.png){kind=icon}
返回
A Bayesian optimization approach for data-driven mixed-integer nonlinear programming problems
.png){kind=icon}
PRE
AI
DOI：10.1002/aic.18448.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The optimization of process systems within the field of Chemical Engineering often confronts uncertainties that can exert significant influences on the performance and dependability of the obtained solutions. This research endeavors to investigate the application of Bayesian optimization in the realm of constrained mixed integer nonlinear problems. A comparative analysis was conducted, exploring different surrogate models, and evaluating diverse kernel functions and acquisition functions. Furthermore, a sampling strategy was devised to assess the enhancement achieved by the acquisition function. The findings of this article reveal the superior performance of sparse Gaussian processes in conjunction with computationally inexpensive acquisition functions, thereby highlighting their suitability for addressing mixed integer nonlinear programming problems characterized by noisy functions and stochastic behavior. Consequently, this article presents a computationally efficient approach to effectively tackle the challenges associated with data-driven mixed integer nonlinear programming problems within the domain of Process System Engineering.
Keyword:
artificial intelligence
mathematical modeling
optimization
process
simulation
期刊
IF:
4
论文数:
1.1W
被引数:
2.9W
机构
引用论文
.png){kind=icon}
分享收藏Advanced viscometric thermometer: Steady and unsteady state temperature measurement in electric or magnetic fields高级测粘温度计: 在电场或磁场中进行稳态和非稳态温度测量
PREAI
分享收藏
查看更多内容