A Self-organizing Genetic Algorithm for UWB Microstrip Antenna Optimization Using a Machine Learning Technique

Martins, Sinara R.; Lins, Hertz Wilton de Castro; Silva, Cláudio R. M.

doi:10.1007/978-3-642-32639-4_77

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Interpolation combined with GA used for the design of an UWB ring monopole antenna was presented in References 186 and 187 where fitness function behaviors such as the BW, the RL, and the central frequency division (CFD) were estimated. After optimizing those parameters, comparison was held between a simulated antenna and a real prototype manufactured from the obtained values.…”

Section: Machine Learning‐assisted Antenna Optimizationmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

104

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This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

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Section: Machine Learning‐assisted Antenna Optimizationmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

104

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“…Sebagian besar desain dan optimasi antena berbasis algoritma genetika di atas lebih banyak digunakan untuk antena tipe pita terbatas atau tipe pita ganda. Untuk mendapatkan pita yang lebar, algoritma tersebut digabungkan atau digantikan dengan metode lain, seperti particle swarm optimization (PSO) dalam [6]- [9] atau machine learning (ML) dalam [10]. Selain itu, perangkat lunak yang digunakan untuk desain dan simulasi elektromagnetik menggunakan perangkat lunak komersial, seperti HFSS dalam [6], [8], [11] dan CST dalam [5], [9].…”

Section: Pendahuluanunclassified

“…Desain dan optimasi menggunakan AGMM sesuai untuk penerapan komunikasi pita lebar dengan dispersi minimum seperti dalam [16]. Penelitian ini menghasilkan metode baru dalam optimasi antena monopole pita lebar menggunakan algoritma genetika dan metode momen, selain dengan PSO [6]- [9] dan ML [10]. Algoritma genetika dalam metode AGMM digunakan untuk mendapatkan antena pita lebar dengan pendekatan fungsi objektif gabungan antena domain frekuensi dan domain waktu, yaitu 40% parameter S11, 20% frekuensi terendah dan dan 40% respons impuls yang dinyatakan dalam parameter fidelity.…”

Section: Pola Radiasi Antena Planar Monopole Bentuk Sembarangunclassified

Desain dan Optimasi Antena Pita Lebar Planar Monopole Bentuk Sembarang dengan Algoritma Genetika dan Metoda Momen

Suryana

2018

Jurnal Teknologi dan Sistem Komputer

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This paper presents a new approach in designing an ultra wideband minimum dispersion antenna optimally to avoid the degradation of broadband communications system performance. Design and iterative optimization are applied to an arbitrary shape of planar monopole antenna using a genetic algorithm combined with the moment method, abbreviated as AGMM method, and implemented with Matlab. Two arbitrary shapes of planar monopole antennas have been implemented in compact physical size using AGMM optimization, each having 9.1 GHz and 7.4 GHz bandwidths, the lowest frequency of 1.9 GHz and 2.7 GHz and fidelity 0.6 and 0.64 for any arbitrary discrete antenna and edge profile antenna. This method can be applied to design any arbitrary shapes of an ultra-wideband antenna with each has wide bandwidth more than 7 GHz, the lowest frequency below 3 GHz and a minimum fidelity of 0,55 that is suitable for high-speed communication, such as 5G system. AGMM yang masing-masing memiliki lebar pita 9,1 GHz dan 7,4 GHz, frekuensi terendah 1,9 GHz dan 2,7 GHz serta memiliki fidelity 0,6 dan 0,64 untuk antena diskrit sembarang dan antena profil tepi sembarang. Metode ini dapat diterapkan untuk merancang antena pita lebar bentuk sembarang dengan lebar pita lebih dari 7 GHz, frekuensi terendah < 3 GHz dan memiliki fidelity di atas 0,55 yang cocok untuk komunikasi berkecepatan tinggi, misalnya sistem 5G. Kata Kunci -antena pita lebar; dispersi minimum; algoritma genetika; metode momen; antena optimal Tersedia di https: //jtsiskom.undip.ac.id (30 April 2018) Abstrak -Makalah ini memaparkan pendekatan baru dalam perancangan optimal antena pita lebar dispersi minimum untuk menghindari penurunan kinerja sistem komunikasi kecepatan tinggi. Desain dan optimasi iteratif diterapkan pada antena planar monopole bentuk sembarang menggunakan algoritma genetika yang digabungkan dengan metode momen yang disingkat sebagai metode AGMM. Metode ini diimplementasikan dengan Matlab. Dua buah tipe antena planar monopole pita lebar bentuk sembarang dan ukuran fisik kompak berhasil dirancang dengan

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“…Os problemas de otimização são comuns em diversas áreas de aplicação do mundo real. Nesse contexto, várias técnicas bioinspiradas baseadas na inteligência de enxames têm sido aplicadas em diversas áreas como sistemas de potência (Yoshida et al, 2000;Duque, L. Oliveira & E. Oliveira, 2012), telecomunicações (Martins, Lins & Silva, 2012), roteamento de veículos (Bell & McMullen, 2004;Bianchi et al, 2006), planejamento de robôs (Ayala, Molin & Coelho, 2009;T. Arora, Gigras & V. Arora, 2014) e em outras áreas.…”

Section: Introductionunclassified

Um Breve Estudo Sobre Os Algoritmos Dos Vaga-Lumes E Dos Morcegos Para Otimização De Funções De Referência

Leite

Marcone

Paiva

2018

HOLOS

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Uma alternativa para resolver problemas complexos de otimização é usando algoritmos meta-heurísticos. As meta-heurísticas são métodos aproximados que oferecem soluções aceitáveis para problemas de otimização. Nos últimos anos, algumas meta-heurísticas inspiradas no comportamento de animais da natureza, em especial aqueles que vivem em grupos, vêm sendo bastante utilizadas. Este trabalho tem como objetivo apresentar um breve estudo do desempenho de duas meta-heurísticas conhecidas como Algoritmo dos Vaga-lumes e Algoritmo dos Morcegos. A comparação é realizada por meio de vários experimentos computacionais para otimizar funções de referência clássicas da literatura. Para isso, foram realizadas 30 execuções independentes, considerando diferentes dimensões e quantidade de avaliações da função objetivo. Após os experimentos, alguns dados estatísticos foram apresentados e eles evidenciaram a superioridade do Algoritmo dos Vaga-lumes quando comparado com o Algoritmo dos Morcegos.

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A Self-organizing Genetic Algorithm for UWB Microstrip Antenna Optimization Using a Machine Learning Technique

Cited by 5 publications

References 6 publications

A review on the design and optimization of antennas using machine learning algorithms and techniques

A review on the design and optimization of antennas using machine learning algorithms and techniques

Desain dan Optimasi Antena Pita Lebar Planar Monopole Bentuk Sembarang dengan Algoritma Genetika dan Metoda Momen

Um Breve Estudo Sobre Os Algoritmos Dos Vaga-Lumes E Dos Morcegos Para Otimização De Funções De Referência

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