RESEARCH PAPER
Reliability optimization design method based on multi-level surrogate model
| 1 | School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Liaoning, 116028, P.R. China |
| 2 | CRRC Changchun Railway Vehicle Co., Ltd, Jilin, 130062, P. R. China |
| 3 | School of Traffic and Transportation Engineering, Dalian Jiaotong University, Liaoning, 116028, P.R. China |
Publication date: 2020-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(4):638–650
HIGHLIGHTS
- A multi-point addition criterion of genetic-algorithm-based is introduced to the Kriging model.
- A multi-level surrogate model is first proposed considering the poor local search performance of Kriging model.
- A design optimization approach combined multilevel surrogate model with reliability is studied.
KEYWORDS
Kriging modelreliability-based optimizationmulti-level surrogate modeladaptive dynamic penalty function
ABSTRACT
In this work, a genetic-algorithm-based Kriging model with multi-point addition sequence
optimization strategy is addressed to make up for the shortcomings of Kriging model with
single point criterion. This approach combines the multi-point addition strategy with genetic
algorithm to enable the Kriging model to efficiently capture the globally optimal solution.
Based on this, a multi-level surrogate method is presented by employing a local surrogate
model to modify the Kriging global surrogate model, and then applied to design optimization to improve the accuracy and efficiency of global optimization. Meanwhile, a reliability
design optimization method based on multi-level surrogate model is studied by dealing with
the reliability constraints with an adaptive reliability penalty function. Numerical examples
show that the proposed method can find the optimal solution of the object problem with the
least calculation cost under the condition of satisfying the reliability constraint.
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