As threats of landslide hazards have become gradually more severe in recent decades, studies on landslide pre-vention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility, which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study, the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area, 152 landslides were identified and randomly divided into two groups as training(70％)and validation(30％)dataset.Additionally, a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally, the compre-hensive performance of the two models was validated and compared using various indexes, such as the root mean square error(RMSE), processing time, convergence, and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS, ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808, 0.785 and 0.755, respectively.In terms of the validation dataset, the ANFIS-SBO model exhibited a higher AUROC value of 0.781, while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681, respectively.Moreover, the ANFIS-SBO model showed lower RMSE values for the validation dataset, indicating that the SBO algorithm had a better optimization capability.Meanwhile, the pro-cessing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore, both the ensemble models proposed in this paper can generate adequate results, and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.