Efficiently solving multi-objective optimization problems for simulation
optimization of important scientific and engineering applications such as
materials design is becoming an increasingly important research topic. This is
due largely to the expensive costs associated with said applications, and the
resulting need for sample-efficient, multiobjective optimization methods that
efficiently explore the Pareto frontier to expose a promising set of design
solutions. We propose moving away from using explicit optimization to identify
the Pareto frontier and instead suggest searching for a diverse set of outcomes
that satisfy user-specified performance criteria. This method presents decision
makers with a robust pool of promising design decisions and helps them better
understand the space of good solutions. To achieve this outcome, we introduce
the Likelihood of Metric Satisfaction (LMS) acquisition function, analyze its
behavior and properties, and demonstrate its viability on various problems.

该论文提出了一种利用 LMS 指标和多目标优化方法来寻找符合用户性能标准的智能设计方案的方法，并在多个问题上进行了验证与分析。