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 ...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.展开更多
为了解决复杂工程优化问题计算量大的问题,提出了基于Kriging代理模型的改进EGO(Efficient Global Optimization)算法.采用小生境微种群遗传算法求解Kriging模型的相关向量,避免了模式搜索算法求解相关向量时对初始值的敏感性问题.采用...为了解决复杂工程优化问题计算量大的问题,提出了基于Kriging代理模型的改进EGO(Efficient Global Optimization)算法.采用小生境微种群遗传算法求解Kriging模型的相关向量,避免了模式搜索算法求解相关向量时对初始值的敏感性问题.采用小生境微种群遗传算法,结合无惩罚因子的惩罚函数法对EI(Expected Improvement)函数寻优,解决了惩罚因子难以选择的问题,增强了算法的鲁棒性.采用2个数值算例和1个工程算例对算法进行测试的结果表明,改进后的EGO算法收敛精度更高,比较适合在工程中应用.展开更多
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).
文摘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.
文摘为了解决复杂工程优化问题计算量大的问题,提出了基于Kriging代理模型的改进EGO(Efficient Global Optimization)算法.采用小生境微种群遗传算法求解Kriging模型的相关向量,避免了模式搜索算法求解相关向量时对初始值的敏感性问题.采用小生境微种群遗传算法,结合无惩罚因子的惩罚函数法对EI(Expected Improvement)函数寻优,解决了惩罚因子难以选择的问题,增强了算法的鲁棒性.采用2个数值算例和1个工程算例对算法进行测试的结果表明,改进后的EGO算法收敛精度更高,比较适合在工程中应用.
