Comparison of different optimization techniques in antenna design - Part I

Zich, R.E.; Mussetta, Marco; Grimaccia, Francesco; Banchetti, Jacopo; Guggiari, T.; Catelan, A. Oregio; Presti, Francesco Lo; Testoni, O.; Zanelli, T.

doi:10.1109/aps.2012.6348757

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Dedicated antenna forms, necessary for antenna design by 5G implementation, were enhanced to suit the requirements of specific applications. Employing fractal-shaped antennas have allowed us to accomplish all these actions (Wang et al , 2013; Hreshee, 2013; Li et al , 2012; Wang et al , 2012; Zich et al , 2012; Ling et al ., 2021), which request compact, conformal and broadband high-performance devices. Antennas derived from Koch's curve fractals are studied.…”

Section: Evolutionary Techniques Of Optimizationmentioning

confidence: 99%

A review on optimization of antenna array by evolutionary optimization techniques

Kala

Sundari

2021

IJIUS

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PurposeOptimization involves changing the input parameters of a process that is experimented with different conditions to obtain the maximum or minimum result. Increasing interest is shown by antenna researchers in finding the optimum solution for designing complex antenna arrays which are possible by optimization techniques.Design/methodology/approachDesign of antenna array is a significant electro-magnetic problem of optimization in the current era. The philosophy of optimization is to find the best solution among several available alternatives. In an antenna array, energy is wasted due to side lobe levels which can be reduced by various optimization techniques. Currently, developing optimization techniques applicable for various types of antenna arrays is focused on by researchers.FindingsIn the paper, different optimization algorithms for reducing the side lobe level of the antenna array are presented. Specifically, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search algorithm (CSA), invasive weed optimization (IWO), whale optimization algorithm (WOA), fruitfly optimization algorithm (FOA), firefly algorithm (FA), cat swarm optimization (CSO), dragonfly algorithm (DA), enhanced firefly algorithm (EFA) and bat flower pollinator (BFP) are the most popular optimization techniques. Various metrics such as gain enhancement, reduction of side lobe, speed of convergence and the directivity of these algorithms are discussed. Faster convergence is provided by the GA which is used for genetic operator randomization. GA provides improved efficiency of computation with the extreme optimal result as well as outperforming other algorithms of optimization in finding the best solution.Originality/valueThe originality of the paper includes a study that reveals the usage of the different antennas and their importance in various applications.

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Section: Evolutionary Techniques Of Optimizationmentioning

confidence: 99%

A review on optimization of antenna array by evolutionary optimization techniques

Kala

Sundari

2021

IJIUS

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Comparison of different optimization techniques in antenna design - Part II

Zich¹,

Mussetta²,

Grimaccia³

et al. 2012

Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation

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In this paper an elliptical reflectarray antenna is optimized by some novel evolutionary techniques: Ant Colony Optimization (ACO), Biogeography Based Optimization (BBO), Differential Evolution (DE), Population-Based Incremental Learning (PBIL) and Stud Genetic Algorithm (SGA). The optimized design of the reflectarray is aimed to compare their performances on a complex EM optimization problem. Results show that some techniques (DE, BBO, SGA) are particularly effective in dealing with antenna optimization

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Planar array optimization by means of SNO and StudGA

Boldini

Gonano

Grimaccia

et al. 2014

2014 IEEE Antennas and Propagation Society International Symposium (APSURSI)

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In recent years there has been an increasing attention to novel evolutionary optimization techniques. A recently developed algorithm called Social Network Optimization (SNO), based on the emulation of decision making process in social network environments, is here considered and compared to Stud Genetic Algorithm (SGA). The design of a planar array is here addressed in order to compare their performances on a benchmark EM optimization problem. Reported results show their effectiveness in dealing with antenna optimization

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Comparison of different optimization techniques in antenna design - Part I

Cited by 5 publications

References 8 publications

A review on optimization of antenna array by evolutionary optimization techniques

A review on optimization of antenna array by evolutionary optimization techniques

Comparison of different optimization techniques in antenna design - Part II

Planar array optimization by means of SNO and StudGA

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