Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Abstract

Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.