当前位置: X-MOL 学术Comput. Ind. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Bayesian optimization for a multiple-component system with target values
Computers & Industrial Engineering ( IF 7.9 ) Pub Date : 2021-04-20 , DOI: 10.1016/j.cie.2021.107310
Jihwan Jeong , Hayong Shin

Bayesian optimization (BO) that employs the Gaussian process (GP) as a surrogate model has recently gained much attention in optimization of expensive black-box functions. In BO, the number of experiments necessary to optimize a function can be considerably reduced by sequentially selecting next design points that are optimal with respect to some sampling criterion. However, little research has been done to address the optimization of a multiple-component system where each component has a certain target value to meet. In this paper, we aim to find an optimal design parameter in the sense that the response function is close to the target value for every component. To this end, the squared errors from the targets are aggregated to produce an objective function. Instead of modeling this objective using GP as in the standard BO formulation, we place the GP prior over the response function. As a result, the distribution over the objective function follows that of the weighted sum of non-central chi-squared random variables (WSNC) due to the inter-dependency between responses. When components of the system are changed, the standard BO suffers inefficiency; however, our formulation enables us to retain a learned model, resulting in better efficiency. We compare the rates of convergence of different BO methods and other black-box optimization baselines using several test functions. The performance of our model is comparable to the standard BO when there is no change in the system, but the superiority of our method becomes clear when changes in the components occur.



中文翻译:

具有目标值的多组分系统的贝叶斯优化

使用高斯过程(GP)作为替代模型的贝叶斯优化(BO)最近在昂贵的黑盒函数的优化中引起了很多关注。在BO中,可以通过顺序选择相对于某些采样标准而言最佳的下一个设计点来大大减少优化功能所需的实验次数。但是,很少有研究来解决多组件系统的优化问题,在该系统中,每个组件都有一定的目标值可以满足。在本文中,我们的目标是在响应函数接近每个组件的目标值的意义上找到最佳设计参数。为此,来自目标的平方误差被汇总以产生目标函数。与其像在标准BO公式中那样使用GP为该目标建模,我们将GP放在响应函数之前。结果,由于响应之间的相互依赖性,目标函数的分布遵循非中心卡方随机变量(WSNC)的加权和的分布。当更改系统组件时,标准BO效率低下;但是,我们的表述使我们能够保留学习的模型,从而提高了效率。我们使用几种测试功能比较了不同BO方法和其他黑盒优化基线的收敛速度。当系统没有变化时,我们模型的性能可与标准BO媲美,但是当组件发生变化时,我们方法的优势就显而易见。由于响应之间的相互依赖性,目标函数的分布遵循非中心卡方随机变量(WSNC)的加权和的分布。当更改系统组件时,标准BO效率低下;但是,我们的表述使我们能够保留学习的模型,从而提高了效率。我们使用几种测试功能比较了不同BO方法和其他黑盒优化基线的收敛速度。当系统没有变化时,我们模型的性能可与标准BO媲美,但是当组件发生变化时,我们方法的优势就显而易见。由于响应之间的相互依赖性,目标函数的分布遵循非中心卡方随机变量(WSNC)的加权和的分布。当更改系统组件时,标准BO效率低下;但是,我们的表述使我们能够保留学习的模型,从而提高了效率。我们使用几种测试功能比较了不同BO方法和其他黑盒优化基线的收敛速度。当系统没有变化时,我们模型的性能可与标准BO媲美,但是当组件发生变化时,我们方法的优势就显而易见。但是,我们的表述使我们能够保留学习的模型,从而提高了效率。我们使用几种测试功能比较了不同BO方法和其他黑盒优化基线的收敛速度。当系统没有变化时,我们模型的性能可与标准BO媲美,但是当组件发生变化时,我们方法的优势就显而易见。但是,我们的表述使我们能够保留学习的模型,从而提高了效率。我们使用几种测试功能比较了不同BO方法和其他黑盒优化基线的收敛速度。当系统没有变化时,我们模型的性能可与标准BO媲美,但是当组件发生变化时,我们方法的优势就显而易见。

更新日期:2021-04-29
down
wechat
bug