使用遗传算法的稀疏数组

R. Haupt, J. J. Menozzi, C. J. McCormack
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引用次数: 1038

摘要

展示了如何应用遗传算法(GAs)来获得最优的稀疏阵列。对50元各向同性点源线性阵列进行了5次阵列细化优化,得到一致的遗传算法结果。所有运行之间的误差都在0.8dB以内。给出了遗传算法优化后的50元阵列远场方向图示例。该阵列模式针对最小最大旁瓣电平进行了优化。结果表明,遗传算法是优化阵列细化的理想方法。基因中的位对应于打开或关闭该元素。虽然遗传算法很慢,但它可以处理涉及许多天线元件的非常大的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thinned arrays using genetic algorithms
It is shown how to apply genetic algorithms (GAs) to arrive at an optimally thinned array. Consistent GA results from five array thinning optimizations for a 50-element linear array of isotropic point sources are shown. All of the runs are within 0.8dB of one another. A sample far-field pattern for the 50-element array resulting from the GA optimization is shown. The array patterns are optimized for the lowest maximum sidelobe level. It is concluded that a GA is ideal for optimizing the thinning of an array. The bits in a gene correspond to turning the element on or off. Although a GA is slow, it can handle very large problems involving many antenna elements.<>
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