Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model

This paper proposes a new soft computing model (artificial intelligence model) for modeling rock fragmentation (i.e., the size distribution of rock (SDR)) with high accuracy, based on a boosted generalized additive model (BGAM) and a firefly algorithm (FFA), called FFA-BGAM. Accordingly, the FFA was used as a robust optimization algorithm/meta-heuristic algorithm to optimize the BGAM model. A split-desktop environment was used to analyze and calculate the size of rock from 136 images, which were captured from 136 blasts. To this end, blast designs were collected and extracted as the input parameters. Subsequently, the proposed FFA-BGAM model was evaluated and compared through previous well-developed soft computing models, such as FFA-ANN (artificial neural network), FFA-ANFIS (adaptive neuro-fuzzy inference system), support vector machine (SVM), Gaussian process regression (GPR), and k-nearest neighbors (KNN) based on three performance indicators (MAE, RMSE, andR(2)). The results indicated that the new intelligent technique (i.e., FFA-BGAM) provided the highest accuracy in predicting the SDR with an MAE of 0.920, RMSE of 1.213, andR(2)of 0.980. In contrast, the remaining models (i.e., FFA-ANN, FFA-ANFIS, SVM, GPR, and KNN) yielded lower accuracies in predicting the SDR, i.e., MAEs of 1.248, 1.661, 1.096, 1.573, 1.237; RMSEs of 1.598, 2.068, 1.402, 2.137, 1.717; andR(2)of 0.967, 0.968, 0.972, 0.940, 0.963, respectively.

Automated summary

This paper introduces a new soft computing model FFA-BGAM for accurately modeling rock fragmentation using a firefly algorithm to optimize the BGAM model. Compared to other soft computing models, FFA-BGAM shows higher accuracy in predicting rock size distribution.