Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility

Developing an accurate forecasting model for the volatility of iron ore price plays a vital role in future investments and decisions for mining projects and related companies. Viewed from this perspective, this paper proposes a novel model for accurately forecasting monthly iron ore price volatilities. This model integrates chaotic behavior into a recent meta-heuristic method grasshopper optimization algorithm (GOA) to form a new GOA algorithm called chaotic grasshopper optimization algorithm (CGOA), which is used as a trainer to learn the multilayer perceptron neural network (NN). The results of the proposed model (CGOA-NN) are compared to other models, including the conventional grasshopper optimization algorithm for NN (GOA-NN), Particle swarm optimization for NN (PSO-NN), Genetic Algorithm for NN (GA-NN), and classic NN. Empirical results demonstrate the superiority of the hybrid CGOA-NN model over other models. Moreover, the proposed CGOA-NN model demonstrates an improvement in the forecasting accuracy obtained from classic NN, GA-NN, PSO-NN, and GOA-NN models by 60.82%, 32.18%, 16.49%, and 38.71% decrease in mean square error, respectively.