Introducing deeper nulls and reduction of Side Lobe Levels in a symmetric linear antenna array using genetic algorithm

Goswami, Bipul; Mandal, Durbadal

doi:10.1109/rait.2012.6194454

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Novel algorithms are proposed for mitigating the disadvantages of various stochastic algorithms such as premature convergence, stagnation etc. First evolutionary proposed technique is GA (Mani and Bose, 2007; Awan et al , 2006; Basha et al , 2011; Wang et al , 2019b; Goswami and Mandal, 2012; Yallaparagada et al , 2011) utilized for antenna array synthesis. PSO is implemented for overcoming several problems related to antenna and electromagnetic systems.…”

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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“…Another objective is to reduce the energy transmitted by developing an array with fewer elements that fulfills the requirements of a full regular array through the reduction of the effective number of radiating elements. So the researchers have figured out ways to suppress peak sidelobe level using different optimization techniques such as simplex algorithm [1,2] and genetic algorithm [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. While reducing PSLL it is also desired to maintain the first null width within a specified region.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research is based on modifying GA to obtain shaped beam patterns [10,11]. Genetic Algorithm has also been implemented to introduce nulls at the specified locations [12,13]. In papers [14][15][16][17], different approaches are discussed to achieve sparseness and thinning of antenna array using genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of linear antenna array using genetic algorithm to reduce peak sidelobe level

Khalid

Sheikh

Shah³

et al. 2015

2015 9th International Conference on Electrical and Electronics Engineering (ELECO)

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In antenna design, it is important to optimize the amplitude weights of a linear antenna array to achieve low peak sidelobe level (PSLL). This paper deals with a linear inequality constraint on array factor to get maximum response in look direction and reduced sidelobe levels in a specified stop band region. Linear regular array as well as thinned linear array is optimized using a constrained genetic algorithm (CGA). The convergence of the optimization algorithm is also reported and its utility is shown in getting a desired antenna beam pattern with fewer radiating elements. Thinning is achieved by iteratively applying CGA which results in reducing the number of elements by 28% and overall transmitted energy by 27%, keeping a specified beam width first null (BWFN) with considerable reduction in PSLL.

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Pattern synthesis of conformal antenna array to achieve optimized amplitude weights for null steering and PSLL reduction

Khalid

Shah²,

Mirza³

et al. 2017

2017 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

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Introducing deeper nulls and reduction of Side Lobe Levels in a symmetric linear antenna array using genetic algorithm

Cited by 5 publications

References 13 publications

A review on optimization of antenna array by evolutionary optimization techniques

A review on optimization of antenna array by evolutionary optimization techniques

Synthesis of linear antenna array using genetic algorithm to reduce peak sidelobe level

Pattern synthesis of conformal antenna array to achieve optimized amplitude weights for null steering and PSLL reduction

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