VOPy: A Framework for Black-box Vector Optimization

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Bibliographic Details
Published in:	arXiv.org (Dec 9, 2024), p. n/a
Main Author:	Yaşar, Cahit Yıldırım
Other Authors:	Efe Mert Karagözlü, İlter Onat Korkmaz, Ararat, Çağın, Tekin, Cem
Published:	Cornell University Library, arXiv.org
Subjects:	Algorithms Python Source code Black boxes Multiple objective analysis Optimization
Online Access:	Citation/Abstract Full text outside of ProQuest
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Description
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
Source:	Engineering Database