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
在函数最优点求解问题中,如果函数表达式很复杂(或黑箱问题),很难利用常用的优化算法求解全局最优点。这时需要先用插值或拟合函数去逼近原函数,然后对新的逼近函数求最优点,进而得到原函数的最优点。基于上述思想,Jones等人于1989提出...在函数最优点求解问题中,如果函数表达式很复杂(或黑箱问题),很难利用常用的优化算法求解全局最优点。这时需要先用插值或拟合函数去逼近原函数,然后对新的逼近函数求最优点,进而得到原函数的最优点。基于上述思想,Jones等人于1989提出了EGO(Efficient Global Optimization)算法。EGO算法不足之处在于:它浪费了一个采样点判断EGO算法是否满足终止条件,寻求EI最大值点的收敛速率不高,算法终止条件选择不佳,不能保证估计值的最小点(即EI最大值点)是原函数的内点。针对EGO算法的不足之处,提出了改进的加速EGO算法。仿真实验表明,SEGO极大地节省了运算时间,并且能获得任意精度的全局最优点。展开更多
For an energy-efficient induction machine, the life-cycle cost (LCC) usually is the most important index to the consumer. With this target, the optimization design of a motor is a complex nonlinear problem with constr...For an energy-efficient induction machine, the life-cycle cost (LCC) usually is the most important index to the consumer. With this target, the optimization design of a motor is a complex nonlinear problem with constraints. To solve the problem, the authors introduce a united random algorithm. At first, the problem is divided into two parts, the optimal rotor slots and the optimization of other dimensions. Before optimizing the rotor slots with genetic algorithm ( GA), the second part is solved with TABU algorithm to simplify the problem. The numerical results showed that this method is better than the method using a traditional algorithm.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.
文摘在函数最优点求解问题中,如果函数表达式很复杂(或黑箱问题),很难利用常用的优化算法求解全局最优点。这时需要先用插值或拟合函数去逼近原函数,然后对新的逼近函数求最优点,进而得到原函数的最优点。基于上述思想,Jones等人于1989提出了EGO(Efficient Global Optimization)算法。EGO算法不足之处在于:它浪费了一个采样点判断EGO算法是否满足终止条件,寻求EI最大值点的收敛速率不高,算法终止条件选择不佳,不能保证估计值的最小点(即EI最大值点)是原函数的内点。针对EGO算法的不足之处,提出了改进的加速EGO算法。仿真实验表明,SEGO极大地节省了运算时间,并且能获得任意精度的全局最优点。
文摘For an energy-efficient induction machine, the life-cycle cost (LCC) usually is the most important index to the consumer. With this target, the optimization design of a motor is a complex nonlinear problem with constraints. To solve the problem, the authors introduce a united random algorithm. At first, the problem is divided into two parts, the optimal rotor slots and the optimization of other dimensions. Before optimizing the rotor slots with genetic algorithm ( GA), the second part is solved with TABU algorithm to simplify the problem. The numerical results showed that this method is better than the method using a traditional algorithm.
