MULTI-OBJECTIVES ADAPTIVE ARRAY SYNTHESIS USING SPEEDY-PARTICLE SWARM METHOD

2015 ◽  
Vol 77 (10) ◽  
Author(s):  
N.N.N.A. Malik ◽  
M. Esa ◽  
S.K.S. Yusof ◽  
N.M.A. Latiff
Keyword(s):  
Antenna Arrays ◽  
Particle Swarm ◽  
Main Beam ◽  
Adaptive Array ◽  
Sidelobe Level ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Particle Swarm Method ◽  
Distance Position ◽  
Linear Antenna Arrays

A method of computing the optimum element distance position of multi-objectives adaptive linear antenna arrays (MLAA) is developed by taking several objectives (eg. adaptive capability, beamwidth and minimum sidelobe level (SLL)) into consideration. In this paper, the recently invented algorithm, known as Speedy-Particle Swarm Optimization (SpPSO) algorithm is adopted to optimize the distance between the MLAA elements. Different numerical examples of 8- and 12-element MLAA are presented to validate and illustrate the capability of SpPSO for pattern synthesis with a prescribed adaptive angle, controllable beamwidth and minimum SLL. It was found that by employing SpPSO method, the results provide considerable improvement over the conventional array. It is observed that the maximum normalized SLL of -12.27 dB has been achieved by using SpPSO for 8-element MLAA. The proposed SpPSO-based LAA also able to achieve a beampattern with sufficiently low sidelobes for 12-element MLAA by having maximum SLL of -16.46 dB, a desired wider FNBW of 50° and main beam that is pointing to 20°.  

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.

scholarly journals Array Pattern Synthesis Using Particle Swarm Optimization with Dynamic Inertia Weight

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Chuang Han ◽  
Ling Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Main Beam ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Initial Space ◽  
Array Pattern ◽  
Dynamic Inertia Weight

A Feedback Particle Swarm Optimization (FPSO) with a family of fitness functions is proposed to minimize sidelobe level (SLL) and control null. In order to search in a large initial space and converge fast in local space to a refined solution, a FPSO with nonlinear inertia weight algorithm is developed, which is determined by a subtriplicate function with feedback taken from the fitness of the best previous position. The optimized objectives in the fitness function can obtain an accurate null level independently. The directly constrained SLL range reveals the capability to reduce SLL. Considering both element positions and complex weight coefficients, a low-level SLL, accurate null at specific directions, and constrained main beam are achieved. Numerical examples using a uniform linear array of isotropic elements are simulated, which demonstrate the effectiveness of the proposed array pattern synthesis approach.

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