Optimal components selection for active filter design with average differential evolution algorithm

Durmuş, Burhanettin

doi:10.1016/j.aeue.2018.07.021

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…In the mutation, each individual in the population known as the target vector Xi is utilized to produce a mutant vector, Vi. The DE/rand/1 mutation strategy is used in this work as (5):…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…Hence, these problems can be formulated as optimization problems, which optimization algorithms can fix. Metaheuristic methods have been suggested in the literature to tackle challenging problems in diverse domains [2], such as genetic algorithm (GA) [3], differential evolution (DE) [4], [5], particle swarm optimization (PSO) [6], ant colony optimization (ACO) [7]- [9], and artificial bee colony (ABC) [10]- [12]. Nevertheless, many researchers use hybrid metaheuristic methods to overcome many optimization issues.…”

Section: Introductionmentioning

confidence: 99%

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An optimal design of current conveyors using a hybrid-based metaheuristic algorithm

Abi

Benhala

2022

IJECE

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<span lang="EN-US">This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.</span>

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“…In the mutation, each individual in the population known as the target vector Xi is utilized to produce a mutant vector, Vi. The DE/rand/1 mutation strategy is used in this work as (5):…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An optimal design of current conveyors using a hybrid-based metaheuristic algorithm

Abi

Benhala

2022

IJECE

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“…Las mismas permiten obtener filtros que cumplen con las especificaciones con errores de diseño considerablemente inferiores a los obtenidos con métodos tradicionales. Son numerosos los EAs propuestos para dimensionar filtros, entre otros: algoritmos genéticos, optimización por enjambre de partículas, evolución diferencial, vortex seach y tabu search (Doʇan & Ölmez, 2015;Durmuş, 2018;Lovay et al, 2019;Vural et al, 2012).…”

Section: Conclusionesunclassified

Jornadas de Ciencia y Tecnología 2022

UTN¹

2022

AJEA

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Las Jornadas de Ciencia y Tecnología 2022 organizadas por la Facultad Regional San Francisco de la Universidad Tecnológica Nacional buscan, a través de la presentación de investigaciones en marcha y de conferencias seleccionadas, estrechar lazos entre los distintos grupos de investigación de esta y otras Facultades que estén relacionados con diferentes carreras, entre otras, ingeniería Química, Industrial, en Sistemas de información, Electromecánica, Electrónica, otras ingenierías y la Licenciatura en Administración Rural.En este sentido, se propone la comunicación y el intercambio de experiencias entre áreas altamente especializadas, que permitan resolver problemas y generar nuevas alternativas tecnológicas a partir de enfoques interdisciplinarios. Además, las Jornadas tienen como objetivo propiciar el contacto entre los distintos grupos anteriormente mencionados y la industria, con la difusión de los adelantos científicos y tecnológicos producidos en el mundo y la discusión de las investigaciones desarrolladas en Argentina. En las Jornadas de Ciencia y Tecnología 2022, se desarrollaron conferencias plenarias de expertos que abarcaron diversos tópicos sobre una amplia gama de disciplinas de interés para la tecnología y los procesos industriales.Estas Jornadas, que se vienen desarrollando desde 2002, constituyen un espacio de abordaje de contenidos de ciencia y tecnología para estudiantes, docentes e investigadores de la Facultad en una visión conjunta e integrada con la industria. En la edición 2022, se llevaron a cabo de manera presencial y con algunas actividades en modalidad híbrida.En esta edición, los trabajos de investigación se presentaron en formato de trabajo completo con referato y su compilado resulta en un libro digital que cumple con las normativas de AJEA y que cuenta con DOI e ISBN. AutoridadesUniversidad Tecnológica Nacional – República ArgentinaRectorIng. Ruben Soro VicerectorIng. Haroldo Avetta Facultad Regional San Francisco – UTNDecanoIng. Esp. Alberto Toloza VicedecanaIng. Claudia Mariela Verino Subsecretaría GeneralIng. Carolina Inés Apendino Secretario AcadémicoMg. Ing. Juan Carlos Calloni Secretario de Extensión y Cultura UniversitariaIng. Alejandro Raúl Trossero Secretario de Ciencia y TecnologíaDr. Ing. Diego Martín Ferreyra Secretario Administrativo Ing. César Daniel Mina Secretario de Asuntos Estudiantiles Ing. Paulo Jesús Gianoglio Secretario de Vinculación Tecnológica Dr. Lic. Oscar Miguel Rete Secretaria de Posgrado Ing. Claudia Mariela Verino Subsecretario de Vinculación Institucional Tec. Ezequiel Alejandro Fusero Subsecretaria de Planeamiento Académico Lic. Esp. Vanina Guadalupe Fraire Directora Académica Tec. Analía Fabiana Armando Directora Administrativa Lic. Patricia Marchetti Directora de Recursos Humanos Tec. María Angélica Navarro Director de Mantenimiento y Servicios Generales Tec. Froilán Juan Bautista Giménez Directora de Dpto de Ing. Química Dra. Ing. Paula Carolina Garnero Director de Dpto de Ing. en Sistemas de Información Ing. Esp. Gabriel Rubén Cerutti Director de Dpto de Ing Electromecánica Ing. Esp. Ernesto Galiano Director de Dpto de Ing. Electrónica Mag. Ing. Gastón Carlos Peretti Director de Dpto de Lic. en Administración Rural Cdor. Daniel Roberto Ricci Director de Dpto de Materias Básicas Ing. Esp. Gustavo Yoaquino Coordinadora de Ing. Industrial Ing. Carolina Inés Apendino Coordinador de Tec. Universitaria en Programación Mag. Ing. Javier Daniel Saldarini Miembros del Comité OrganizadorDiego Martín FerreyraClaudia del Valle MercolLucía SobreroAylen Ileana GimenezMiembros del Comité CientíficoEsp. Ing. Ernesto GalianoDra. Paula GarneroIng. Gustavo YoaquinoMg. Gastón PerettiCr. Daniel RicciIng. Gabriel CeruttiIng, Walter ToniniDra. Mariana BerrnardDra. Verónica NicolauDra. María Eugenia TavernaIng. Raúl MarlattoDr. Matías RaspoIng. Hugo PipinoDr. Javier RedolfiEsp. Ing. Juan Pablo FerreyraIng. Micaela MulassanoIng. Javier VignoloCra. Maricel RovasioIng. Carolina ApendinoEsp. Lic. Vanina FraireIng. Franco NovaraDra. María Belén VignolaDra. Carolina AloisioDra. Vanina GunteroDr. Diego Martín Ferreyra

