Addressing mixed constraints: an improved framework for black-box optimization

Raju Chowdhury () and Neelesh S. Upadhye ()
Additional contact information
Raju Chowdhury: Indian Institute of Technology Madras
Neelesh S. Upadhye: Indian Institute of Technology Madras

Journal of Global Optimization, 2025, vol. 92, issue 4, No 2, 889-908

Abstract: Abstract This article proposes a novel hybrid Bayesian optimization framework designed to solve problems with equality, inequality constraints, and their combinations. We develop a new variant of the expected improvement acquisition function that effectively addresses constraints during iteration. This function balances exploration and exploitation within the constrained search space. Our framework adeptly manages inequality and equality constraints and can initiate the iterative process even from infeasible starting points. Finally, we evaluate our proposed framework against existing approaches using synthetic test problems and a real-world engineering design and hydrology problem. By integrating Bayesian optimization techniques with advanced constraint handling, our framework provides a promising avenue for addressing mixed constraints in black-box optimization scenarios.

Keywords: Gaussian process; Black-box function; Expected Improvement; Primary 90C56; Secondary 62F15; 62C10 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10898-025-01486-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jglopt:v:92:y:2025:i:4:d:10.1007_s10898-025-01486-5

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/10898

DOI: 10.1007/s10898-025-01486-5

Access Statistics for this article

Journal of Global Optimization is currently edited by Sergiy Butenko

More articles in Journal of Global Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().