GOAT method: Green Orthogonal Array Tuning method
Nevena Ranković, Dragica Ranković · Alexandria Engineering Journal · 2025
This paper is a natural extension of our previous work and introduces an eco-efficient and integrated approach to hyper-parameter optimization (HPO) using Taguchi’s orthogonal array tuning method (OATM), which forms the basis for our GOAT ( G reen O rthogonal A rray T uning) method across leading models in Machine Learning (ML), Deep Learning (DL), and Graph Neural Networks (GNNs): XGBoost, LightGBM, CatBoost, LSTM, GRU, GGNN, and GGSNN. Taguchi’s method requires fewer than 10 experiments and just 11 s of running time for all models, demonstrating its efficiency. GGSNN emerges as the best-performing model overall. A comprehensive case study on software estimation, using 46 publicly available datasets, highlights the method’s ability to reduce time and energy consumption while improving accuracy, promoting sustainable practices and high-impact real-world applications.