SL-COA: Hybrid Efficient and Enhanced Coati Optimization Algorithm for Structural Reliability Analysis

Yunhan Ling¹, Huajun Peng², Yiqing Shi^1,*, Chao Xu¹, Jingzhen Yan¹, Jingjing Wang¹, Hui Ma^3,*
1 China Academy of Machinery, Beijing Research Institute of Mechanical & Electrical Technology Co., Ltd., Beijing, 100083, China
2 AVIC Guizhou Anda Aviation Forging Co., Ltd., Anshun, 561000, China
3 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
* Corresponding Author: Yiqing Shi. Email: email ; Hui Ma. Email: email
(This article belongs to the Special Issue: Machine Learning-Assisted Structural Integrity Assessment and Design Optimization under Uncertainty)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.061763

Received 02 December 2024; Accepted 07 February 2025; Published online 31 March 2025

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Abstract

The traditional first-order reliability method (FORM) often encounters challenges with non-convergence of results or excessive calculation when analyzing complex engineering problems. To improve the global convergence speed of structural reliability analysis, an improved coati optimization algorithm (COA) is proposed in this paper. In this study, the social learning strategy is used to improve the coati optimization algorithm (SL-COA), which improves the convergence speed and robustness of the new heuristic optimization algorithm. Then, the SL-COA is compared with the latest heuristic optimization algorithms such as the original COA, whale optimization algorithm (WOA), and osprey optimization algorithm (OOA) in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples. The optimization results show that the proposed SL-COA algorithm has a high competitiveness. Secondly, this study introduces the SL-COA algorithm into the MPP (Most Probable Point) search process based on FORM and constructs a new reliability analysis method. Finally, the proposed reliability analysis method is verified by four mathematical examples and two engineering examples. The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.