VOPy: A Framework for Black-box Vector Optimization

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Podrobná bibliografie
Vydáno v:	arXiv.org (Dec 9, 2024), p. n/a
Hlavní autor:	Yaşar, Cahit Yıldırım
Další autoři:	Efe Mert Karagözlü, İlter Onat Korkmaz, Ararat, Çağın, Tekin, Cem
Vydáno:	Cornell University Library, arXiv.org
Témata:	Algorithms Python Source code Black boxes Multiple objective analysis Optimization
On-line přístup:	Citation/Abstract Full text outside of ProQuest
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Abstrakt:	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
Zdroj:	Engineering Database