VOPy: A Framework for Black-box Vector Optimization
This provides a tool for researchers and practitioners in optimization fields to address complex multi-objective problems with partial orders, but it is incremental as it builds on existing methods with a new framework.
The authors tackled black-box vector optimization by introducing VOPy, an open-source Python library that extends traditional multi-objective optimization tools to handle flexible, cone-based ordering of solutions across various environments, resulting in a modular framework for research and application.
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.