论文标题
上限信托界限可行性标准，用于将飞机设计的混合约束贝叶斯优化混合的贝叶斯优化
Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design
论文作者
论文摘要
贝叶斯优化方法已成功应用于昂贵评估的黑匣子优化问题。在本文中，我们调整了所谓的超级效率全局优化算法，以解决更准确的约束问题。所提出的方法通过上限信托结合来处理约束，后者通过结合高斯过程给出的平均预测和相关的不确定性函数来鼓励对可行领域的探索。最重要的是，提出了基于学习率标准的改进程序，以增强剥削和勘探权衡。我们在一组数值实验中显示了该方法的良好潜力。最后，我们向概念飞机配置提出了一个应用程序，与一组最先进的黑匣子优化求解器相比，我们在上面显示了所提出方法的优越性。关键字：全局优化，约束优化，黑匣子优化，贝叶斯优化，高斯流程。
Bayesian optimization methods have been successfully applied to black box optimization problems that are expensive to evaluate. In this paper, we adapt the so-called super effcient global optimization algorithm to solve more accurately mixed constrained problems. The proposed approach handles constraints by means of upper trust bound, the latter encourages exploration of the feasible domain by combining the mean prediction and the associated uncertainty function given by the Gaussian processes. On top of that, a refinement procedure, based on a learning rate criterion, is introduced to enhance the exploitation and exploration trade-off. We show the good potential of the approach on a set of numerical experiments. Finally, we present an application to conceptual aircraft configuration upon which we show the superiority of the proposed approach compared to a set of the state-of-the-art black box optimization solvers. Keywords: Global Optimization, Mixed Constrained Optimization, Black box optimization, Bayesian Optimization, Gaussian Process.
