基于多目标优化的稀疏天线阵综合

L. Pappula, D. Ghosh
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引用次数: 1

摘要

稀疏天线阵列的合成过程涉及同时最小化相互冲突的参数,如峰值旁瓣电平和第一零波束宽度。这需要开发一个多目标优化过程,该过程将根据手头的应用程序提供最佳折衷解决方案。本文采用NSGA-II非支配排序遗传算法实现了多目标优化。与单目标优化方法相比,这种方法产生了更多的改进结果,同时它提供了基于Pareto前沿选择解决方案的灵活性。
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Sparse antenna array synthesis using multi-objective optimization
The process of sparse antenna array synthesis involves the simultaneous minimization of the number of mutually conflicting parameters, such as peak sidelobe level and first null beam width. This necessitates the development of a multi objective optimization process which will provide the best compromised solution based on the application at hand. In this paper multi-objective optimization is achieved using the non-dominating sorting genetic algorithm of NSGA-II. This approach yields much more improved results as compared to single objective optimization approach and at the same time it offers flexibility in choosing the solution based on the Pareto front.
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