一种基于Kriging模型的天线高维全局优化算法

A high dimensional global optimization method for antenna design based on Kriging model

传统的天线优化设计需要对大量的参数组合进行电磁仿真后才能得到最优结果,使得天线高维优化设计效率普遍较低。针对该问题,使用在参数空间均匀分布的少量样本及其仿真结果构建初始Kriging模型,优化循环中每代种群由高适应度个体和高离散性个体组成,依据Kriging模型预测的个体响应和不确定性,对进化后的下一代种群进行筛选,选择最优个体执行电磁仿真并更新Kriging模型。利用此方法优化一个6变量E形天线的工作频点,相比同类优化算法,所需的电磁仿真次数可减少80%左右。

Traditional antenna optimization designs need numerous simulation trials of different parameter combinations to reach the optimum, which leads to low efficiency in solving high dimensional antenna design and optimization problems. To address this issue, we design an initial Kriging model by using a few uniformly distributed sampling points and their simulation data. During the optimization it- erations, the population of each generation is comprised of individuals with high fitness as well as indi- viduals with high diversity. The optimal individual is selected according to its responses and uncertainty predicted by the Kriging model. Electromagnetic simulations are conducted for this individual, and the results are used to update the Kriging model. This algorithm is applied to optimize the resonant frequen- cies of an E-shaped antenna with 6 variables. Compared with other optimization methods, the number of EM simulation is reduced by about 80%.