Copertina

VOPy: A Framework for Black-box Vector Optimization

Salvato in:
Pubblicato in:arXiv.org (Dec 9, 2024), p. n/a
Autore principale: Yaşar, Cahit Yıldırım
Altri autori: Efe Mert Karagözlü, İlter Onat Korkmaz, Ararat, Çağın, Tekin, Cem
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Algorithms
Python
Source code
Black boxes
Multiple objective analysis
Optimization
Accesso online:Citation/Abstract
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Abstract:We introduce VOPy, an open-source Python library designed to address black-box vector optimization, where multiple objectives must be optimized simultaneously with respect to a partial order induced by a convex cone. VOPy extends beyond traditional multi-objective optimization (MOO) tools by enabling flexible, cone-based ordering of solutions; with an application scope that includes environments with observation noise, discrete or continuous design spaces, limited budgets, and batch observations. VOPy provides a modular architecture, facilitating the integration of existing methods and the development of novel algorithms. We detail VOPy's architecture, usage, and potential to advance research and application in the field of vector optimization. The source code for VOPy is available at https://github.com/Bilkent-CYBORG/VOPy.
ISSN:2331-8422
Fonte:Engineering Database