VOPy 是用于黑箱向量优化的开源框架，支持多目标协同优化。
VOPy: A Framework for Black-box Vector Optimization
- 基于凸锥定义解的偏序关系，实现灵活多目标排序
- 兼容噪声观测、离散/连续空间与有限预算等复杂场景
- 模块化设计，便于集成现有方法或开发新算法
我们提出 VOPy，一个开源 Python 库，用于解决黑箱向量优化问题，即在由凸锥诱导的偏序关系下同时优化多个目标。VOPy 超越传统多目标优化工具，支持基于凸锥的灵活解排序，适用于存在观测噪声、离散或连续设计空间、有限预算及批量观测的场景。该框架具备模块化架构，便于集成已有方法并开发新算法。本文详细介绍了 VOPy 的架构、使用方式及其对向量优化领域研究与应用的推动潜力。代码已公开于 https://github.com/Bilkent-CYBORG/VOPy。
原文摘要 · Abstract (English)
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.
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