Koziel Slawomir, Pietrenko-Dabrowska Anna
Engineering Optimization & Modeling Center, Reykjavik University, 102, Reykjavik, Iceland.
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, 80-233, Poland.
Sci Rep. 2024 Sep 28;14(1):22492. doi: 10.1038/s41598-024-73323-w.
Achieving compact size has emerged as a key consideration in modern microwave design. While structural miniaturization can be accomplished through judicious circuit architecture selection, precise parameter tuning is equally vital to minimize physical dimensions while meeting stringent performance requirements for electrical characteristics. Due to the intricate nature of compact structures, global optimization is recommended, yet hindered by the excessive expenses associated with system evaluation, typically conducted through electromagnetic (EM) simulation. This challenge is further compounded by the fact that size reduction is a constrained problem entailing expensive constraints. This paper introduces an innovative method for cost-effective explicit miniaturization of microwave components on a global scale. Our approach leverages response feature technology, formulating the optimization problem based on a set of characteristic points derived from EM-analyzed responses, combined with an implicit constraint handling approach. Both elements facilitate handling size reduction by transforming it into an unconstrained problem and regularizing the objective function. The core search engine employs a machine-learning framework with kriging-based surrogates refined using the predicted improvement in the objective function as the infill criterion. Our algorithm is demonstrated using two miniaturized couplers and is shown superior over several benchmark routines, encompassing both conventional (gradient-based) and population-based procedures, alongside a machine learning technique. The primary strengths of the proposed framework lie in its reliability, computational efficiency (with a typical optimization cost ranging from 100 to 150 EM circuit analyses), and straightforward setup.
实现紧凑尺寸已成为现代微波设计中的关键考量因素。虽然可以通过明智地选择电路架构来实现结构小型化，但精确的参数调整对于在满足严格电气特性性能要求的同时最小化物理尺寸同样至关重要。由于紧凑结构的复杂性，建议进行全局优化，但受到与系统评估相关的高昂成本的阻碍，系统评估通常通过电磁（EM）仿真进行。尺寸减小是一个带有昂贵约束的受限问题，这一事实进一步加剧了这一挑战。本文介绍了一种在全球范围内对微波组件进行经济高效的显式小型化的创新方法。我们的方法利用响应特征技术，基于从EM分析响应中导出的一组特征点来制定优化问题，并结合隐式约束处理方法。这两个要素都有助于通过将尺寸减小转化为无约束问题并正则化目标函数来处理尺寸减小问题。核心搜索引擎采用机器学习框架，使用基于克里金法的代理模型，并以目标函数的预测改进作为填充准则进行优化。我们的算法通过两个小型化耦合器进行了演示，并显示出优于几种基准例程，包括传统的（基于梯度的）和基于种群的程序，以及一种机器学习技术。所提出框架的主要优势在于其可靠性、计算效率（典型的优化成本范围为100至150次EM电路分析）和简单的设置。
