Optimizing a Machine Learning Algorithm by a Novel Metaheuristic Approach: A Case Study in Forecasting

Bahadır Gülsün and Muhammed Resul Aydin ()
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Bahadır Gülsün: Department of Industrial Engineering, Yildiz Technical University, 34349 Istanbul, Türkiye
Muhammed Resul Aydin: Department of Industrial Engineering, Yildiz Technical University, 34349 Istanbul, Türkiye

Mathematics, 2024, vol. 12, issue 24, 1-19

Abstract: Accurate sales forecasting is essential for optimizing resource allocation, managing inventory, and maximizing profit in competitive markets. Machine learning models are being increasingly used to develop reliable sales-forecasting systems due to their advanced capabilities in handling complex data patterns. This study introduces a novel hybrid approach that combines the artificial bee colony (ABC) and fire hawk optimizer (FHO) algorithms, specifically designed to enhance hyperparameter optimization in machine learning-based forecasting models. By leveraging the strengths of these two metaheuristic algorithms, the hybrid method enhances the predictive accuracy and robustness of models, with a focus on optimizing the hyperparameters of XGBoost for forecasting tasks. Evaluations across three distinct datasets demonstrated that the hybrid model consistently outperformed standalone algorithms, including the genetic algorithm (GA), artificial rabbits optimization (ARO), the white shark optimizer (WSO), the ABC algorithm, and the FHO, with the latter being applied for the first time to hyperparameter optimization. The superior performance of the hybrid model was confirmed through the RMSE, the MAPE, and statistical tests, marking a significant advancement in sales forecasting and providing a reliable, effective solution for refining predictive models to support business decision-making.

Keywords: extreme gradient boosting algorithm; machine learning algorithm; forecasting model; metaheuristic algorithms; hyperparameter tuning; hybrid metaheuristic (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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