论文标题
不确定性意识到多目标贝叶斯优化的搜索框架和约束
Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints
论文作者
论文摘要
我们考虑使用昂贵的函数评估的约束多目标（MO）黑框优化的问题，该问题的目标是近似满足一组约束的真正帕累托解决方案集，同时最小化功能评估的数量。我们提出了一个新颖的框架，以使用约束（USEMOC）进行多目标优化的不确定性感知搜索框架，以有效地选择输入序列以评估以解决此问题。 USEMOC的选择方法包括通过真实功能的替代模型来解决廉价约束的MO优化问题，以识别最有前途的候选人，并根据不确定性的度量选择最佳候选人。我们应用了此框架，以优化通过昂贵的模拟的多输出开关电容器电压调节器的设计。我们的实验结果表明，USEMOC能够在发现优化电路所需的模拟数量中减少90％以上。
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.
