Calculation of optimized parameters of rectangular microstrip patch antenna using particle swarm optimization

Yilmaz, Asım Egemen; Kuzuoğlu, Mustafa

doi:10.1002/mop.22918

Cited by 25 publications

(18 citation statements)

References 10 publications

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“…The particle is moving in the search space with some velocity which plays a pivotal role in the search process [15,17]. Denote the velocity of the i-th particle by v i .…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…PSO is similar to the genetic algorithms (GAs) in the sense that these two heuristics are population-based search techniques, namely, PSO and GA operate on a population (swarm) and transform it to another set of population in a single iteration with likely improvement using a combination or deterministic and probabilistic rules. PSO is quite often compared with GA [13][14][15][16][17]. Interestingly, most of the literature is concerned with simple comparative scenarios involving experiments exploiting numeric data.…”

Section: Introductionmentioning

confidence: 99%

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Prototype-based Classifier with Feature Selection and Its Design with Particle Swarm Optimization: Analysis and Comparative Studies

Park¹,

Oh²

2012

Journal of Electrical Engineering and Technology

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-In this study, we introduce a prototype-based classifier with feature selection that dwells upon the usage of a biologically inspired optimization technique of Particle Swarm Optimization (PSO). The design comprises two main phases. In the first phase, PSO selects P % of patterns to be treated as prototypes of c classes. During the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative coordinates of the original feature space. The proposed scheme of feature selection is developed in the wrapper mode with the performance evaluated with the aid of the nearest prototype classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness (quality of solution) and efficiency (computing cost) of the approach when applied to a collection of selected data sets. We also include a comparative study which involves the usage of genetic algorithms (GAs). Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner becomes characterized by low classification error. In addition, the advantage of the PSO is quantified in detail by running a number of experiments using Machine Learning datasets.

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“…The particle is moving in the search space with some velocity which plays a pivotal role in the search process [15,17]. Denote the velocity of the i-th particle by v i .…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prototype-based Classifier with Feature Selection and Its Design with Particle Swarm Optimization: Analysis and Comparative Studies

Park¹,

Oh²

2012

Journal of Electrical Engineering and Technology

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“…With the development of the artificial intelligence optimization algorithms, the various methods involving these optimization algorithms have been applied to microstrip antennas [19][20][21][22]. In Ref [19], the multiband and wideband patch antennas were designed by using parallel particle swarm optimization and finite difference time domain (FDTD) algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref [19], the multiband and wideband patch antennas were designed by using parallel particle swarm optimization and finite difference time domain (FDTD) algorithm. The optimized parameters of a rectangular microstrip antenna (RMSA) have been calculated by particle swarm optimization (PSO) algorithm [20]. The genetic algorithm (GA) based on biological evolution [21] was used to determine the optimum feed position of the probe-feed microstrip antenna.…”

Section: Introductionmentioning

confidence: 99%

Yeni Bir Melez Optimizasyon Algoritması Kullanarak UMTS, WLAN ve WiMAX Uygulamaları için Çift–Geniş Bantlı Monopole Anten Tasarımı

Üstün¹,

Akdağlı²

2016

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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In this study, a compact dual-wideband monopole antenna is proposed for the universal mobile telecommunications system (UMTS), wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications. In order to obtain the desired operating frequency bands, UMTS (1.9-2.1 GHz), WLAN (2.4/5.2/5.8 GHz) and WiMAX (2.5/3.5/5.5 GHz), a novel integration technique consisting of the HyperLynx ® 3D electromagnetic (EM) platform based on the method of moments (MoM) and a new hybrid optimization algorithm (HOA) is utilized. The HOA is developed by integrating powerful mutation and crossover strategies of differential evolution (DE) algorithm with the onlooker bee phase of the artificial bee colony (ABC) algorithm. The monopole antenna is capable of producing two distinct operating bands. First impedance bandwidth of 2.48 GHz is from 1.44 to 3.92 GHz and second band having the bandwidth of 1.58 GHz is between 4.92 and 6.50 GHz. The proposed antenna has been designed, simulated and fabricated on 42×49.2×1.6 mm 3 FR4 substrate with relative permittivity 4.4 and loss tangent 0.02. The better performance in terms of impedance matching is achieved, as compared to the double-or triple-band antennas previously published in the literature. In addition, the design antenna performs the omnidirectional radiation patterns with a good gain performance in the operating bands.

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“…In [4,5], patch antennas were optimized using particle swarm optimization (PSO). Namkung et al [6] applied genetic algorithm (GA) for feed point optimization of a patch antenna.…”

Section: Introductionmentioning

confidence: 99%

Broadband patch antenna design using differential evolution algorithm

Zhang

Cui

Jiao

et al. 2009

Micro & Optical Tech Letters

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Calculation of optimized parameters of rectangular microstrip patch antenna using particle swarm optimization

Cited by 25 publications

References 10 publications

Prototype-based Classifier with Feature Selection and Its Design with Particle Swarm Optimization: Analysis and Comparative Studies

Prototype-based Classifier with Feature Selection and Its Design with Particle Swarm Optimization: Analysis and Comparative Studies

Yeni Bir Melez Optimizasyon Algoritması Kullanarak UMTS, WLAN ve WiMAX Uygulamaları için Çift–Geniş Bantlı Monopole Anten Tasarımı

Broadband patch antenna design using differential evolution algorithm

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