Knee Detection in Bayesian Multiobjective Optimization Using Thompson Sampling
提出在贝叶斯优化框架中使用汤普森抽样来高效定位帕累托前沿的膝点区域，实验表明只需少量评估即可准确检测单膝和多膝，为决策者提供计算高效的优化方法。
Real-world problems often consist of multiple conflicting objectives to be optimized simultaneously, featuring a set of Pareto-optimal solutions. Estimating the entire Pareto front can be computationally expensive, and is not always necessary, as decision makers (DMs) will likely be interested only in specific regions of the Pareto front. In the absence of knowledge about the DM preferences, the so-called knees in the Pareto front are considered to be particularly attractive. In this article, we propose using Thompson sampling in the Bayesian optimization framework to estimate the location of the knee regions in a data-efficient manner. Our experimental results show that the proposed methods accurately locate the knee regions after a very small number of evaluations, providing a computationally efficient approach to single- and multiknee detection in multiobjective optimization.