Thin-Wire Antenna Design Using a Novel Branching Scheme and Genetic Algorithm Optimization

Smith, James Seale; Baginski, M.E.

doi:10.1109/tap.2019.2902960

Cited by 34 publications

(12 citation statements)

References 12 publications

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“…The antenna layout evolves in 136 iterations from a design with large amount of grayness and fuzzy boundaries to a final design with black and white materials as well as crisp boundaries. We stress that each pixel in this design (image) is a design variable, and that such large-scale optimization problem is computationally prohibitive to solve by stochastic optimization techniques, such as genetic algorithms [29,30]. Figure 3a shows the structure of the optimized antenna.…”

Section: Resultsmentioning

confidence: 99%

Compact Differential-Fed Planar Filtering Antennas

et al. 2019

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This paper proposes novel low-profile differential-fed planar antennas with embedded sharp frequency selectively. The antennas are compact and easy to integrate with differential devices without matching baluns. The antenna design is formulated as a topology optimization problem, where requirements on impedance bandwidth, directivity, and filtering are used as the design objectives. The optimized antennas operate over the frequency band 6.0–8.5 GHz. The antennas have reflection coefficients below −15 dB, cross-polarization levels below −42 dB, a maximum gain of 6.0 ± 0.5 dB, and a uniform directivity over more than 130° beamwidth angle in the frequency band of interest. In addition, the antennas exhibit sharp roll-off between the operational band and frequencies around the 5.8 GHz WiFi band and the 10 GHz X-band. One antenna has been fabricated with a good match between simulation and measurement results.

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Section: Resultsmentioning

confidence: 99%

Compact Differential-Fed Planar Filtering Antennas

et al. 2019

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“…Unfortunately, EM-driven design optimization in multidimensional parameter spaces is inevitably associated with massive EM simulations generating considerable CPU costs. This is the case even for local methods (gradient [8] or pattern search algorithms [9]), let alone global algorithms, nowadays extensively utilizing population-based metaheuristics (genetic algorithms [10], differential evolution [11], or particle swarm optimizers [12]).…”

Section: Introductionmentioning

confidence: 99%

Expedited Feature-Based Quasi-Global Optimization of Multi-Band Antenna Input Characteristics With Jacobian Variability Tracking

Koziel

Pietrenko‐Dabrowska

2020

IEEE Access

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Design of modern antennas relies-for reliability reasons-on full-wave electromagnetic simulation tools. In addition, increasingly stringent specifications pertaining to electrical and field performance, growing complexity of antenna topologies, along with the necessity for handling multiple objectives, make numerical optimization of antenna geometry parameters a highly recommended design procedure. Conventional algorithms, particularly global ones, entail often-unmanageable computational costs, so alternative approaches are needed. This work proposes a novel method for cost-efficient globalized design optimization of multi-band antennas incorporating the response feature technology into the trust-region framework. It allows for unequivocal allocation of the antenna resonances even for poor initial designs, where conventional local algorithms fail. Furthermore, the algorithm is accelerated by means of Jacobian variability tracking, which reduces the number of expensive finite-differentiation updates. Two real-world antenna design cases are used for demonstration purposes. The optimization cost is comparable to that of local routines while ensuring nearly global search capabilities. INDEX TERMS Antenna design, input characteristics, EM-driven design, trust-region methods, response features.

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“…The expenditures boost even further in the case of global optimization procedures. In fact, performing EM-driven antenna optimization with the use of the most popular population-based metaheuristics (particle swarm [20]- [25] or genetic algorithms [26], [27]) is usually very costly. As a consequence, an extensive research effort has been directed toward expediting the optimization procedures.…”

Section: Introductionmentioning

confidence: 99%