A Kriging-Assisted Light Beam Search Method for Multi-Objective Electromagnetic Inverse Problems

An, Siguang; Yang, Shiyou; Mohammed, Osama A.

doi:10.1109/tmag.2017.2748560

Cited by 22 publications

(11 citation statements)

References 10 publications

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“…The popular modeling methods utilized in this context include response surfaces Kriging interpolation [137]- [141], Gaussian process regression [142], [143], artificial neural networks [144], [145], or combination of various techniques [146]. The application areas range from the design of microwave devices [142], and antennas [139], [146], through optimization of radar absorbers [141], to electromagnetic machine optimization [140], [144]. MO design combined with tolerance analysis is also considered [144], [146].…”

Section: Simulation-driven Surrogate-assisted Multi-objective Desmentioning

confidence: 99%

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

2021

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Section: Simulation-driven Surrogate-assisted Multi-objective Desmentioning

confidence: 99%

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

2021

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“…Perhaps the most promising approach to mitigating this issue is surrogate-assisted approach [45], [51]- [53] (see also Section I), where the computational burden is shifted towards a faster representation of the antenna at hand, the surrogate model Rs. Typically, it is a data-driven model, for example kriging [60], Gaussian process regression (GPR) [61], or neural network [45]. Because the evaluation cost of the surrogate is negligible as compared to EM analysis, Rs can be optimized directly using, for example, multi-objective evolutionary algorithms [62] or MO version of any of popular metaheuristics.…”

Section: A Surrogate-based Multi-objective Design: Generic Proceduresmentioning

confidence: 99%

Fast Multi-Objective Optimization of Antenna Structures by Means of Data-Driven Surrogates and Dimensionality Reduction

Koziel

Pietrenko‐Dabrowska

2020

IEEE Access

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Design of contemporary antenna structures needs to account for several and often conflicting objectives. These are pertinent to both electrical and field properties of the antenna but also its geometry (e.g., footprint minimization). For practical reasons, especially to facilitate efficient optimization, single-objective formulations are most often employed, through either a priori preference articulation, objective aggregation, or casting all but one (primary) objective into constraints. Notwithstanding, the knowledge of the best possible design trade-offs provides a more comprehensive insight into the properties of the antenna structure at hand. Genuine multi-objective optimization is a proper way of acquiring such data, typically rendered in the form of a Pareto set that represents the mentioned trade-off solutions. In antenna design, the fundamental challenge is high computational cost of multi-objective optimization, normally carried out using populationbased metaheuristic algorithms. In most practical cases, the use of reliable, yet costly, full-wave electromagnetic models is imperative to ensure evaluation reliability, which makes conventional multiobjective optimization procedures prohibitively expensive. The employment of fast surrogates (or metamodels) can alleviate these difficulties, yet, construction of metamodels faces considerable challenges by itself, mostly related to the curse of dimensionality. This work proposes a novel surrogate-assisted approach to multi-objective optimization, where the data-driven model is only rendered in a small region spanned by the selected principal components of the extreme Pareto-optimal design set obtained using local search routines. The region is limited in terms of parameter ranges but also dimensionality, yet contains the majority of Pareto front, therefore surrogate construction therein does not incur considerable costs. The typical cost of identifying the Pareto set is just a few hundred of full-wave analyses of the antenna under design. Our technique is validated using two antenna examples (a planar Yagi and an ultra-wideband monopole antenna) and favorably compared to state-of-the-art surrogate-assisted multi-objective optimization methods. INDEX TERMS Antenna design; multi-objective optimization; surrogate modeling; domain confinement; principal components; dimensionality reduction.

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“…Most often R s is constructed as a data-driven surrogate. The widely used methods include kriging interpolation, 57 radial-basis functions, 58 Gaussian process regression, 59 or neural networks. 48 It should be mentioned that several surrogate-based MO techniques have been proposed recently that do not involve population-based method to generate the Pareto set (e.g., References 51,52).…”

Section: Multi-objective Design Using Surrogate Modelsmentioning

confidence: 99%

Low‐cost multi‐criteria design optimization of compact microwave passives using constrained surrogates and dimensionality reduction

Koziel

Pietrenko‐Dabrowska

Al‐Hasan

2020

Int J Numerical Modelling

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Design of contemporary microwave circuits is a challenging task. Typically, it has to take into account several performance requirements and constraints.The design objectives are often conflicting and their simultaneous improvement may not be possible; instead, compromise solutions are to be sought. Representative examples are miniaturized microwave passives where reduction of the circuit size has a detrimental effect on its electrical characteristics. Acquiring information about the best possible design trade-offs is invaluable for the designer, yet it entails computationally expensive multi-objective optimization (MO). MO is typically conducted using population-based metaheuristic algorithms, the cost of which might be extremely high. If the circuit performance is evaluated using full-wave electromagnetic (EM) analysis, this cost is often prohibitive. A workaround is the employment of fast surrogate models, and a number of surrogate-assisted frameworks have been proposed in the literature. Unfortunately, a construction of reliable surrogates is hindered in higher dimensional parameter spaces. The recently proposed constrained modeling mitigates this issue to a certain extent by restricting the modeling process to the region containing the Pareto front to be found. This work proposes a novel surrogate-based MO technique that involves constrained modeling and explicit reduction of the surrogate domain dimensionality. The latter is achieved through the spectral analysis of the extreme Pareto-optimal design set obtained by local search routines. Our methodology is validated using a 15-parameter impedance-matching transformer with the Pareto set identified at the cost of a few hundred EM analyses of the circuit. The numerical experiments also demonstrate a significant reduction of the optimization cost as compared to the state-of-the-art surrogate-assisted MO methods.

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A Kriging-Assisted Light Beam Search Method for Multi-Objective Electromagnetic Inverse Problems

Cited by 22 publications

References 10 publications

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

Fast Multi-Objective Optimization of Antenna Structures by Means of Data-Driven Surrogates and Dimensionality Reduction

Low‐cost multi‐criteria design optimization of compact microwave passives using constrained surrogates and dimensionality reduction

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