Differential Evolutionary Optimization of an Equivalent Dipole Model for Electromagnetic Emission Analysis

Zhao, Wei‐Jiang; Liu, En‐Xiao; Wang, Binfang; Gao, Si‐Ping; Png, Ching Eng

doi:10.1109/temc.2018.2797265

Cited by 36 publications

(20 citation statements)

References 18 publications

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“…Given the aforementioned issues, it is clear that conventional, yet still widespread EM-driven design methods (primarily those relying on parametric studies) are grossly incapable of handling complex design scenarios, particularly those involving multiple objectives. Numerical optimization procedures are much more suitable for this purpose [20]; yet, the vast majority of available algorithms, both conventional (gradient-based methods [21], pattern search [22]), and nature-inspired (evolutionary algorithms [23], differential evolution [24], particle swarm optimizers [25], firefly algorithm [26]) can only process scalar objective functions. For practical convenience, multi-objective tasks are often reformulated into single-objective ones using, e.g., weighted sum method [27] or goal attainment approach [28], as well as objective prioritization by selecting a primary goal and turning the others into constraints [29].…”

Section: Introductionmentioning

confidence: 99%

Rapid Multi-Criterial Antenna Optimization by Means of Pareto Front Triangulation and Interpolative Design Predictors

Koziel

Pietrenko‐Dabrowska²

2021

IEEE Access

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Modern antenna systems are designed to meet stringent performance requirements pertinent to both their electrical and field properties. The objectives typically stay in conflict with each other. As the simultaneous improvement of all performance parameters is rarely possible, compromise solutions have to be sought. The most comprehensive information about available design trade-offs can be obtained through multiobjective optimization (MO), typically in the form of a Pareto set. Notwithstanding, MO is a numerically challenging task, in a large part due to high CPU cost of evaluating the antenna properties, normally carried out through full-wave electromagnetic (EM) analysis. Surrogate-assisted procedures can mitigate the cost issue to a certain extent but construction of reliable metamodels is hindered by the curse of dimensionality, and often highly nonlinear antenna characteristics. This work proposes an alternative approach to MO of antennas. The major contribution of our work consists in establishing a deterministic machine learning procedure, which involves sequential generation of Pareto-optimal designs based on the knowledge gathered so far in the process (specifically, by triangulation of the already obtained Pareto set), and local surrogate-assisted refinement procedures. Our methodology allows for rendering uniformly-distributed Pareto designs at the cost of a few hundreds of antenna EM simulations, as demonstrated by means of three verification case studies. Benchmarking against state-of-the-art MO techniques is provided as well. INDEX TERMS Antenna optimization; EM-driven design; multi-criterial design; Pareto front triangulation; surrogate modeling.

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Section: Introductionmentioning

confidence: 99%

Rapid Multi-Criterial Antenna Optimization by Means of Pareto Front Triangulation and Interpolative Design Predictors

Koziel

Pietrenko‐Dabrowska²

2021

IEEE Access

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“…This can only be realized through rigorous numerical optimization [18]. However, most of widely used algorithms, including conventional methods (e.g., gradient-based [19] or pattern search [20]) or computational-intelligence-based techniques (e.g., evolutionary algorithms [21], particle swarm optimizers [22], or differential evolution [23]) are single-objective routines that can only process scalar cost functions. Controlling several objectives requires either aggregation (e.g., by means of a weighted sum method [24]), or turning most of the objectives (typically, all but one) into constraints and assigning appropriate acceptance levels.…”

Section: Introductionmentioning

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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“…Sources of radiated emission from an integrated circuit (IC) can be modeled by equivalent dipole arrays with near‐field scan data . The source modeling method is very effective for estimating radio frequency interference problems.…”

Section: Introductionmentioning

confidence: 99%

“…Sources of radiated emission from an integrated circuit (IC) can be modeled by equivalent dipole arrays with nearfield scan data. [1][2][3][4] The source modeling method is very effective for estimating radio frequency interference problems. Once the equivalent dipole sources of an actual complicated IC are found, they can be imported into a full-wave simulation tool to estimate the near fields in the presence of additional structures in a real system environment, such as a heatsink, 1 antenna 2,3 shielding box, or dielectric layer.…”

Section: Introductionmentioning

confidence: 99%

“…Equivalent dipole array sources are commonly extracted from simulated or measured near fields at a scanning plane above a certain radiation source. [1][2][3][4] This paper examines the accuracy of the dipole source in estimating the fields at other evaluation planes in the presence of additional structures. Only deterministic factors, rather than the random measurement noises, are discussed here.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy investigation of equivalent dipole arrays for near‐field estimation in presence of shielding or dielectric structures

Kwak

Bae

Hong

et al. 2019

Micro & Optical Tech Letters

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The accuracy of equivalent dipole arrays for source modeling of electromagnetic fields is affected by several factors. In the step for extracting equivalent dipole arrays for a given source, there are three factors that can affect accuracy: scanning height, source spacing, and scanning gap. In the evaluation step of reconstructed fields, there are two other factors that can also affect accuracy: evaluation height and additional structures. In this paper, simple guidelines are suggested considering the three factors in extracting equivalent dipole arrays. Also, the effects of the evaluation height on accuracy of reconstructed fields are demonstrated in the presence of shielding or dielectric structures. A feature selective validation process was utilized to evaluate similarities between actual and estimated fields.

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Differential Evolutionary Optimization of an Equivalent Dipole Model for Electromagnetic Emission Analysis

Cited by 36 publications

References 18 publications

Rapid Multi-Criterial Antenna Optimization by Means of Pareto Front Triangulation and Interpolative Design Predictors

Rapid Multi-Criterial Antenna Optimization by Means of Pareto Front Triangulation and Interpolative Design Predictors

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

Accuracy investigation of equivalent dipole arrays for near‐field estimation in presence of shielding or dielectric structures

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