Electromagnetic simulation tools have been playing an increasing role in the design of contemporary antenna structures. The employment of electromagnetic analysis ensures reliability of evaluating antenna characteristics but also incurs considerable computational expenses whenever massive simulations are involved (e.g., parametric optimization, uncertainty quantification). This high cost is the most serious bottleneck of simulation-driven design procedures, and may be troublesome even for local tuning of geometry parameters, let alone global optimization. On the one hand, globalized search is often necessary because the design problem might be multimodal (i.e., the objective function features multiple local optima) or a reasonably good initial design may not be available. On the other hand, the computational efficiency of popular algorithmic approaches, primarily, nature-inspired population-based algorithms, is generally poor. Combining metaheuristics procedures with surrogate modelling techniques and sequential sampling methods alleviates the problem to a certain extent but modelling of nonlinear antenna responses over broad frequency ranges is extremely challenging, and the aforementioned solutions are normally limited to rather simple structures described by a few parameters. This paper proposes a novel approach to global optimization of multi-band antennas. The major component of the presented framework is the knowledge-based inverse surrogate constructed at the level of response features (e.g., frequency and level locations of the antenna resonances). The surrogate facilitates decision-making process of inexpensive identification of the most promising regions of the parameter space, and a rendition of the good-quality initial design for further local tuning. Our methodology is validated using three examples of dual- and triple-band antennas. The average optimization cost is only 150 full-wave antenna analyses while ensuring precise allocation of the antenna resonances at the target frequencies. This performance is demonstrated superior over both local optimizers and population-based metaheuristics.

电磁仿真工具在当代天线结构的设计中发挥着越来越大的作用。电磁分析的使用确保了评估天线特性的可靠性，但在涉及大量模拟（例如，参数优化、不确定性量化）时也会产生相当大的计算费用。这种高成本是模拟驱动设计程序最严重的瓶颈，甚至对于几何参数的局部调整也可能很麻烦，更不用说全局优化了。一方面，全球化搜索通常是必要的，因为设计问题可能是多模态的（即，目标函数具有多个局部最优），或者可能无法获得相当好的初始设计。另一方面，流行的算法方法的计算效率，主要是受自然启发的基于种群的算法，通常很差。将元启发式程序与代理建模技术和顺序采样方法相结合，在一定程度上缓解了这个问题，但在宽频率范围内对非线性天线响应进行建模极具挑战性，并且上述解决方案通常仅限于由几个参数描述的相当简单的结构。本文提出了一种多频段天线全局优化的新方法。所提出框架的主要组成部分是在响应特征级别（例如，天线谐振的频率和级别位置）构建的基于知识的逆代理。代理促进了参数空间中最有希望的区域的廉价识别的决策过程，以及用于进一步局部调整的高质量初始设计的再现。我们的方法使用双频和三频天线的三个示例进行了验证。平均优化成本仅为 150 次全波天线分析，同时确保在目标频率上精确分配天线谐振。这种性能被证明优于本地优化器和基于群体的元启发式算法。平均优化成本仅为 150 次全波天线分析，同时确保在目标频率上精确分配天线谐振。这种性能被证明优于本地优化器和基于群体的元启发式算法。平均优化成本仅为 150 次全波天线分析，同时确保在目标频率上精确分配天线谐振。这种性能被证明优于本地优化器和基于群体的元启发式算法。