Abstract
The Sequential Optimization and Reliability Assessment (SORA) method is a single-loop method containing a serial of cycles of decoupled deterministic optimization and reliability assessment for improving the efficiency of probabilistic optimization. However, the original SORA method as well as some other existing single-loop methods do not take into account the effect of varying design variance (changing variance) in design problems. In this paper, to enhance the SORA method, three formulations are proposed in order to improve the efficiency for solving problems with changing variance. These formulations are categorized by the different strategies of Inverse Most Probable Point (IMPP) approximation. Mathematical examples and a speed reducer design problem are utilized to test and compare the effectiveness of the proposed formulations. The insight gained from our study on the applicability of different approaches can be extended and utilized in other probabilistic optimization strategies that require IMPP estimations.
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