Ore grade estimation using the imperialist competitive algorithm (ICA)

Abstract

In this paper, a hybrid inverse distance weighted (IDW) and the imperialist competitive algorithm (ICA) was proposed for grade estimations in mining projects. Indeed, the coefficients of the IDW equation, power, and the influence distance were modified by applying the ICA algorithm. For this, an example was solved by using IDW, the ordinary kriging, and IDW-ICA. After successfully validating the IDW-ICA, the iron grades were estimated in the Baba Ali iron mine, the most prominent iron mine in the west of Iran. For this, after gathering data from thirty-two boreholes, the grade estimations were conducted using IDW, kriging, and IDW-ICA. Some statistical parameters, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE), and correlation coefficient, were used to compare the results. The higher value of the R2, 94.41%, the lowest values of MAPE, 17.4%, and RMSE, 4.19, of the proposed algorithm, revealed that the IDW-ICA could be applied as a new-fangled method for grade estimations in the mining projects.