1 research outputs found
Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints
We consider the problem of constrained multi-objective (MO) 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. We propose a novel
framework named Uncertainty-aware Search framework for Multi-Objective
Optimization with Constraints (USeMOC) to efficiently select the sequence of
inputs for evaluation to solve this problem. The selection method of USeMOC
consists of solving a cheap constrained MO optimization problem via surrogate
models of the true functions to identify the most promising candidates and
picking the best candidate based on a measure of uncertainty. We applied this
framework to optimize the design of a multi-output switched-capacitor voltage
regulator via expensive simulations. Our experimental results show that USeMOC
is able to achieve more than 90 % reduction in the number of simulations needed
to uncover optimized circuits.Comment: 9 pages, 2 figures, 1 tabl