利用改进灰狼算法优化二维综合孔径天线阵列

Optimisation of 2D Integrated Aperture Antenna Arrays Using Improved Grey Wolf Algorithm

摘要: 相同灵敏度或分辨率下单孔径天线所需的成本和实现难易程度都远高于孔径阵列，同时相同配置下多天线组成的阵列望远镜性能一定优于单天线望远镜，因此优化阵列十分重要。Kiehbadroudinezhad等人利用遗传算法进行优化已取得不错效果，由于遗传算法存在计算成本高、稳定性差等问题，因此本文提出了改进的灰狼算法(Grey Wolf Optimizer, GWO)进行了优化改进，并与VLA_A这个现实中表现良好的Y型阵列作对比。实验表明对于27根天线阵列优化，提出的改进的灰狼算法比遗传算法更有效地分配uv域，其最终优化综合孔径天线阵列的DV Density为0.3048，该值小于VLA_A的DV Density和遗传算法优化所得的结果。

Abstract: The cost and ease of implementation of a single aperture antenna are much higher than that of an aperture array for the same sensitivity or resolution, while an array telescope consisting of multiple antennas in the same configuration is bound to outperform a single-antenna telescope, so it is important to optimise the array. Kiehbadroudinezhad et al have achieved good results using genetic algorithms for optimisation, due to the high computational cost and poor stability of genetic algorithms, this paper proposes an improved Grey Wolf Optimizer (GWO) for optimi-sation and improvement and compares it with VLA_A, a realistic and well-performing Y-type array which performs well in reality. The experiments show that for the 27 antenna array optimization, the proposed improved Grey Wolf algorithm allocates the uv-domain more efficiently than the genetic algorithm, and its final optimized DV Density of the integrated aperture antenna array is 0.3048, which is smaller than that of the DV Density of the VLA_A and the result obtained from the optimization of the genetic algorithm.