基于蚁群优化算法（AHCOA）的线阵方向图综合
Optimal Pattern synthesis of linear antenna array using Ant Hill Colonization Optimization algorithm(AHCOA)
摘要：
本文的目的是介绍AHCOA的电磁和天线社区。AHCOA是一种新的Meta启发式算法，它的灵感来自于蚁群中的层次结构和部门。它具有高概率的潜力，不仅在解决无约束，但也约束优化问题。本文将该算法应用于线阵天线的方向图综合，主要有以下几个方面：通过均匀激励，考虑均匀阵元的等间距。AHCOA用于获得阵列方向图，以实现最小旁瓣电平。的结果相比，其他国家的最先进的自然为基础的算法，如蚂蚁狮子优化，这表明了相当大的改善AHCOA。
英文摘要：
The aim of this paper is to introduce AHCOA to the electromagnetic and antenna community. AHCOA is a new nature inspired meta heuristic algorithm inspired by how there is a hierarchy and departments in the ant hill colonization. It has high probabilistic potential in solving not only unconstrained but also constrained optimization problems. In this paper the AHCOA is applied to linear antenna array for better pattern synthesis in the following ways : By uniform excitation considering equal spacing of the antenna elements with respect to the uniform array. AHCOA is used in obtaining an array pattern to achieve minimum side lobe levels. The results are compared to other state of the art nature based algorithms such as ant lion optimizer, which show a considerable improvement in AHCOA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
