Synthesis of thinned planar antenna arrays using teaching–learning-based optimization

2014 ◽  
Vol 7 (5) ◽  
pp. 557-563 ◽  
Author(s):  
Nihad I. Dib
Keyword(s):  
Antenna Arrays ◽  
Optimization Problems ◽  
Optimization Method ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Planar Antenna ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Level Reduction ◽  
Better Than

In this paper, the design of thinned planar antenna arrays of isotropic radiators with optimum side lobe level reduction is studied. The teaching–learning-based optimization (TLBO) method, a newly proposed global evolutionary optimization method, is used to determine an optimum set of turned-ON elements of thinned planar antenna arrays that provides a radiation pattern with optimum side lobe level reduction. The TLBO represents a new algorithm for optimization problems in antenna arrays design. It is shown that the TLBO provides results that are better than (or the same as) those obtained using other evolutionary algorithms.

Evolution of conformal antenna design with the aid of probability improved crow search algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nama Ajay Nagendra ◽  
Lakshman Pappula
Keyword(s):  
Antenna Arrays ◽  
Search Algorithm ◽  
Fundamental Problem ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Antenna Element ◽  
Content Type ◽  
Conformal Antenna ◽  
Adopted Model ◽  
Better Than

PurposeThe issues of radiating sources in the existence of smooth convex matters by such objects are of huge significance in the modeling of antennas on structures. Conformal antenna arrays are necessary when an antenna has to match to certain platforms. A fundamental problem in the design is that the possible surfaces for a conformal antenna are infinite in number. Furthermore, if there is no symmetry, each element will see a different environment, and this complicates the mathematics. As a consequence, the element factor cannot be factored out from the array factor.Design/methodology/approachThis paper intends to enhance the design of the conformal antenna. Here, the main objective of this task is to maximize the antenna gain and directivity from the first-side lobe and other side-lobes in the two way radiation pattern. Thus the adopted model is designed as a multiobjective concern. In order to attain this multiobjective function, both the element spacing and the radius of each antenna element should be optimized based on the probability of the Crow Search Algorithm (CSA). Thus the proposed method is named Probability Improved CSA (PI-CSA). Here, the First Null Beam Width (FNBW) and Side-Lobe Level (SLL) are minimized. Moreover, the adopted scheme is compared with conventional algorithms, and the results are attained.FindingsFrom the analysis, the gain of the presented PI-CSA scheme in terms of best performance was 52.68% superior to ABC, 25.11% superior to PSO, 13.38% superior to FF and 3.21% superior to CS algorithms. Moreover, the mean performance of the adopted model was 62.94% better than ABC, 13.06% better than PSO, 24.34% better than FF and 10.05% better than CS algorithms. By maximizing the gain and directivity, FNBW and SLL were decreased. Thus, the optimal design of the conformal antenna has been attained by the proposed PI-CSA algorithm in an effective way.Originality/valueThis paper presents a technique for enhancing the design of the conformal antenna using the PI-CSA algorithm. This is the first work that utilizes PI-CSA-based optimization for improving the design of the conformal antenna.

scholarly journals Synthesis of Thinned Concentric Circular Antenna Arrays Using Modified TLBO Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Zailei Luo ◽  
Xueming He ◽  
Xuedong Chen ◽  
Xin Luo ◽  
Xiaoqing Li
Keyword(s):  
Antenna Arrays ◽  
Optimization Problems ◽  
Extended Space ◽  
Half Wavelength ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Antenna Array Synthesis ◽  
Circular Antenna Arrays ◽  
Circular Antenna Array

Teaching-learning-based optimization (TLBO) algorithm is a new kind of stochastic metaheuristic algorithm which has been proven effective and powerful in many engineering optimization problems. This paper describes the application of a modified version of TLBO algorithm, MTLBO, for synthesis of thinned concentric circular antenna arrays (CCAAs). The MTLBO is adjusted for CCAA design according to the geometry arrangement of antenna elements. CCAAs with uniform interelement spacing fixed at half wavelength have been considered for thinning using MTLBO algorithm. For practical purpose, this paper demonstrated SLL reduction of thinned CCAAs in the whole regular and extended space other than the phi = 0° plane alone. The uniformly and nonuniformly excited CCAAs have been discussed, respectively, during the simulation process. The proposed MTLBO is very easy to be implemented and requires fewer algorithm specified parameters, which is suitable for concentric circular antenna array synthesis. Numerical results clearly show the superiority of MTLBO algorithm in finding optimum solutions compared to particle swarm optimization algorithm and firefly algorithm.

