基于遗传算法的Kriging模型构造与优化

The Construction and Optimization of Kriging Metamodel Based on Genetic Algorithms

相关模型参数的确定是Kriging模型构造的关键,讨论了利用传统数值优化方法,如模式搜索方法,确定相关参数存在依赖搜索起始点等缺点;利用遗传算法获得满足目标函数全局最小情况下的相关模型参数,解决了模型的构造对起始点依赖的问题;将遗传算法与改进后的Kriging模型结合,基于近似模型对系统进行全局最优化.

The determination of correlation parameters is the key point for constructing Kriging model. It is discussed that the optimum result will depend on the starting points to search if the correlation parameters are determined using conventional numerical optimization methods, such as pattern search method. Then the global optimums of correlation model parameters are obtained by Generic Algorithms （GA）. The problem of Kriging construction depending on the starting points is solved. Additionally, using GA with the improved Kriging model the system can be globally optimized based on the approximate model of the system.