Using regression trees for improving the efficiency of nonlinear programming solvers

Terzi F., ARAS M. N., BAYDOĞAN M. G.

Engineering Science and Technology, an International Journal, cilt.64, 2025 (SCI-Expanded, Scopus) identifier identifier

  • 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.