Using regression trees for improving the efficiency of nonlinear programming solvers
Engineering Science and Technology, an International Journal, cilt.64, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 64
- Basım Tarihi: 2025
- Doi Numarası: 10.1016/j.jestch.2025.102012
- Dergi Adı: Engineering Science and Technology, an International Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC, Directory of Open Access Journals
- Anahtar Kelimeler: Optimization, Regression tree, Nonlinear programming, Derivative-free method
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Optimization plays a critical role in fields such as economics, engineering, and computational sciences, where finding the optimal values of decision variables is essential for the design of a product, production system, or service system. However, many optimization problems remain challenging even with advanced solvers. This study integrates machine learning into optimization by employing a regression tree algorithm that is trained on sampled solutions of the problem to improve the efficiency of derivative-free nonlinear programming solvers. The approach is tested on 24 single-objective functions to reduce the domain of decision variables. The results demonstrate better accuracy and consistency in the solver performance. Incorporating a machine learning technique into an optimization method can be extended to solve black-box optimization problems and paves the way for innovative solutions in engineering design and other domains.