Integration Learning of Neural Network Training with Swarm Intelligence and Meta-heuristic Algorithms for Spot Gold Price Forecast

Abstract

This research attempts to enhance the learning performance of radial basis function neural network (RBFNuNet) via swarm intelligence (SI) and meta-heuristic algorithms (MHAs). Further, the genetic algorithm (GA) and ant colony optimization (ACO) algorithms are applied for RBFNuNet to learn. The proposed integration of GA and ACO approaches-based (IGACO) algorithm combines the complementarity of exploitation and exploration capabilities to achieve optimization resolve. The feature of population diversification has higher opportunity to pursue the global optimal substitute being constrained to local optimal exceeding in five continuous test functions. The experimental results have illustrated that GA and ACO approaches can be incorporated intelligently and propose an integrated algorithm, which intents for obtaining the optimal accuracy training performance among relevant algorithms in this study. Additionally, method assessment results for five benchmark problems and a practical spot gold price forecast exercise show that the proposed IGACO algorithm outperforms other algorithms and the Box-Jenkins models in terms of forecasting preciseness and execution time.