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Shape Optimization of Corrugated Coatings Under Grazing Incidence Using a Genetic Algorithm

机译:基于遗传算法的波纹入射条件下波纹涂层的形状优化

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We report on the use of a genetic algorithm (GA) to design optimal shapes for a corrugated coating under near-grazing incidence. A full-wave electromagnetic solver based on the boundary integral formulation is employed to predict the performance of the coating shape. In our GA implementation, we encode each shape of the coating into a binary chromosome. A two-point crossover scheme involving three chromosomes and a geometrical filter are implemented to achieve efficient optimization. Standard magnetic radar absorbing material (MAGRAM) is used for the absorber coating. We present the optimized coating shapes depending on different polarizations. A physical interpretation for the optimized structure is discussed and the resulting shape is compared to conventional planar and triangular shaped designs. Next, we extend this problem from single to multiobjective optimization by using Pareto GA. The optimization results with two different objectives, viz. height (or weight) of the coating versus absorbing performance, are presented.
机译:我们报告了使用遗传算法(GA)为近乎掠食情况下的波纹涂层设计最佳形状。基于边界积分公式的全波电磁求解器用于预测涂层形状的性能。在我们的GA实施中,我们将涂层的每种形状编码为二进制染色体。实现涉及三个染色体的两点交叉方案和几何滤波器,以实现有效的优化。标准的电磁雷达吸收材料(MAGRAM)用于吸收体涂层。我们根据不同的偏振呈现最佳的涂层形状。讨论了优化结构的物理解释,并将得到的形状与常规的平面和三角形设计进行了比较。接下来,我们使用Pareto GA将这个问题从单目标优化扩展到多目标优化。具有两个不同目标的优化结果,即。给出了涂层的高度(或重量)与吸收性能的关系。

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