Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation

Ore grade estimation is one of the key stages and the most complicated aspects in mining. Its complexity originates from scientific uncertainty. In this paper, a novel hybrid SLPSO–SVR model that hybridized the self-adaptive learning based particle swarm optimization (SLPSO) and support vector regression (SVR) is proposed for ore grade estimation. This hybrid SLPSO–SVR model searches for SVR's optimal parameters using self-adaptive learning based particle swarm optimization algorithms, and then adopts the optimal parameters to construct the SVR models. The SVR uses the ‘Max-Margin’ idea to search for an optimum hyperplane, and adopts the ε-insensitive loss function for minimizing the training error between the training data and identified function. The hybrid SLPSO–SVR grade estimation method has been tested on a number of real ore deposits. The result shows that method has advantages of rapid training, generality and accuracy grade estimation approach. It can provide with a very fast and robust alternative to the existing time-consuming methodologies for ore grade estimation.