A NOVEL INTELLIGENT APPROACH FOR PREDICTING WEAR OF EXCAVATOR BUCKET TEETH BASED ON HYBRID ICA-XGBOOST MODEL

This study introduces, for the first time, a hybrid intelligent approach that leverages four metaheuristic optimisation algorithms: Imperialistic Competitive Algorithm (ICA), Grasshopper Optimisation Algorithm (GOA), Antlion Optimiser (ALO), and Artificial Fish Swarm Algorithm (AFSA), to optimise the parametric weights of the Extreme Gradient Boosting (XGBoost) technique, enhancing its ability to predict excavator bucket tooth wear. The hybrid models, named ICA-XGBoost, GOA-XGBoost, ALO-XGBoost, and AFSA-XGBoost, were developed using a data set of 579 wear records from a surface mine in Ghana. Furthermore, the study implemented standalone models, such as XGBoost, Gradient Boosting Regressor (GBR), Random Forest (RF), and Categorical Boosting Regressor (CatBoost), to benchmark the performance of the various hybrid XGBoost models.