Synthesis of Linear, Planar, and Concentric Circular Antenna Arrays Using Rao Algorithms

2020 ◽  
Vol 11 (3) ◽  
pp. 31-49
Author(s):  
Jaya Lakshmi Ravipudi
Keyword(s):  
Antenna Arrays ◽  
Optimization Algorithms ◽  
Side Lobe ◽  
Binary Particle Swarm Optimization ◽  
Lower Side ◽  
Improved Genetic Algorithm ◽  
Swarm Optimization ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Circular Antenna Arrays

The aim of this paper is to display the efficacy of three newly proposed optimization algorithms named as Rao-1, Rao-2, and Rao-3 in synthesizing antenna arrays. The algorithms are applied to three different antenna array configurations. Thinned arrays with isotropic radiators are considered and the main objective is to find the optimal configuration of ON/OFF elements that produce low side lobe levels. The results of Rao-1, Rao-2, and Rao-3 algorithms are compared with those of improved genetic algorithm (IGA), hybrid Taguchi binary particle swarm optimization (HTBPSO), teaching-learning-based optimization (TLBO), the firefly algorithm (FA), and biogeography-based optimization (BBO). The Rao-1, Rao-2, and Rao-3 algorithms were able to realize antenna arrays having lower side lobe levels (SLL) when compared to the other optimization algorithms.

scholarly journals On the Trade-Off between the Main Parameters of Planar Antenna Arrays

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 739
Author(s):  
Daniele Pinchera
Keyword(s):  
Antenna Arrays ◽  
Beam Width ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Planar Antenna ◽  
Trade Off ◽  
Planar Arrays ◽  
Novel Approach ◽  
Level Number ◽  
Scanning Range

The aim of this paper is two-fold. First, the trade-off between directivity, beam-width, side-lobe-level, number of radiating elements, and scanning range of planar antenna arrays is reviewed, and some simple ready-to-use formulas for the preliminary dimensioning of equispaced planar arrays are provided. Furthermore, the synthesis of sparse planar arrays, and the issue of their reduction in directivity, is analyzed. Second, a simple, yet effective, novel approach to overcome the directivity issue is proposed. The presented method is validated by several synthesized layouts; the examples show that it is possible to synthesize sparse arrays, able to challenge with equispaced lattices in terms of directivity, with a significant reduction of the number of radiators.

Maximizing main-lobe to the side-lobe level for the non-uniform chaos game planar antenna array using an IWO algorithm

2012 ◽  
Vol 4 (6) ◽  
pp. 635-646
Author(s):  
Ahmed Najah Jabbar ◽  
Ali Shaban Hasooni ◽  
Muthana Khallil Ibrahim
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Side Lobe ◽  
Main Lobe ◽  
Side Lobe Level ◽  
Planar Antenna ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Chaos Game ◽  
Planar Antenna Array

In this study, we present the implementation of invasive weed optimization (IWO) in the maximization of main-lobe to side-lobe level for the non-uniform planar antenna array. The antenna arrays investigated in this study are generated using the chaos game algorithm (CGA) and shaped into selected fractal geometries chosen on the basis of their interesting performance. This CGA is picked out in order to overcome the limitations found in the fractal arrays. All the attained results are compared with the results produced by a well-known optimization algorithm that is the particle swarm optimization (PSO). In all the optimized arrays, IWO shows superior optimization results compared with PSO.

scholarly journals Bare-Bones Teaching-Learning-Based Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Feng Zou ◽  
Lei Wang ◽  
Xinhong Hei ◽  
Debao Chen ◽  
Qiaoyong Jiang ◽  
...  
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Class Room ◽  
Teaching Learning ◽  
Bare Bones ◽  
Gaussian Sampling ◽  
Better Than

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

Design of non-uniform concentric circular antenna arrays with optimal sidelobe level reduction using biogeography-based optimization

2014 ◽  
Vol 7 (2) ◽  
pp. 161-166 ◽  
Author(s):  
Nihad Dib ◽  
Ashraf Sharaqa
Keyword(s):  
Antenna Arrays ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Sidelobe Level ◽  
Sqp Method ◽  
Sidelobe Level Reduction ◽  
Level Reduction ◽  
Circular Antenna Arrays ◽  
Better Than

This paper presents the design of non-uniform concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum sidelobe level (SLL) reduction. The biogeography-based optimization (BBO) method is used to determine an optimum set of excitation amplitudes that provide a radiation pattern with optimum SLL reduction with the constraint of a fixed major lobe beamwidth. The BBO method represents a new global evolutionary algorithm for optimization problems in electromagnetics. It is shown that the BBO results provide an SLL reduction that is comparable to that obtained using well-known algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and evolutionary programming (EP). Moreover, BBO results are compared with those obtained using the Matlab function Fmincon which uses a sequential quadratic programming (SQP) method. The comparison shows that the design of non-uniformly excited CCAAs using the SQP method provides a SLL reduction that is better than that obtained using global stochastic optimization methods, indicating that global optimization techniques might not really be needed in this problem.

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