Abstract
En 中文
To address difficulties in relaxation factor selection, local optimality and uncertainty sensitivity in multidisciplinary collaborative optimization (CO), this article proposes a multidisciplinary collaborative robust optimiza-tion method based on weighted allocation of discrepancy information, called stability collaborative optimization-maximum variation analysis (SCO-MVA). The method adaptively adjusts relaxation factors by weighting interdisciplinary and system-discipline discrepancies, and achieves stable coordination through a two-level optimization strategy. Comparative stud-ies with static and dynamic relaxation CO methods show that the proposed approach significantly improves convergence efficiency while maintain-ing optimization accuracy, reducing the average number of iterations by approximately 60-85%. Furthermore, maximum variation analysis is incor-porated to model interval uncertainty and construct a robust optimization framework. Validation using multiple examples demonstrates that robust solutions maintain full constraint feasibility under worst-case uncertainty, with only a 5-15% increase in objective function value, indicating a reason-able trade-off between robustness and optimality.
Keywords:
Collaborative optimization
stability collaborative optimization
maximum variation analysis
robust design
Journal
IF:
2.2
Papers:
105
Citations:
3.8K