论文标题
蚂蚁山殖民优化算法（AHCOA）用于控制均匀线性阵列的侧叶
Ant Hill Colonization optimization algorithm(AHCOA) for controlling the side lobe of a uniform linear array
论文作者
论文摘要
本文旨在将蚂蚁山殖民化优化算法（AHCOA）引入电磁和天线社区。蚂蚁山是由特殊物种的蚂蚁建造的，称为福兰蚂蚁（也是草地蚂蚁，消防蚂蚁和收割者蚂蚁）。 Ahcoa是一种新颖的新自然启发算法，模仿了蚂蚁如何建造和维持蚂蚁山的生存和寄托多年。这个问题解决了在不同领域的受限和不受约束的优化问题。通过编写对蚂蚁山模具的体积分析的方程来使用AHCOA使用结构的构建方式。在本文中，我们已经展示了AHCOA比以前关于蚂蚁狮型优化器的论文更好，该论文在纸张中控制天线模式合成中的侧叶[1]。还说明了AHCOA在合成和分析D/ D/分析中的潜力从1.1,0.6,0.5,0.3和0.1线性阵列变化。天线侧叶水平的最小化与蚂蚁狮子优化器进行了比较，显示了为什么AHCOA优于先前模拟的蚂蚁狮子优化器用于侧叶控制。结果表明，为什么将线性阵列更好地用于AHCOA，而不是平面阵列中使用的其他算法。本文说明了为什么AHCOA是线性阵列中使用的天线优化的强大候选者。
This paper aims to introduce the Ant hill colonization optimization algorithm(AHCOA) to the electromagnetics and antenna community. The ant hill is built by special species of ants known as formicas ants(also meadow ants, fire ants and harvester ants). AHCOA is a novel new nature inspired algorithm mimicking how the ants built and sustain the ant hill for their survival and sustenance for many years. This problem solves constrained and unconstrained optimization problems with wide capability in diverse fields. AHCOA is used by writing equations of volumetric analysis of the ant hill mould the manner in which the structure is architected. In this paper, we have shown how AHCOA is better than the previous paper on ant lion optimizer for controlling side lobe in antenna pattern synthesis in paper [1]. The potential of AHCOA in synthesizing and analyzing for d/ varying from 1.1,0.6,0.5,0.3 and 0.1 linear array is also illustrated. Antenna side lobe level minimization is compared with ant lion optimizer showing why AHCOA is better than the previously simulated ant lion optimizer for side lobe control. The results show why linear arrays are better synthesized for AHCOA then other algorithms used in planar arrays. This paper shows why AHCOA is a strong candidate for antenna optimization used in linear arrays.
