摘要
设计类问题在科学研究和工业领域无处不在.作为一种十分有效的全局优化算法,近年来,贝叶斯优化方法在设计类问题上被广泛应用.通过设计恰当的概率代理模型和采集函数,贝叶斯优化框架只需经过少数次目标函数评估即可获得理想解,非常适用于求解目标函数表达式未知、非凸、多峰和评估代价高昂的复杂优化问题.从方法论和应用领域两方面深入分析、讨论和展望了贝叶斯优化的研究现状、面临的问题和应用领域,期望为相关领域的研究者提供有益的借鉴和参考.
Designing problems are ubiquitous in science research and industry applications. In recent years, Bayesian optimization, which acts as a very effective global optimization algorithm, has been widely applied in designing problems. By structuring the probabilisfic surrogate model and the acquisition function appropriately, Bayesian optimization framework can guarantee to obtain the optimal solution under a few numbers of function evaluations, thus it is very suitable to solve the extremely complex optimization problems in which their objective functions could not be expressed, or the functions are non-convex, multimodal and computational expensive. This paper provides a detailed analysis on Bayesian optimization in methodology and application areas, and discusses its research status and the problems in future researches. This work is hopefully beneficial to the researchers from the related communities.
作者
崔佳旭
杨博
CUI Jia-Xu;YANG Bo(Key Laboratory of Symbolic Computation and Knowledge Engineering for the Ministry of Education（Jilin University）,Changchun 130012,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China)
出处
《软件学报》
EI
CSCD
北大核心
2018年第10期3068-3090,共23页
Journal of Software
基金
国家自然科学基金(61572226
61876069)
吉林省重点科技研发项目(20180201067GX
20180201044GX)~~
关键词
贝叶斯优化
全局优化算法
概率代理模型
采集函数
黑箱
Bayesian optimization
global optimization algorithm
probabilistic surrogate model
acquisition function
black-box
