  We consider the problem of black-box multi-objective optimization (MOO) using
expensive function evaluations (also referred to as experiments), where the
goal is to approximate the true Pareto set of solutions by minimizing the total
resource cost of experiments. For example, in hardware design optimization, we
need to find the designs that trade-off performance, energy, and area overhead
using expensive computational simulations. The key challenge is to select the
sequence of experiments to uncover high-quality solutions using minimal
resources. In this paper, we propose a general framework for solving MOO
problems based on the principle of output space entropy (OSE) search: select
the experiment that maximizes the information gained per unit resource cost
about the true Pareto front. We appropriately instantiate the principle of OSE
search to derive efficient algorithms for the following four MOO problem
settings: 1) The most basic em single-fidelity setting, where experiments are
expensive and accurate; 2) Handling em black-box constraints} which cannot be
evaluated without performing experiments; 3) The discrete multi-fidelity
setting, where experiments can vary in the amount of resources consumed and
their evaluation accuracy; and 4) The em continuous-fidelity setting, where
continuous function approximations result in a huge space of experiments.
Experiments on diverse synthetic and real-world benchmarks show that our OSE
search based algorithms improve over state-of-the-art methods in terms of both
computational-efficiency and accuracy of MOO solutions.
