A parallel chemical reaction optimization method based on preference-based multi-objective expected improvement  

作　　者：Mingqi Jiang Zhuo Wang Zhijian Sun Jian Wang 

机构地区：[1]Shenyang Institute of Automation,Chinese Academy of Science,Shenyang 110016,China [2]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Chinese Journal of Chemical Engineering》2025年第2期82-92,共11页中国化学工程学报(英文版)

基　　金：the Nature Foundation(Basic Research)Special Project of Shenyang(22-315-6-20);Liaoning Province Artificial Intelligence Innovation and Development Program Project(2023JH26/10300014);Basic Research Program of Shenyang Institute of Automation,Chinese Academy of Sciences(2023JC2K03).

摘　　要：Optimizing chemical reaction parameters is an expensive optimization problem. Each experiment takes a long time and the raw materials are expensive. High-throughput methods combined with the parallel Efficient Global Optimization algorithm can effectively improve the efficiency of the search for optimal chemical reaction parameters. In this paper, we propose a multi-objective populated expectation improvement criterion for providing multiple near-optimal solutions in high-throughput chemical reaction optimization. An l-NSGA2, employing the Pseudo-power transformation method, is utilized to maximize the expected improvement acquisition function, resulting in a Pareto solution set comprising multiple designs. The approximation of the cost function can be calculated by the ensemble Gaussian process model, which greatly reduces the cost of the exact Gaussian process model. The proposed optimization method was tested on a SNAr benchmark problem. The results show that compared with the previous high-throughput experimental methods, our method can reduce the number of experiments by almost half. At the same time, it theoretically enhances temporal and spatial yields while minimizing by-product formation, potentially guiding real chemical reaction optimization.

关 键 词：Algorithm Chemical reaction Computer simulation Efficient global optimization Machine learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]