scholarly journals A beamforming study of the linear antenna array using grey wolf optimization algorithm

2020 ◽  
Vol 20 (3) ◽  
pp. 1538
Author(s):  
Asma Issa Mohsin ◽  
Asaad S. Daghal ◽  
Adheed Hasan Sallomi
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
High Performance ◽  
Optimization Problems ◽  
Linear Antenna ◽  
Grey Wolf ◽  
Sidelobe Level ◽  
Grey Wolf Optimization ◽  
Hunting Behavior ◽  
Code Division Multiple

<p><br />The grey wolf optimization (GWO) algorithm is considered an inspired meta-heuristic algorithm, which inspired by the social hierarchy and hunting behavior of the grey wolves. GWO has a high-performance capability of solving constrained, as well as unconstrained optimization problems. In this paper, the beamforming of smart antennas in a code division multiple access system based on the GWO algorithm is investigated. The sidelobe level (SLL) is minimized along with peak sidelobe level reduction, as well as an optimal beam pattern has been accomplished by using GWO to uniform linear antenna arrays. In this work, an amplitude is introduced as constant, while the interspacing distance between antenna array elements and the number of elements in a linear array are variables. The simulation results show that a faster convergence and likely high accurate beamforming are gained using GWO based method. Finally, it is shown that the GWO outperforms the genetic algorithm (GA) based method.</p>

scholarly journals Elliptic and Concentric-Elliptic Antenna Array Design using Grey Wolf Optimization

2019 ◽  
Vol 4 (1) ◽  
pp. 32-38
Author(s):  
Abdelmadjid RECIOUI ◽  
Oussama Traikia ◽  
Youcef GRAINAT
Keyword(s):  
Antenna Arrays ◽  
Communication Systems ◽  
Grey Wolf ◽  
Good Reduction ◽  
Sidelobe Level ◽  
Grey Wolf Optimization ◽  
Hunting Behavior ◽  
Array Design ◽  
The Social ◽  
Optimal Values

In this work, Grey Wolf Optimization (GWO) is applied to the optimization of elliptical and concentric elliptical antenna arrays. The objective is to minimize the Sidelobe level and improve the directivity. GWO is a nature-inspired meta-heuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. The obtained optimal values result in a good reduction of the sidelobe level for the elliptical and concentric elliptical antenna arrays with an enhancement in the directivity. This makes the designed arrays of practical use in the communication systems.

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.

SYNTHESIS OF UNIFORM LINEAR ANTENNA ARRAY USING GENETIC ALGORITHM WITH COST BASED ROULETTE TO MAXIMIZE SIDELOBE LEVEL REDUCTION

2011 ◽  
Vol 08 (02) ◽  
pp. 171-179
Author(s):  
T. S. JEYALI LASEETHA ◽  
R. SUKANESH
Keyword(s):  
Genetic Algorithm ◽  
Antenna Array ◽  
Radiation Pattern ◽  
Optimization Method ◽  
Adaptive Beamforming ◽  
Linear Antenna ◽  
Algorithm Optimization ◽  
Sidelobe Level ◽  
Linear Antenna Array ◽  
Sidelobe Level Reduction

This paper discusses the deployment of Genetic Algorithm optimization method for the synthesis of antenna array radiation pattern in adaptive beamforming. The synthesis problem discussed is to find the weights of the Uniform Linear Antenna array elements that are optimum to provide the radiation pattern with maximum reduction in the sidelobe level. This technique proved its effectiveness in improving the performance of the antenna array.

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.

scholarly journals An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1457
Author(s):  
Avelina Alejo-Reyes ◽  
Erik Cuevas ◽  
Alma Rodríguez ◽  
Abraham Mendoza ◽  
Elias Olivares-Benitez
Keyword(s):  
Supplier Selection ◽  
Search Strategy ◽  
Optimization Problems ◽  
Displacement Vector ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Order Quantity ◽  
Hunting Behavior

Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

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