Parameter sets of the chosen algorithms.

Antennas play a crucial role in designing an efficient communication system. However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. Pattern synthesis in smart antennas is a major area of research because of its widespread application across various radar and communication systems. This paper presents an effective technique to minimize the SLL and thus improve the radiation pattern of the linear antenna array (LAA) using the chaotic inertia-weighted Wild Horse optimization (IERWHO) algorithm. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. In this paper, the method is applied for the first time in antenna array synthesis by optimizing parameters such as inter-element spacing and excitation to minimize the SLL while keeping other constraints within the boundary limits, while ensuring that the performance is not affected. For performance evaluation, the simulation tests include 12 benchmark test functions and 12 test functions to verify the effectiveness of the improvement strategies. According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization.天线在高效通信系统的设计中发挥着至关重要的作用。然而，降低波束方向图的最大旁瓣电平（SLL）是天线阵列研究中的一项关键挑战。由于智能天线在各类雷达与通信系统中应用广泛，其方向图综合技术已然成为研究热点。本文提出一种基于混沌惯性权重野生马优化（IERWHO）算法的有效技术，用于最小化线性天线阵列（LAA）的最大旁瓣电平，进而优化其辐射方向图。野生马优化器（WHO）是一种基于野生马群体社会行为的新型元启发式算法。所提IERWHO算法是对野生马优化（WHO）算法的改进版本，融合了混沌序列因子、非线性因子与惯性权重因子的思想。本文首次将该方法应用于天线阵列综合任务，通过优化阵元间距与激励幅度等参数，在保证其他约束条件处于边界范围内且不影响整体性能的前提下，实现最大旁瓣电平的最小化。为进行性能评估，本文采用12个基准测试函数与12个测试函数开展仿真实验，以验证改进策略的有效性。本文的实验结果令人振奋，所提出的IERWHO算法在优化领域具备可观的应用价值。