Multiobjective Collaborative Robust Optimization With Interval Uncertainty and Interdisciplinary Uncertainty Propagation
Multiobjective Collaborative Robust Optimization With Interval Uncertainty and Interdisciplinary Uncertainty Propagation
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DOI：
10.1115/1.2936898
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发表时间：
2008-08
影响因子：
3.3
通讯作者：
Mian Li;S. Azarm
中科院分区：
文献类型：
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作者：
Mian Li;S. Azarm
提出了一种新的多学科设计优化（MDO）问题求解方法，该方法首次在文献中具有以下特点：每个学科都有多个目标和约束，并且具有连续-离散混合变量，参数中存在不确定性，因此，不确定性在学科内和学科间存在传播;不确定参数的概率分布是不可用的，但其不确定性的区间是已知的;并且学科可以完全（双向）耦合。建议的多目标协同鲁棒优化（McRO）方法使用多目标遗传算法作为优化器。McRO在多目标和多学科的意义上获得尽可能好的解决方案。此外，对于McRO解决方案，目标和/或约束函数的变化可以保持在可接受的范围内。McRO包括跨学科不确定性传播的技术。该方法可用于多目标优化问题的鲁棒优化，或约束，或两者一起在系统和子系统的水平。从应用的McRO的数值和工程实例的结果。它的结论是，McRO可以解决完全耦合的MDO问题的区间不确定性，并获得解决方案，是可比的单学科的鲁棒优化方法。
We present a new solution approach for multidisciplinary design optimization (MDO) problems that, for the first time in literature, has all of the following characteristics: Each discipline has multiple objectives and constraints with mixed continuous-discrete variables; uncertainty exists in parameters and as a result, uncertainty propagation exists within and across disciplines; probability distributions of uncertain parameters are not available but their interval of uncertainty is known; and disciplines can be fully (two-way) coupled. The proposed multiobjective collaborative robust optimization (McRO) approach uses a multiobjective genetic algorithm as an optimizer. McRO obtains solutions that are as best as possible in a multiobjective and multidisciplinary sense. Moreover, for McRO solutions, the variation of objective and/or constraint functions can be kept within an acceptable range. McRO includes a technique for interdisciplinary uncertainty propagation. The approach can be used for robust optimization of MDO problems with multiple objectives, or constraints, or both together at system and subsystem levels. Results from an application of McRO to a numerical and an engineering example are presented. It is concluded that McRO can solve fully coupled MDO problems with interval uncertainty and obtain solutions that are comparable to a single-disciplinary robust optimization approach.