VOPy: A Framework for Black-box Vector Optimization

TL;DR

VOPy is an open-source Python library that facilitates black-box vector optimization with multiple objectives, supporting flexible cone-based solution ordering and diverse application scenarios.

Contribution

It introduces a modular framework for black-box vector optimization that extends traditional multi-objective tools with cone-based ordering and broad applicability.

Findings

Supports environments with observation noise

Handles discrete and continuous design spaces

Enables batch observations and limited budgets

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.