  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.
