scholarly journals Circular Antenna Array Synthesis Using Multiverse Optimizer

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ahmet Emre Taser ◽  
Kerim Guney ◽  
Erhan Kurt
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Mathematical Problem ◽  
Synthesis Process ◽  
Pattern Synthesis ◽  
White Hole ◽  
Array Pattern ◽  
Nature Inspired Algorithm ◽  
Antenna Array Synthesis ◽  
Circular Antenna Array

Antenna array synthesis is one of the most popular topics in the electromagnetic field. Since achieving a desired antenna radiation pattern is a mathematical problem, in the literature, there are various optimization algorithms applied to the synthesis process of different kinds of antenna arrays. In this study, Multiverse Optimizer (MVO) and modified MVO (MMVO) are used to perform circular antenna array (CAA) synthesis. During the exploration, exploitation, and local search phases of calculation, MVO uses three concepts in cosmology; white hole, black hole, and wormhole. Convergence capability of this nature-inspired algorithm is employed for finding optimum amplitude and position values of CAA elements in order to achieve an array pattern with low maximum sidelobe level (MSL) and minimum circumference. The performance of MVO and MMVO was tested on five design examples of pattern synthesis, and the obtained results were compared with ten different algorithms. The simulation results show that MVO and MMVO provide low MSLs with small circumferences.

scholarly journals Impacts of Genetic Algorithm Parameters on the Solution Performance for the Uniform Circular Antenna Array Pattern Synthesis Problem

2010 ◽  
Vol 8 (03) ◽  
Author(s):  
F. Yaman ◽  
A. E. Ylmaz
Keyword(s):  
Genetic Algorithm ◽  
Antenna Array ◽  
Mutation Rate ◽  
Synthesis Problem ◽  
Pattern Synthesis ◽  
Array Pattern ◽  
Real Coded Genetic Algorithm ◽  
Random Position ◽  
Random Crossover ◽  
Circular Antenna Array

In this paper, the uniform circular antenna array pattern synthesis problem is solved by means of the real coded genetic algorithm (GA). At the same time, the impacts of the mutation rate and the crossover position on the GAperformance are also investigated. For this purpose, a circular antenna array with uniformly spaced isotropic elements having identical excitation amplitudes is used as a model. Unlike the conventional GA (with fixed mutation rate and random crossover positions), typical GA implementations with variable mutation rate and restricted crossover position are considered for performance improvement. In conclusion, for the specific problem, decreasing mutation rate with negative derivative is observed to be outperforming the implementations with different mutation rate behaviors. Moreover, regarding the crossover technique, it is observed that imposing some restrictions on the crossover positions (rather than fully random position selection) yields better solutions.

Application of the Unit Circle Representation to Pattern Synthesis of Nonuniformly Spaced and Nonuniformly Excited Arrays based on the Teaching Learning Based Optimization

2019 ◽  
Vol 4 (1) ◽  
pp. 8-17
Author(s):  
Abdelmadjid RECIOUI
Keyword(s):  
Unit Circle ◽  
Antenna Array ◽  
Communication Systems ◽  
Sidelobe Level ◽  
Pattern Synthesis ◽  
Linear Arrays ◽  
Array Pattern ◽  
Teaching Learning Based Optimization ◽  
Null Placement ◽  
Teaching Learning

Pattern synthesis of Antenna array has gained much attention over the last years as they constitute an important role in the modern communication systems. Unit circle-based techniques such as Schelkunoff null placement method have proved their effectiveness to synthesize uniformly spaced linear arrays. Nonuniformly spaced antenna array pattern synthesis has been investigated and interesting results have been obtained. In this work, the unit circle representation approach is applied to synthesize nonuniformly spaced and nonuniformly excited linear arrays. The objective is to accurately place nulls in the desired directions while achieving the least possible sidelobe level. The problem is cast as an optimization problem that is solved using the Teaching Learning Based Optimization (TLBO). Examples are dealt with to prove the design approach effectiveness and flexibility for modern communication system applications.

scholarly journals Null Steering in Failed Antenna Arrays

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Om Prakash Acharya ◽  
Amalendu Patnaik ◽  
Sachendra N. Sinha
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Correction Procedure ◽  
Swarm Optimization ◽  
Array Pattern ◽  
Null Steering ◽  
Simulation Results ◽  
Pattern Simulation

Antenna array pattern nulling is desirable in order to suppress the interfering signals. But in large antenna arrays, there is always a possibility of failure of some elements, which may degrade the radiation pattern with an increase in side lobe level (SLL) and removal of the nulls from desired position. In this paper a correction procedure is introduced based on Particle Swarm Optimization (PSO) which maintains the nulling performance of the failed antenna array. Considering the faulty elements as nonradiating elements, PSO reoptimizes the weights of the remaining radiating elements to reshape the pattern. Simulation results for a Chebyshev array with imposed single, multiple, and broad nulls with failed antenna array are presented.

A Comparison of Swarm-Based Optimization Algorithms in Linear Antenna Array Synthesis

2021 ◽  
Author(s):  
Ali Durmus ◽  
Rifat KURBAN ◽  
Ercan KARAKOSE
Keyword(s):  
Antenna Array ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Heuristic Algorithms ◽  
Beam Width ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Linear Antenna ◽  
Half Power ◽  
Antenna Array Synthesis

Abstract Today, the design of antenna arrays is very important in providing effective and efficient wireless communication. The purpose of antenna array synthesis is to obtain a radiation pattern with low side lobe level (SLL) at a desired half power beam width (HPBW) in far-field. The amplitude and position values ​​of the array elements can be optimized to obtain a radiation pattern with suppressed SLLs. In this paper swarm-based meta-heuristic algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Mayfly algorithm (MA) and Jellyfish Search (JS) algorithms are compared to realize optimal design of linear antenna arrays. Extensive experiments are conducted on designing 10, 16, 24 and 32-element linear arrays by determining the amplitude and positions. Experiments are repeated 30 times due to the random nature of swarm-based optimizers and statistical results show that performance of the novel algorithms, MA and JS, are better than well-known methods PSO and ABC.

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