Multiobjective genetic algorithm approach for a dual-feed circular polarised patch antenna design

Aljibouri, B.; Lim, Eng Gee; Evans, H.

doi:10.1049/el:20000766

Cited by 23 publications

(7 citation statements)

References 4 publications

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“…Computational speedup can be obtained using surrogate-assisted techniques [7,43,44], where the optimization burden is shifted into the cheaper representation of the structure of interest. In some situations, typically for simple components, development of analytical models is possible [45,46], in others (particularly for structures such as microwave filters, couplers or power splitters), equivalent circuit models can be used [1], [30]. Both types of models are very fast.…”

Section: Variable-fidelity Em Simulation Modelsmentioning

confidence: 99%

Multi-objective optimization of expensive electromagnetic simulation models

Koziel

Bekasiewicz

2016

Applied Soft Computing

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Section: Variable-fidelity Em Simulation Modelsmentioning

confidence: 99%

Multi-objective optimization of expensive electromagnetic simulation models

Koziel

Bekasiewicz

2016

Applied Soft Computing

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“…They are usually utilized at ultra-high frequency (UHF) and higher frequencies because the size of the antenna is directly tied to the wavelength at the resonant frequency. It had been generally applied in wireless communication system [2], [3].…”

Section: Microstrip Antennamentioning

confidence: 99%

Design and Analysis of Equilateral Triangular Single Patch Antenna with Side Length 8cm

Song

Chin

Bong

et al. 2006

2006 International RF and Microwave Conference

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“…Normally, its goal is to produce a set of alternative designs representing the best possible trade‐offs between conflicting objectives . A quick look into the literature reveals that the most popular class of multi‐objective optimization algorithms are population‐based metaheuristics such as genetic algorithms and particle‐swarm optimizers . Their primary advantage is an ability to find the entire representation of the Pareto set in a single algorithm run.…”

Section: Introductionmentioning

confidence: 99%

Point‐by‐point Pareto front exploration and adjoint sensitivities for rapid multi‐objective optimization of compact impedance matching transformers

Koziel

Bekasiewicz

2018

Int J Numerical Modelling

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This paper proposes a technique for rapid multi‐objective optimization of miniaturized impedance matching transformers. The key component of our methodology is direct exploration of the Pareto front in the search for 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). In particular, starting from the design corresponding to the best electrical performance of the circuit, the subsequent Pareto‐optimal designs are obtained by a series of local optimizations with respect to one of the objectives, using the previously found solution as a starting point. The optimization is constrained not to degrade the other objective beyond a certain threshold (determining the spread of the solutions along the Pareto front). Low cost of the design process is ensured by exploiting cheap adjoint sensitivities. Our technique is demonstrated using an example of a compact 3‐section impedance transformer matching the 50‐Ohm source to the 130‐Ohm load and working in 3.1‐to‐10.6 GHz and 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.

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Multiobjective genetic algorithm approach for a dual-feed circular polarised patch antenna design

Cited by 23 publications

References 4 publications

Multi-objective optimization of expensive electromagnetic simulation models

Multi-objective optimization of expensive electromagnetic simulation models

Design and Analysis of Equilateral Triangular Single Patch Antenna with Side Length 8cm

Point‐by‐point Pareto front exploration and adjoint sensitivities for rapid multi‐objective optimization of compact impedance matching transformers

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