Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints

• URL: http://arxiv.org/abs/2008.07029v2
• Date: Tue, 1 Sep 2020 04:53:56 GMT
• Title: Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints
• Authors: Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
• Abstract summary: We consider the problem of constrained multi-objective (MO) blackbox optimization using expensive function evaluations. We propose a novel framework named Uncertainty-aware Search framework for Multi-Objective Optimization with Constraints. We show that USeMOC is able to achieve more than 90 % reduction in the number of simulations needed to uncover optimized circuits.
• Score: 44.25245545568633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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