High-Efficient Sequential Approximate Strategy for Reliability-Based Robust Design Optimization
X. M. Lai, Q. F. Lai, H. Huang, C. Wang, Y. Zhang, L. Yan, J. H. Yang, & S. R. Liao
†College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, 361021, China, ‡Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha,
410083, China, §College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China, ††State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an 710049, China
ABSTRACT: To solve reliability-based robust design optimization (RBRDO) problem in engineering practice, it requires repeated performance of actual complex computation-intensive model simulation within the nesting optimization. In order to improve the computation efficiency, this paper presents an improved formulation of the RBRDO, based on which the RBRDO problem is transformed into approximate sequential sub-optimization problems (ASSOP).In the outer optimization, a fast method to calculate the performance-measure-approach (PMA) functions in ASSOP is introduced, which only needs adaptive limited computation number of the actual simulation models. In the inner optimization, the PMAs in each sub-optimization problem are further expressed as the explicit functions of design vectors, which make each sub-optimization problem deterministic and accelerate the computation speed without performing the actual complex computation-intensive model simulation. Numerical examples shows that the above method is quite efficient since it can effectively reduce the computation number of the actual simulation model and the method itself is applicable to integrate into the existing commercial software such as finite element software. Hence, the method presented in this paper is of high application value to solve complex RBRDO problems in engineering practice
Keywords : Reliability-Based robust design optimization; Performance measure approach; Trust region; Kriging model.
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