Enhanced dung beetle optimizer for Kriging-assisted time-varying reliability analysis

Yunhan Ling; Yiqing Shi; Huimin Hou; Lidong Pan; Hao Chen; Peixin Liang; Shiyuan Yang; Peng Nie; Jiahao Han; Debiao Meng; Yunhan Ling; Yiqing Shi; Huimin Hou; Lidong Pan; Hao Chen; Peixin Liang; Shiyuan Yang; Peng Nie; Jiahao Han; Debiao Meng

doi:10.3934/math.20241420

AIMS Mathematics

2024, Volume 9, Issue 10: 29296-29332. doi: 10.3934/math.20241420

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Enhanced dung beetle optimizer for Kriging-assisted time-varying reliability analysis

1.
China Academy of Machinery Beijing Research Institute of Mechanical & Electrical Technology Co., Ltd., Beijing 100083, China
2.
INEGI, Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
3.
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
4.
Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan 523808, China

Received: 11 August 2024 Revised: 13 September 2024 Accepted: 19 September 2024 Published: 16 October 2024
MSC : 60K10, 62N05, 90C23

During the engineering structure's operation, the mechanical structure's performance and loading will change with time, so the parameter uncertainty and structural reliability will also have dynamic characteristics. The time-varying reliability analysis method can more accurately evaluate structural reliability by fully using this dynamic uncertainty. However, the time-varying reliability analysis was mainly based on the spanning rate method, which was complex and difficult to obtain the final result. Therefore, this study proposed an enhanced dung beetle optimization (EDBO) assisted time-varying reliability analysis method based on the adaptive Kriging model. With the help of the adaptive Kriging model and the EDBO optimization algorithm, the efficiency of the time-varying reliability analysis method was improved. At the same time, to prevent prematurely falling into the local search trap, the method improved the uniformity of the sample by initializing the sample through improved tent chaotic mapping (ITCM). Next, the Gaussian random walk strategy was used to search the updated position, which further improved the accuracy of the reliability analysis results. Finally, the accuracy and effectiveness of the proposed time-varying reliability analysis method were verified by four mechanical structure model examples. From the calculation results, it can be seen that with the help of the new DBO optimization algorithm, the relative error of the proposed reliability analysis results was about 20%~30% lower than that of the traditional reliability analysis method. What's more, the calculation efficiency was higher than that of other reliability analysis methods.
- time-varying reliability analysis,
- adaptive Kriging,
- Dung Beetles optimization
Citation: Yunhan Ling, Yiqing Shi, Huimin Hou, Lidong Pan, Hao Chen, Peixin Liang, Shiyuan Yang, Peng Nie, Jiahao Han, Debiao Meng. Enhanced dung beetle optimizer for Kriging-assisted time-varying reliability analysis[J]. AIMS Mathematics, 2024, 9(10): 29296-29332. doi: 10.3934/math.20241420

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Abstract

During the engineering structure's operation, the mechanical structure's performance and loading will change with time, so the parameter uncertainty and structural reliability will also have dynamic characteristics. The time-varying reliability analysis method can more accurately evaluate structural reliability by fully using this dynamic uncertainty. However, the time-varying reliability analysis was mainly based on the spanning rate method, which was complex and difficult to obtain the final result. Therefore, this study proposed an enhanced dung beetle optimization (EDBO) assisted time-varying reliability analysis method based on the adaptive Kriging model. With the help of the adaptive Kriging model and the EDBO optimization algorithm, the efficiency of the time-varying reliability analysis method was improved. At the same time, to prevent prematurely falling into the local search trap, the method improved the uniformity of the sample by initializing the sample through improved tent chaotic mapping (ITCM). Next, the Gaussian random walk strategy was used to search the updated position, which further improved the accuracy of the reliability analysis results. Finally, the accuracy and effectiveness of the proposed time-varying reliability analysis method were verified by four mechanical structure model examples. From the calculation results, it can be seen that with the help of the new DBO optimization algorithm, the relative error of the proposed reliability analysis results was about 20%~30% lower than that of the traditional reliability analysis method. What's more, the calculation efficiency was higher than that of other reliability analysis methods.

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