Abstract: Since slope instability may cause huge economic losses and casualties, a rational and effective model for slope stability evaluation is of vital significance to disaster prevention. Considering multiple uncertainties of slope stability evaluation, we propose a slope stability evaluation model coupling fruit fly optimization algorithm (FOA), normal cloud model (CM), and support vector machine (SVM). The normal CM is used to optimize the FOA by describing the randomness and fuzziness in the foraging process of drosophila. The CM-based FOA, namely, CMFOA, is used to optimize the parameters of SVM classification model. The reliability of the proposed CMFOA-based SVM model is verified with a case study and comparisons with GA-SVM, PSO-SVM, and Grid Search-SVM. Results demonstrated that the proposed model is effective and feasible in slope stability evaluation. The accuracy and efficiency of parameter selection for classification using the CMFOA-SVM model are better than those of other models. It also overcomes the shortcoming of local optimal capability in traditional FOA and provides a new reference for other similar classification problems.

Key words: slope stability, cloud model, fruit fly optimization algorithm, support vector machine, evaluation indices