A New Metaheuristic Optimization Algorithm, the Weighted Attraction Method

Friedl, G.; Kuczmann, Miklós

doi:10.14513/actatechjaur.v8.n3.381

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…In this paper, a newly developed method is applied to find an approximation of the Jiles-Atherton model parameter set, the weighted attraction method [11].The main goal was to develop a new algorithm that is capable of global optimization using less number of individuals without increasing the runtime. The idea behind this method is the gravitational attraction between particles.…”

Section: Metaheuristic Optimization and The Weighted Attraction Methodsmentioning

confidence: 99%

Identification of scalar Jiles-Atherton hysteresis model parameters with a new particle behaviour based metaheuristic algorithm

Friedl

Kuczmann

2015

ELECTROTECHNICAL REVIEW

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This paper is about a possible determination process of the scalar Jiles-Atherton hysteresis model parameters. A newly developed particle behaviour based metaheuristic algorithm, the so-called Weighted Attraction Method is used to find an approximation to the model parameters. The paper shortly discuss phenomena of magnetic hysteresis, the equations of the Jiles-Atherton hysteresis model, and the applied optimization method, thereafter the identification process, and the effectiveness of the optimization process is presented. Streszczenie. W artykule opisano możliwy proces wyznaczania parametrów skalarnego modelu histerezy Jilesa-Athertona. Nowo opracowany model opiera się na algorytmie metaheurystycznym. Metoda Weighted Attraction Method jest używana do wyznaczenia przybliżonych parametrów modelu. W artykule krótko omówiono zjawisko histerezy magnetycznej, równania modelu histerezy Jilesa-Athertona i zastosowanej metody optymalizacji, a następnie zaprezentowano proces identyfikacji i skuteczności procesu optymalizacji. (Identyfikacja parametrów skalarnego modelu histerezy Jilesa-Athertona w oparciu o algorytm metaheurystyczny).

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Section: Metaheuristic Optimization and The Weighted Attraction Methodsmentioning

confidence: 99%

Identification of scalar Jiles-Atherton hysteresis model parameters with a new particle behaviour based metaheuristic algorithm

Friedl

Kuczmann

2015

ELECTROTECHNICAL REVIEW

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“…in Necati Ozisik & Orlande (2000). In contrast, SOA (Tamura & Yasuda, 2011), VS (Dogan & Olmez, 2015), and WAM (Friedl & Kuczmann, 2015) are considered global optimization metaheuristic algorithms that try to mimic natural phenomena such as pressure fronts, vortex pattern created by the vertical flow of the stirred fluids, and gravitational attraction between particles in order to solve optimization problems. It is important to emphasize that one can distinguish between the global optimization from the local one, because the first one focuses on finding the extreme of a function in the whole search space (function´s domain) (Kvasov & Mukhametzhanov, 2017;Sergeyev & Kvasov, 2017).…”

Section: Algorithm Fundamentalsmentioning

confidence: 99%

Internal volumetric heat generation and heat capacity prediction during a material electromagnetic treatment process using hybrid algorithms

2018

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This work considers the estimation of internal volumetric heat generation, as well as the heat capacity of a solid spherical sample, heated by a homogeneous, time-varying electromagnetic field. To that end, the numerical strategy solves the corresponding inverse problem. Three functional forms (linear, sinusoidal, and exponential) for the electromagnetic field were considered. White Gaussian noise was incorporated into the theoretical temperature profile (i.e. the solution of the direct problem) to simulate a more realistic situation. Temperature was pretended to be read through four sensors. The inverse problem was solved through three different kinds of approach: using a traditional optimizer, using modern techniques, and using a mixture of both. In the first case, we used a traditional, deterministic Levenberg-Marquardt (LM) algorithm. In the second one, we considered three stochastic algorithms: Spiral Optimization Algorithm (SOA), Vortex Search (VS), and Weighted Attraction Method (WAM). In the final case, we proposed a hybrid between LM and the metaheuristics algorithms. Results show that LM converges to the expected solutions only if the initial conditions (IC) are within a limited range. Oppositely, metaheuristics converge in a wide range of IC but exhibit low accuracy. The hybrid approaches converge and improve the accuracy obtained with the metaheuristics. The difference between expected and obtained values, as well as the RMS errors, are reported and compared for all three methods.

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“…El algoritmo de la atracción ponderada (WAM) se basa en el comportamiento de atracción gravitacional entre las partículas [16]. El algoritmo básico consta de cinco pasos, que se presentan en la Tabla 3.…”

Section: Método De La Atracción Ponderadaunclassified

Algoritmos de optimización en la estimación de propiedades termodinámicas en tiempo real durante el tratamiento térmico de materiales con microondas

García

Amaya

Correa

2017

revuin

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Este artículo puede compartirse bajo la licencia CC BY-ND 4.0 y se referencia usando el siguiente formato: E. García, I. Amaya, R. Correa, "Algoritmos de optimización en la estimación de propiedades termodinámicas en tiempo real durante el tratamiento térmico de materiales con microondas", UIS Ingenierías, vol. RESUMENEste trabajo considera la predicción en tiempo real de parámetros térmicos de una muestra sometida a radiación. Se estimaron la conductividad térmica y la capacidad calorífica. Se modeló el flujo volumétrico interno de calor uniforme y constante en el tiempo. Para resolver el problema inverso se usaron algoritmos de optimización global tales como el método de la espiral, el método de búsqueda en vórtice, el método de pesos ponderados, el método de enjambre de partículas unificado, el método de optimización del campo electromagnético y el método de búsqueda armónica de ancho auto-regulado. Los resultados muestran que todos los algoritmos empleados estiman correctamente estos dos parámetros siempre y cuando, la relación señal a ruido (SNR) de las muestras (simuladas en este trabajo) esté por encima de 30 [dB]. Por ende, para propósitos prácticos, si se dispone de un buen diseño experimental y una buena instrumentación, se podrían estimar con alta precisión estos parámetros en tiempo real. PALABRAS CLAVE:Calentamiento por microondas, Estimación de parámetros, Métodos de optimización global, Problema inverso. ABSTRACTThis work considers the real-time prediction of thermal parameters for a cylindrical sample heated in a uniform electromagnetic field. The thermal conductivity ( ) and the heat capacity ( ) were estimated for the present case. The inner volumetric electromagnetic flux radiation process was modeled as uniform and constant in time. The spiral optimization algorithm (SOA), the vortex search (VS), the weighted attraction method (WAM), the unified particle swarm optimization (UPSO), the electromagnetic field optimization (EFO) and the self-regulated fret-width harmony search algorithm (SFHS) were used to solve the ill-posed inverse problem. Results showed that all employed algorithms correctly estimated these two parameter only if the signal-to-noise-ratio of the measured samples (simulated in this work) were above of 30 [dB]. Therefore, for practical purposes, these parameters can be estimated in real-time if a good experimental design and a correctly specified electronic instrumentation are available.

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A New Metaheuristic Optimization Algorithm, the Weighted Attraction Method

Cited by 8 publications

References 9 publications

Identification of scalar Jiles-Atherton hysteresis model parameters with a new particle behaviour based metaheuristic algorithm

Identification of scalar Jiles-Atherton hysteresis model parameters with a new particle behaviour based metaheuristic algorithm

Internal volumetric heat generation and heat capacity prediction during a material electromagnetic treatment process using hybrid algorithms

Algoritmos de optimización en la estimación de propiedades termodinámicas en tiempo real durante el tratamiento térmico de materiales con microondas

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