Antenna Array Synthesis with Evolutionary Algorithms

2012 ◽  
pp. 47-54
Author(s):  
Ernesto Sanchez ◽  
Giovanni Squillero ◽  
Alberto Tonda
Keyword(s):  
Evolutionary Algorithms ◽  
Antenna Array ◽  
Antenna Array Synthesis

Antenna array synthesis using lambda function

2005 ◽  
Author(s):  
S. Shih ◽  
L. Bergstein
Keyword(s):  
Antenna Array ◽  
Antenna Array Synthesis

Non Uniform-Antenna Array Synthesis Using Neural Networks

2007 ◽  
Vol 21 (8) ◽  
pp. 1001-1011 ◽  
Author(s):  
R. G. Ayestarán ◽  
F. Las-Heras ◽  
J. A. Martínez
Keyword(s):  
Neural Networks ◽  
Antenna Array ◽  
Antenna Array Synthesis

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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