## 1 Introduction  
Soil shear strength is defined as the resistance of soil against shear stresses [1; 2], which cause deformations or displacements in the soil mass [3]. Therefore, it is considered as one of the most crucial designing properties in geotechnical engineering. In the building sector, for example, having a reliable approximation of the shear strength helps foundation engineers to choose the type of foundation, as well as the necessity of subsoil improvement. It is also a very determinant factor for exploring soil problems like slope stability [4; 5] and bearing capacity [6; 7] in different conditions. Figure 1 illustrates an example of shear failures for strip footing subsoil and rainfall-induced landslide. Like many other engineering problems, before the advent of soft computing approaches, the shear strength was investigated and predicted using statistical [8] and numerical methods [9; 10; 11; 12]. [PERSON] et al. [13] investigated uncertainty reduction in examining the undrained shear strength in clays using multivariate approaches. [PERSON] and [PERSON] [14] provided a summary of 19 shear strength evaluative procedures consisting of six based on soil-water retention curve (SWRC), and 13 based on mathematical relationships. They also stated that the studied literature review highlighted the suitability of SWRC-based procedures for successful simulation of the shear strength. The shear strength can be affected by various soil factors like moisture content, plastic index, clay content, etc. [1]. Thus, implementing direct evaluative methods for exploring the relationship between the shear strength and its related parameters is relatively costly and complicated [15; 16], and also there might lead to different results because of different stress states, stress histories, degrees of sampling disturbance, and strain rates [17]. Due to these difficulties, indirect methods are very popular in the field of geotechnical parameter evaluation [18; 19; 20]. In recent years, soft computing techniques gained increasing attention for simulating various geotechnical phenomena like slope stability [21; 22; 23; 24]. As for the shear strength, many scholars showed the applicability of intelligent models. [PERSON] et al. [25] showed the efficiency (correlation coefficient (\(R\)) = 0.89 and mean absolute percentage error (MAPE) = 3.27%) of machine learning for computing peak shear strength of discrete fiber-reinforced soils. [PERSON] et al. [26] used a multiple linear regression (MLR) model to predict the shear strength in the central part of Iran. [PERSON] et al. [27] successfully employed an artificial neural network (ANN) for forecasting the unsaturated shear strength. Likewise, [PERSON] et al. [28] used this tool for investigating the changes in undrained shear strength with respect to principal stress rotation. [PERSON] et al. [29] explored the prediction capability of an optimized support vector machine (SVM) for predicting the shear strength in Bazoft watershed, central Iran. The results were also compared with the multilayer perceptron (MLP) model. Regarding the \(R\) values of 0.98 and 0.52, along with mean absolute error (MAE) values of 0.17 and 0.81, the superiority of the SVM was derived. Moreover, the superiority of the ANN (to statistical models) was shown by [PERSON] et al. [30] in analyzing the relationship between shear strength parameters and index properties of normally consolidated plastic clay. The recent advances in metaheuristic science led to the proper optimization of different engineering parameters. Moreover, it was proven that synthesizing these algorithms with typical predictive models led to raising their accuracy through prevailing computational weaknesses [31; 32; 33].