In this paper, optimization of the linear array (LA) antenna is performed using modified social group optimization algorithm (SGOA). First step of the work involves in transforming the electromagnetic engineering problem to an optimization problem which is completely described in terms of objectives. Linear array synthesis is inherently considered as a multi-attribute problem. The pattern synthesis of LA is carried out with several objectives involving sidelobe level (SLL), beam-width (BW) and desired nulls. The SLL suppression with BW constraint is considered as first objective of this work and the results are compared with several evolutionary computing algorithms like ant lion (ALO), grey wolf (GWO) and root-runner (RRA). Following this, the MSGOA is further used to synthesise null patterns in which the pattern is completely described in terms of nulls with SLL and BW as constraints. The entire simulation-based experimentation is performed using Matlab® on i5 computing system.

中文翻译：

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使用改进的社会群体优化算法优化线性阵列

在本文中，线性阵列 (LA) 天线的优化是使用改进的社会群体优化算法 (SGOA) 执行的。工作的第一步涉及将电磁工程问题转化为一个优化问题，该问题完全按照目标进行描述。线阵合成本质上被认为是一个多属性问题。LA 的模式合成通过涉及旁瓣电平 (SLL)、波束宽度 (BW) 和所需零点的几个目标进行。具有 BW 约束的 SLL 抑制被认为是这项工作的第一个目标，并将结果与​​几种进化计算算法进行比较，如蚂蚁狮子 (ALO)、灰狼 (GWO) 和根跑者 (RRA)。按照此，MSGOA 进一步用于合成空模式，其中模式完全根据空值描述，SLL 和 BW 作为约束。整个基于仿真的实验是在 i5 计算系统上使用 Matlab® 进行的。