An improved multiobjective evolutionary algorithm based on decomposition approach and its application in antenna array beam pattern synthesis

2021 ◽  
Author(s):  
Shuang Liang ◽  
Zhiyi Fang ◽  
Guanxiao Li ◽  
Yaqing Zhao ◽  
Xuejie Liu ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Antenna Array ◽  
Beam Pattern ◽  
Pattern Synthesis ◽  
Decomposition Approach ◽  
Multiobjective Evolutionary Algorithm

scholarly journals Synthesis of Steered Flat-top Beam Pattern Using Evolutionary Algorithm

2016 ◽  
Vol 5 (3) ◽  
pp. 86 ◽  
Author(s):  
D. Mandal ◽  
K. S. Kola ◽  
J. Tewary ◽  
V. P. Roy ◽  
A. K. Bhattacharjee
Keyword(s):  
Evolutionary Algorithm ◽  
Dynamic Range ◽  
Synthesis Method ◽  
Elevation Angle ◽  
Differential Evolution Algorithm ◽  
Beam Pattern ◽  
Main Beam ◽  
Pattern Synthesis ◽  
Feed Networks ◽  
Flat Top Beam

In this paper a pattern synthesis method based on Evolutionary Algorithm is presented. A Flat-top beam pattern has been generated from a concentric ring array of isotropic elements by finding out the optimum set of elements amplitudes and phases using Differential Evolution algorithm. The said pattern is generated in three predefined azimuth planes instate of a single phi plane and also verified for a range of azimuth plane for the same optimum excitations. The main beam is steered to an elevation angle of 30 degree with lower peak SLL and ripple. Dynamic range ratio (DRR) is also being improved by eliminating the weakly excited array elements, which simplify the design complexity of feed networks.

scholarly journals Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Zhi-Kun Chen ◽  
Feng-Gang Yan ◽  
Xiao-Lin Qiao ◽  
Yi-Nan Zhao
Keyword(s):  
Differential Evolution ◽  
Antenna Array ◽  
Optimization Problem ◽  
Mean Squared Error ◽  
Differential Evolution Algorithm ◽  
Mimo Radar ◽  
Beam Pattern ◽  
Pattern Synthesis ◽  
Array Design ◽  
Second Stage

A two-stage design approach is proposed to address the sparse antenna array design for multiple-input multiple-output radar. In the first stage, the cyclic algorithm (CA) is used to establish a covariance matrix that satisfies the beam pattern approximation for a full array. In the second stage, a sparse antenna array with a beam pattern is designed to approximate the desired beam pattern. This paper focuses on the second stage. The optimization problem for the sparse antenna array design aimed at beam pattern synthesis is formulated, where the peak side lobe (PSL) is weakly constrained by the mean squared error. To solve this optimization problem, the differential evolution (DE) algorithm with multistrategy is introduced and PSL suppression is treated as an inequality constraint. However, in doing so, a new multiobjective optimization problem is created. To address this new problem, a multiobjective differential evolution algorithm based on Pareto technique is proposed. Numerical examples are provided to demonstrate the advantages of the proposed approach over state-of-the-art methods, including DE and genetic algorithm.

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