Multi-Objective Optimization of Compact UWB Impedance Matching Transformers Using Pareto Front Exploration and Adjoint Sensitivities

被引:0
|
作者
Koziel, Slawomir [1 ]
Bekasiewicz, Adrian [2 ]
Cheng, Qingsha S. [3 ]
机构
[1] Reykjavik Univ, Sch Sci & Engn, Reykjavik, Iceland
[2] Gdansk Univ Technol, Fac Elect Telecommun & Informat, Gdansk, Poland
[3] Southern Univ Sci & Tech, Dept Elect & Elect Engn, Shenzhen, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
computer-aided design; compact microwave circuits; impedance matching transformers; multi-objective optimization; Pareto front exploration; adjoint sensitivity; ANTENNA DESIGN; COUPLER; HYBRID;
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
In this paper, a technique for fast multi-objective optimization of impedance matching transformers has been presented. In our approach, a set of alternative designs that represent the best possible trade-offs between conflicting objectives (here, the maximum reflection level within a frequency band of interest and the circuit size) is identified by directly exploring the Pareto front. More specifically, the subsequent Pareto-optimal design is obtained by local optimization starting from the previously found solution. Low cost of the design optimization process is ensured by exploiting cheap adjoint sensitivities. The proposed technique is demonstrated using an example of three-section transformer matching the 50 Ohm source to the 130 Ohm load and working in 3.1-to-10.6 GHz range. For this example, 16-element representation of the Pareto set is obtained at the cost of just 60 evaluations of the full-wave EM simulation model of the transformer structure.
引用
收藏
页数:4
相关论文
共 50 条
  • [41] Optimization techniques for crisp and fuzzy multi-objective static inventory model with Pareto front
    Sahoo, Anuradha
    Panda, Minakshi
    OPSEARCH, 2024, 61 (04) : 2242 - 2284
  • [42] Distributed Multi-Objective GA for generating comprehensive Pareto front in deceptive optimization problems
    Ando, Shin
    Suzuki, Einoshin
    2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 2006, : 1554 - 1561
  • [43] An Adaptive Consensus Based Method for Multi-objective Optimization with Uniform Pareto Front Approximation
    Borghi, Giacomo
    Herty, Michael
    Pareschi, Lorenzo
    APPLIED MATHEMATICS AND OPTIMIZATION, 2023, 88 (02):
  • [44] DEEP CONVOLUTIONAL NEURAL NETWORKS FOR PARETO OPTIMAL FRONT OF MULTI-OBJECTIVE OPTIMIZATION PROBLEM
    Liu, Ruilin
    Zhang, Tao
    Chen, Fang
    JOURNAL OF NONLINEAR AND CONVEX ANALYSIS, 2022, 23 (04) : 833 - 846
  • [45] An Adaptive Consensus Based Method for Multi-objective Optimization with Uniform Pareto Front Approximation
    Giacomo Borghi
    Michael Herty
    Lorenzo Pareschi
    Applied Mathematics & Optimization, 2023, 88
  • [46] Learning to Balance Exploration and Exploitation in Pareto Local Search for Multi-objective Combinatorial Optimization
    Zhang, Haotian
    Shi, Jialong
    Sun, Jianyong
    Xu, Zongben
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 383 - 386
  • [47] A New Multi-swarm Multi-objective Particle Swarm Optimization Based on Pareto Front Set
    Sun, Yanxia
    van Wyk, Barend Jacobus
    Wang, Zenghui
    ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE, 2012, 6839 : 203 - +
  • [48] Multi-Criteria Decision Making - Pareto Front Optimization Strategy for Solving Multi-Objective Problems
    Kesireddy, Adarsh
    Carrillo, Luis Rodolfo Garcia
    Baca, Jose
    2020 IEEE 16TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION (ICCA), 2020, : 53 - 58
  • [49] Multi-objective pareto optimization of centrifugal pump using genetic algorithms
    Nariman-Zadeh, N.
    Amanifard, N.
    Hajiloo, A.
    Ghalandari, P.
    Hoseinpoor, B.
    PROCEEDING OF THE 11TH WSEAS INTERNATIONAL CONFERENCE ON COMPUTERS: COMPUTER SCIENCE AND TECHNOLOGY, VOL 4, 2007, : 135 - +
  • [50] Multi-Objective Optimization of a Transonic Compressor Rotor by Using an Adjoint Method
    Luo, Jiaqi
    Liu, Feng
    AIAA JOURNAL, 2015, 53 (03) : 797 - 801