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用于多模谐振器拓扑设计的代理辅助差分进化算法

Surrogate-Assisted Differential Evolution for the Design of Multimode Resonator Topology.

作者信息

Stanovov Vladimir, Khodenkov Sergey, Gorbunov Sergey, Rozhnov Ivan, Kazakovtsev Lev

机构信息

Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk 660037, Russia.

School of Space and Information Technology, Siberian Federal University, Krasnoyarsk 660041, Russia.

出版信息

Sensors (Basel). 2024 Aug 5;24(15):5057. doi: 10.3390/s24155057.

DOI:10.3390/s24155057
PMID:39124103
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11314684/
Abstract

The microstrip devices based on multimode resonators represent a class of electromagnetic microwave devices, promising use in tropospheric communication, radar, and navigation systems. The design of wideband bandpass filters, diplexers, and multiplexers with required frequency-selective properties, i.e., bandpass filters, is a complex problem, as electrodynamic modeling is a time-consuming and computationally intensive process. Various planar microstrip resonator topologies can be developed, differing in their topology type, and the search for high-quality structures with unique frequency-selective properties is an important research direction. In this study, we propose an approach for performing an automated search for multimode resonators' conductor topology parameters using a combination of evolutionary computation approach and surrogate modeling. In particular, a variant of differential evolution optimizer is applied, and the model of the target function landscape is built using Gaussian processes. At every iteration of the algorithm, the model is used to search for new high-quality solutions. In addition, a general approach for target function formulation is presented and applied in the proposed approach. The experiments with two microwave filters have demonstrated that the proposed algorithm is capable of solving the problem of tuning two types of topologies, namely three-mode resonators and six-mode resonators, to the required parameters, and the application of surrogated-assisted algorithm has significantly improved overall performance.

摘要

基于多模谐振器的微带器件是一类电磁微波器件,有望应用于对流层通信、雷达和导航系统。设计具有所需频率选择性特性的宽带带通滤波器、双工器和多路复用器,即带通滤波器,是一个复杂的问题,因为电动力学建模是一个耗时且计算量大的过程。可以开发各种平面微带谐振器拓扑结构,它们的拓扑类型不同,寻找具有独特频率选择性特性的高质量结构是一个重要的研究方向。在本研究中,我们提出了一种结合进化计算方法和代理建模来自动搜索多模谐振器导体拓扑参数的方法。具体而言,应用了差分进化优化器的一个变体,并使用高斯过程构建目标函数景观模型。在算法的每次迭代中,该模型用于搜索新的高质量解决方案。此外,还提出了一种目标函数公式化的通用方法,并在所提出的方法中应用。对两个微波滤波器的实验表明,所提出的算法能够将两种拓扑结构,即三模谐振器和六模谐振器,调整到所需参数,并且代理辅助算法的应用显著提高了整体性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/206a9ec82870/sensors-24-05057-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/855dcfe329e8/sensors-24-05057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/a42e7f8896c4/sensors-24-05057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/2ee23f552f80/sensors-24-05057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/be0ee5e513da/sensors-24-05057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/206d1e1a92f6/sensors-24-05057-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/d3e115e14d65/sensors-24-05057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/af82faa70ef1/sensors-24-05057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/b66b1cc32d68/sensors-24-05057-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/6e1997f62744/sensors-24-05057-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/2ce727bc297b/sensors-24-05057-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/206a9ec82870/sensors-24-05057-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/855dcfe329e8/sensors-24-05057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/a42e7f8896c4/sensors-24-05057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/2ee23f552f80/sensors-24-05057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/be0ee5e513da/sensors-24-05057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/206d1e1a92f6/sensors-24-05057-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/d3e115e14d65/sensors-24-05057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/af82faa70ef1/sensors-24-05057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/b66b1cc32d68/sensors-24-05057-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/6e1997f62744/sensors-24-05057-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/2ce727bc297b/sensors-24-05057-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a89/11314684/206a9ec82870/sensors-24-05057-g011.jpg

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