论文标题
多保真多目标贝叶斯优化：输出空间熵搜索方法
Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach
论文作者
论文摘要
我们研究了黑框优化多个目标的新问题，这些目标通过多余功能评估的评估及其准确性各不相同。总体目标是通过最大程度地减少用于功能评估所消耗的资源来近似真正的帕累托解决方案集。例如，在电力系统设计优化中，我们需要找到使用多保真模拟器进行设计评估的设计，这些设计使成本，大小，效率和热耐受性。在本文中，我们提出了一种新的方法，称为多保真输出空间熵搜索多目标优化（MF-OSEMO）来解决此问题。关键的想法是选择候选输入和保真矢量对的顺序，以最大程度地提高有关单位资源成本的真正帕累托前部的信息。我们对几个合成和现实基准问题的实验表明，MF-Osemo均具有近似值，可显着改善用于多目标优化的最新单一预性算法。
We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations that vary in the amount of resources consumed and their accuracy. The overall goal is to approximate the true Pareto set of solutions by minimizing the resources consumed for function evaluations. For example, in power system design optimization, we need to find designs that trade-off cost, size, efficiency, and thermal tolerance using multi-fidelity simulators for design evaluations. In this paper, we propose a novel approach referred as Multi-Fidelity Output Space Entropy Search for Multi-objective Optimization (MF-OSEMO) to solve this problem. The key idea is to select the sequence of candidate input and fidelity-vector pairs that maximize the information gained about the true Pareto front per unit resource cost. Our experiments on several synthetic and real-world benchmark problems show that MF-OSEMO, with both approximations, significantly improves over the state-of-the-art single-fidelity algorithms for multi-objective optimization.
