Array pattern synthesis in the complex plane optimised by a genetic algorithm

1996 ◽  
Vol 32 (20) ◽  
pp. 1843 ◽  
Author(s):  
R.J. Mitchell ◽  
B. Chambers ◽  
A.P. Anderson
Keyword(s):  
Genetic Algorithm ◽  
Complex Plane ◽  
Pattern Synthesis ◽  
Array Pattern

Array pattern synthesis approach using a genetic algorithm

2014 ◽  
Vol 8 (14) ◽  
pp. 1236-1240 ◽  
Author(s):  
Hassan M. Elkamchouchi ◽  
Mohamed M. Hassan
Keyword(s):  
Genetic Algorithm ◽  
Pattern Synthesis ◽  
Array Pattern ◽  
Synthesis Approach

scholarly journals Linear Array Pattern Synthesis Using An Improved Multiobjective Genetic Algorithm

2017 ◽  
Vol 26 (4) ◽  
pp. 1048-1059 ◽  
Author(s):  
C. Han ◽  
L. Wang ◽  
Z. Zhang ◽  
J. Xie ◽  
Z. Xing
Keyword(s):  
Genetic Algorithm ◽  
Linear Array ◽  
Pattern Synthesis ◽  
Multiobjective Genetic Algorithm ◽  
Array Pattern

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.

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