A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

3rd International Conference on Energy Equipment Science and Engineering

Tong, Cao^1 ; Gong, Haili^1

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang

110016, China^1

关键词: Adaptive importance sampling;    Adaptive importance sampling method;    Comparison result;    Computational costs;    Hybrid algorithms;    Hybrid reliability;    Number of samples;    Particle swarm optimization algorithm;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/128/1/012094/pdf
DOI  :  10.1088/1755-1315/128/1/012094

来源: IOP

【 摘 要 】

This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.