@Article{cmes.2025.061763,
AUTHOR = {Yunhan Ling, Huajun Peng, Yiqing Shi, Chao Xu, Jingzhen Yan, Jingjing Wang, Hui Ma},
TITLE = {SL-COA: Hybrid Efficient and Enhanced Coati Optimization Algorithm for Structural Reliability Analysis},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {143},
YEAR = {2025},
NUMBER = {1},
PAGES = {767--808},
URL = {http://www.techscience.com/CMES/v143n1/60458},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2025.061763}
}