A computationally efficient surrogate model is suggested to approximate the objective and constraint function values, which replace expensive evaluation of the objective and constraint function values in numerical simulation-based optimization. Kriging surrogate model has been widely used in surrogate-based design optimization (SBDO) to replace the highly nonlinear black-box functions. In this paper, a novel adaptive Kriging model based on parallel infilling strategy is proposed to improve both the numerical accuracy and efficiency of the SBDO methods. The parallel infilling strategy consists of two parts: local sampling and globaluthor sampling. In the local sampling, new additional sampling points are generated only within a limited region that is determined according to the optimal point at the last iteration, while in global sampling they are generated based on the fitting error estimation in the whole region. The effectiveness of the proposed algorithm is verified through applications to analytical functions. Then the algorithm is applied to the multi-objective optimal design of an ironless permanent magnet synchronous linear motor.

中文翻译：

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一种基于并行填充策略的自适应克里金辅助电磁装置单目标和多目标优化算法

建议使用计算效率高的替代模型来逼近目标函数值和约束函数值，从而取代基于数值模拟的优化中对目标函数值和约束函数值的昂贵评估。克里金代理模型已广泛用于基于代理的设计优化（SBDO），以取代高度非线性的黑盒函数。在本文中，提出了一种基于并行填充策略的新型自适应克里金模型，以提高 SBDO 方法的数值精度和效率。并行填充策略由两部分组成：局部采样和全局作者采样。在局部采样中，仅在根据上次迭代的最优点确定的有限区域内生成新的附加采样点，而在全局采样中，它们是根据整个区域的拟合误差估计生成的。通过对分析函数的应用验证了所提出算法的有效性。然后将该算法应用于无铁心永磁同步直线电机的多目标优化设计。