We consider the problem of constrained multi-objective blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions satisfying a set of constraints while minimizing the number of function evaluations. For example, in aviation power system design applications, we need to find the designs that trade-off total energy and the mass while satisfying specific thresholds for motor temperature and voltage of cells. This optimization requires performing expensive computational simulations to evaluate designs. In this paper, we propose a new approach referred as {\em Max-value Entropy Search for Multi-objective Optimization with Constraints (MESMOC)} to solve this problem. MESMOC employs an output-space entropy based acquisition function to efficiently select the sequence of inputs for evaluation to uncover high-quality pareto-set solutions while satisfying constraints. We apply MESMOC to two real-world engineering design applications to demonstrate its effectiveness over state-of-the-art algorithms.

翻译：我们考虑使用昂贵的功能评价来限制多目标黑盒优化的问题,其目标是接近满足一系列限制条件的一套真正的Pareto解决方案,同时尽量减少功能评价的数量。例如,在航空动力系统设计应用程序中,我们需要找到在满足运动温度和电池电压的具体阈值的同时权衡总能量和质量的设计。这种优化要求进行昂贵的计算模拟来评价设计。在本文件中,我们提议了一种新的方法,称为“最大值最大值搜索多目标优化与制约(MESMOC) } ” 来解决这个问题。 MESMOC使用基于输出空间的输入权获取功能来高效地选择用于评估的输入序列,以发现高质量的微粒定值解决方案,同时满足各种限制。我们将MESMOC应用到两个真实世界工程设计应用程序中,以展示其相对于最新算法的有效性。