scholarly journals Pattern Synthesis of Linear Antenna Array Using Improved Differential Evolution Algorithm with SPS Framework

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5158
Author(s):  
Ruimeng Zhang ◽  
Yan Zhang ◽  
Jinping Sun ◽  
Qing Li
Keyword(s):  
Differential Evolution ◽  
Antenna Array ◽  
Antenna Arrays ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Linear Antenna ◽  
Pattern Synthesis ◽  
De Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Linear Antenna Arrays

In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm’s performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement.

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.

scholarly journals MODIFIED DIFFERENTIAL EVOLUTION ALGORITHM FOR PATTERN SYNTHESIS OF ANTENNA ARRAYS

2013 ◽  
Vol 137 ◽  
pp. 371-388 ◽  
Author(s):  
Xin Li ◽  
Wen-Tao Li ◽  
Xiao-Wei Shi ◽  
Jing Yang ◽  
Jian-Feng Yu
Keyword(s):  
Differential Evolution ◽  
Antenna Arrays ◽  
Differential Evolution Algorithm ◽  
Pattern Synthesis ◽  
Evolution Algorithm

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

Phase-Only Pattern Synthesis for Linear Antenna Arrays

2017 ◽  
Vol 16 ◽  
pp. 3232-3235 ◽  
Author(s):  
Junli Liang ◽  
Xuhui Fan ◽  
Wen Fan ◽  
Deyun Zhou ◽  
Jian Li
Keyword(s):  
Antenna Arrays ◽  
Linear Antenna ◽  
Pattern Synthesis ◽  
Linear Antenna Arrays

scholarly journals Array Pattern Synthesis Using a Hybrid Differential Evolution and Analytic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2227
Author(s):  
Rui Li ◽  
Le Xu ◽  
Xiaoqun Chen ◽  
Yong Yang ◽  
Xiaoning Yang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Least Squares ◽  
Antenna Array ◽  
Least Squares Method ◽  
Total Least Squares ◽  
Weight Matrix ◽  
Pattern Synthesis ◽  
Application Range ◽  
De Algorithm ◽  
Array Pattern

In this paper, a hybrid differential evolution and weight total least squares method (HDE-WTLSM) is proposed for antenna array pattern synthesis. A variable diagonal weight matrix is introduced in total least squares method. Then, the weight matrix is optimized by differential evolution (DE) algorithm to control the differences of the desired level and the obtained level in different directions. This algorithm combines the advantages of evolutionary algorithm and numerical algorithm, so it has a wider application range and faster convergence speed. To compare HDE-WTLSM with DE algorithm and typical numerical algorithms, these methods are applied to a linear antenna array and a conformal truncated conical array. Using our method, lower sidelobe levels and deeper nulls are obtained. The simulation results verify the validity and efficiently of HDE-WTLSM.

scholarly journals An improved differential evolution algorithm with a restart technique to solve systems of nonlinear equations

2020 ◽  
Vol 10 (1) ◽  
pp. 118-136
Author(s):  
Jeerayut Wetweerapong ◽  
Pikul Puphasuk
Keyword(s):  
Differential Evolution ◽  
Nonlinear Equations ◽  
Nonlinear Models ◽  
Differential Evolution Algorithm ◽  
Systems Of Nonlinear Equations ◽  
Test Problems ◽  
De Algorithm ◽  
New Strategy ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

In this research, an improved differential evolution algorithm with a restart technique (DE-R) is designed for solutions of systems of nonlinear equations which often occurs in solving complex computational problems involving variables of nonlinear models. DE-R adds a new strategy for mutation operation and a restart technique to prevent premature convergence and stagnation during the evolutionary search to the basic DE algorithm. The proposed method is evaluated on various real world and synthetic problems and compared with the recently developed methods in the literature. Experiment results show that DE-R can successfully solve all the test problems with fast convergence speed and give high quality solutions. It also outperforms the compared methods.

scholarly journals Optimal Pattern Synthesis of Linear Antenna Array Using Grey Wolf Optimization Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Prerna Saxena ◽  
Ashwin Kothari
Keyword(s):  
Antenna Arrays ◽  
Side Lobe ◽  
Optimization Techniques ◽  
Main Beam ◽  
Linear Antenna ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pattern Synthesis ◽  
Uniform Array ◽  
Linear Antenna Arrays

The aim of this paper is to introduce the grey wolf optimization (GWO) algorithm to the electromagnetics and antenna community. GWO is a new nature-inspired metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It has potential to exhibit high performance in solving not only unconstrained but also constrained optimization problems. In this work, GWO has been applied to linear antenna arrays for optimal pattern synthesis in the following ways: by optimizing the antenna positions while assuming uniform excitation and by optimizing the antenna current amplitudes while assuming spacing and phase as that of uniform array. GWO is used to achieve an array pattern with minimum side lobe level (SLL) along with null placement in the specified directions. GWO is also applied for the minimization of the first side lobe nearest to the main beam (near side lobe). Various examples are presented that illustrate the application of GWO for linear array optimization and, subsequently, the results are validated by benchmarking with results obtained using other state-of-the-art nature-inspired evolutionary algorithms. The results suggest that optimization of linear antenna arrays using GWO provides considerable enhancements compared to the uniform array and the synthesis obtained from other optimization techniques.

Optimized hyper beamforming of receiving linear antenna arrays using Firefly algorithm

2013 ◽  
Vol 6 (2) ◽  
pp. 181-194 ◽  
Author(s):  
Gopi Ram ◽  
Durbadal Mandal ◽  
Rajib Kar ◽  
Sakti Prasad Ghoshal
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Firefly Algorithm ◽  
Beam Width ◽  
Linear Antenna ◽  
Linear Antenna Array ◽  
Heuristic Search Method ◽  
Real Coded Genetic Algorithm ◽  
Beam Patterns ◽  
Linear Antenna Arrays

In this paper, an optimized hyper beamforming method is presented based on a hyper beam exponent parameter for receiving linear antenna arrays using a new meta-heuristic search method based on the Firefly algorithm (FFA). A hyper beam is derived from the sum and difference beam patterns of the array, each raised to the power of a hyper beam exponent parameter. As compared to the conventional hyper beamforming of the linear antenna array, FFA applied to the hyper beam of the same array can achieve much more reduction in sidelobe level (SLL) and improved first null beam width (FNBW), keeping the same value of the hyper beam exponent. As compared to the uniformly excited linear antenna array with inter-element spacing of λ/2, conventional non-optimized hyper beamforming and optimal hyper beamforming of the same obtained by real-coded genetic algorithm, particle swarm optimization and Differential evolution, FFA applied to the hyper beam of the same array can achieve much greater reduction in SLL and same or less FNBW, keeping the same value of the hyper beam exponent parameter. The whole experiment has been performed for 10-, 14-, and 20-element linear antenna arrays.

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