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...