Low-Cost Multi-Objective Optimization of Antennas Using Pareto Front Exploration and Response Features

被引:0
|
作者
Koziel, Slawomir [1 ,2 ]
Bekasiewicz, Adrian [1 ,2 ]
机构
[1] Reykjavik Univ, Sch Sci & Engn, Reykjavik, Iceland
[2] Gdansk Univ Technol, Fac Elect Telecommun & Informat, Gdansk, Poland
关键词
Antenna design; multi-objective optimization; surrogate modeling; feature-based optimization; DESIGN;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the paper, a procedure for low-cost multiobjective optimization of antenna structures is presented. Our approach is based on exploration of the Pareto front representing the best possible trade-offs between conflicting objectives, here, the structure size and its electrical performance. Starting from the design representing the best in-band reflection level, subsequent Pareto-optimal designs are identified through local constrained optimization aimed at reducing the structure size while maintaining the prescribed thresholds concerning the reflection response. For the sake of computational efficiency, variable-fidelity EM simulation models and feature-based optimization algorithm is utilized. The proposed methodology is demonstrated using a compact UWB monopole antenna.
引用
收藏
页码:571 / 572
页数:2
相关论文
共 50 条
  • [41] 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):
  • [42] 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
  • [43] Local Pareto approximation for multi-objective optimization
    Utyuzhnikov, Sergei
    Maginot, Jeremy
    Guenov, Marin
    ENGINEERING OPTIMIZATION, 2008, 40 (09) : 821 - 847
  • [44] 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
  • [45] Classifier ensembles for image identification using multi-objective Pareto features
    Albukhanajer, Wissam A.
    Jin, Yaochu
    Briffa, Johann A.
    NEUROCOMPUTING, 2017, 238 : 316 - 327
  • [46] Point-by-point Pareto front exploration and adjoint sensitivities for rapid multi-objective optimization of compact impedance matching transformers
    Koziel, Slawomir
    Bekasiewicz, Adrian
    INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS, 2018, 31 (05)
  • [47] 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
  • [48] Multi-fidelity EM simulations and constrained surrogate modelling for low-cost multi-objective design optimisation of antennas
    Koziel, Slawomir
    Sigurdsson, Ari T.
    IET MICROWAVES ANTENNAS & PROPAGATION, 2018, 12 (13) : 2025 - 2029
  • [49] 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 - +
  • [50] 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