Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting

• Hybrid enhanced artificial bee colony-least squares support vector machines (eABC-LSSVM) for energy fuels price prediction.
• Conventional mutation in ABC for preventing over fitting.
• Levy mutation in ABC to enrich the searching behavior of the bees in search space.
• Hybrid eABC for tuning LSSVM hyper parameters.

The importance of optimizing machine learning control parameters has motivated researchers to investigate for proficient optimization techniques. In this study, a Swarm Intelligence approach, namely artificial bee colony (ABC) is utilized to optimize parameters of least squares support vector machines. Considering critical issues such as enriching the searching strategy and preventing over fitting, two modifications to the original ABC are introduced. By using commodities prices time series as empirical data, the proposed technique is compared against two techniques, including Back Propagation Neural Network and by Genetic Algorithm. Empirical results show the capability of the proposed technique in producing higher prediction accuracy for the prices of interested time series data.