Many real-world black-box optimization problems have multiple conflicting objectives. Rather than attempting to approximate the entire set of Pareto-optimal solutions, interactive preference learning allows to focus the search on the most relevant subset. However, few previous studies have exploited the fact that utility functions are usually monotonic. In this paper, we address the Bayesian Optimization with Preference Exploration (BOPE) problem and propose using a neural network ensemble as a utility surrogate model. This approach naturally integrates monotonicity and supports pairwise comparison data. Our experiments demonstrate that the proposed method outperforms state-of-the-art approaches and exhibits robustness to noise in utility evaluations. An ablation study highlights the critical role of monotonicity in enhancing performance.

翻译：许多现实世界中的黑箱优化问题存在多个相互冲突的目标。相较于尝试近似整个帕累托最优解集，交互式偏好学习能够将搜索聚焦于最相关的子集。然而，先前的研究很少利用效用函数通常具有单调性这一事实。本文针对带偏好探索的贝叶斯优化问题，提出使用神经网络集成作为效用代理模型。该方法自然地整合了单调性，并支持成对比较数据。实验表明，所提方法优于现有先进方法，并且在效用评估存在噪声时表现出鲁棒性。消融研究进一步凸显了单调性对提升性能的关键作用。