Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints

Understand

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.