基于改进EGO算法的黑箱函数全局最优化  

Global optimization of black-box function using improved EGO algorithm

作　　者：王彦[1] 尹素菊[1] 

机构地区：[1]北京工业大学应用数理学院,北京100124

出　　处：《计算机应用研究》2015年第3期764-767,共4页Application Research of Computers

摘　　要：基于Kriging模型的EGO算法是一种适用于黑箱函数求极值的全局最优化算法,但该算法忽略了对Kriging模型精度的控制。针对该算法的不足之处,提出了兼顾Kriging模型精度与模型寻优的迭代函数,并将改进后的EGO算法应用于五个检验函数及一个存货模型,从Kriging模型精度及优化结果两方面对改进前后的算法进行比较。结果表明,改进后的EGO算法提高了最终Kriging模型的精度,并在对目标函数进行少量估值的情况下获得了更为全局化的最优解。EGO algorithm based on Kriging model is a suitable method for the global optimization of black-box function, but it ignored the accuracy of Kriging model. To overcome the shortcoming of EGO algorithm, this paper proposed an improved algo- rithm, and it＇ s iterative function took into account the accuracy and the optimization of the Kriging model. Then this paper ap- plied the algorithm to five test functions and an inventory model. The results show that compared to the original EGO algo- rithm, the improved algorithm can improve the final accuracy of the Kriging model and obtain a more globally optimal solution via a small amount of the valuations to the objective function.

关 键 词：计算机实验设计 KRIGING模型 EI方法 全局最优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]