Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes and works by maximizing the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed.

中文翻译：

---

贝叶斯多目标优化中的目标解决方案：顺序和批处理版本

多目标优化旨在找到冲突目标的权衡解决方案。这些构成了帕累托最优集。在评估成本高昂的函数的上下文中，查找整个集合是不可能的，而且通常是无信息的。由于最终用户通常更喜欢目标空间的某个部分，我们修改了贝叶斯多目标优化算法，该算法使用高斯过程并通过最大化预期超体积改进来工作，以将搜索集中在首选区域。高斯过程和目标策略的累积效应导致特别有效地收敛到帕累托集的所需部分。为了利用并行计算的优势，提出并分析了目标标准的多点扩展。