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“…ADE algoritması, diğer evrimsel hesaplama algoritmaları gibi çözüm geliştirme sürecinde sezgisel ve rassal değişimleri ve seçimleri kullanan, popülasyon tabanlı yeni önerilmiş bir sezgisel algoritmadır [17]. Hızlı bir yakınsama davranışı gösteren bu sezgisel algoritma, problemlerinin çözümünde kayda değer sonuçlar üretmektedir.…”

Section: Ade Algoritmasıunclassified

“…Bundan dolayı bu çalışmada sezgisel hesaplama tabanlı bilineer modelleme konusuna odaklanılmıştır. ADE algoritması, yeni önerilen sezgisel algoritmalardan biri olup hızlı yakınsama oranına sahip bir optimizasyon aracı olarak sunulmaktadır [17]. Popülasyondaki bireylerin ortalama değerleri üzerinden yeni birey üretiminin gerçekleştirildiği bu sezgisel algoritmada, kümülatif bir çözüm gelişimi sağlanmaktadır.…”

Section: Introductionunclassified

Ortalama farksal gelişim algoritması ile bilineer sistem kimliklendirme

Durmuş¹

2018

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Bu çalışmada, bilineer sistem kimliklendirme problemi için ortalama farksal gelişim (average differential evolution-ADE) algoritması önerilmiştir. Doğrusal olmayan sisteme ait parametrelerin ADE tabanlı bilineer model üzerinden kestirimi gerçekleştirilmiştir. Bilinmeyen sistem çıkışı ile bilineer model çıkışı arasındaki Ortalama Karesel Hata (Mean Square Error, MSE) performans ölçütü olarak kullanılmıştır. Önerilen algoritmanın performansı, hem farklı sezgisel algoritmaların kullanıldığı benzetim çalışmaları ile hem de literatürde rapor edilmiş diğer metotlar ile karşılaştırılmıştır. Karşılaştırmalı sonuçlarda, ADE tabanlı modelleme ile hata değerlerinin azaldığı ve hesaplanan parametre değerlerinin doğruluk oranının arttığı görülmüştür. Hızlı bir yakınsama ile global çözüme ulaşma kabiliyetine sahip olan ADE algoritması, parametre kestirimi uygulamaları için etkin bir araç olarak kullanılabilir.

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Optimal components selection for active filter design with average differential evolution algorithm

Cited by 18 publications

References 20 publications

An optimal design of current conveyors using a hybrid-based metaheuristic algorithm

An optimal design of current conveyors using a hybrid-based metaheuristic algorithm

Jornadas de Ciencia y Tecnología 2022

Ortalama farksal gelişim algoritması ile bilineer sistem kimliklendirme

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