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First-order reliability method based on Harris Hawks Optimization for high-dimensional reliability analysis

delete2020-05-13
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PRE
AI
C
Changting Zhong
M
Mengfu Wang *
C
Chao Dang
可文海 封面图
可文海 (Wenhai Ke)
S
Shengqi Guo
DOI:10.1007/s00158-020-02587-3delete
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摘要

摘要

En 中文
The first-order reliability method (FORM) is a prevalent method in the structural reliability community. However, when solving the high-dimensional problem with a highly nonlinear limit state function, FORM usually encounters non-convergence or divergence. In this study, an improved FORM combining Harris Hawks Optimization (HHO-FORM) is presented for high-dimensional reliability analysis. HHO is a meta-heuristic algorithm mimicking the predatory behavior of Harris hawks, and efficient in finding the global optimum of high-dimensional problems. In HHO-FORM, the reliability index is firstly formulated as the solution of a constrained optimization problem according to the FORM theory. Then, the constraints are handled with the exterior penalty function method. In addition, the optimal reliability index is determined by the Harris Hawks Optimization that accelerates the convergence by the population-based mechanism and the strategy of Levy Flight. The HHO-FORM does not require the derivatives of the limit state functions that reduce the computational burden for high-dimensional problems. So the simplicity of HHO-FORM greatly improves the efficiency in solving high-dimensional reliability problems. The HHO-FORM is firstly tested on three challenging numerical high-dimensional problems and then applied to two high-dimensional engineering problems to verify its performance. Four gradient-based FORM algorithms and four heuristic-based FORM algorithms are also compared with the proposed method. The experimental results demonstrate that HHO-FORM provides good accuracy and efficiency for high-dimensional reliability problems.
Keyword:
First-order reliability method
Harris Hawks Optimization
High-dimensional reliability analysis
Meta-heuristics
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期刊

Structural and Multidisciplinary Optimization 封面图
Structural and Multidisciplinary Optimization
IF:
4
论文数:
4.8K
被引数:
1.7W

机构

H
hunan university
学者数:
4.5W
论文数: 3.3W
被引数: 70
E
East China Jiaotong University
学者数:
4.1K
论文数: 2.9K
被引数: 2.9K