scholarly journals Synthesis of Thinned Planar Antenna Array Using Multiobjective Normal Mutated Binary Cat Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Lakshman Pappula ◽  
Debalina Ghosh
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Performance Metrics ◽  
Beam Width ◽  
Optimization Method ◽  
Binary Particle Swarm Optimization ◽  
Planar Antenna ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Cat Swarm Optimization

The process of thinned antenna array synthesis involves the optimization of a number of mutually conflicting parameters, such as peak sidelobe level, first null beam width, and number of active elements. This necessitates the development of a multiobjective optimization approach which will provide the best compromised solution based on the application at hand. In this paper, a novel multiobjective normal mutated binary cat swarm optimization (MO-NMBCSO) is developed and proposed for the synthesis of thinned planar antenna arrays. Through this method, a high degree of flexibility is introduced to the realm of thinned array design. A Pareto-optimal front containing all the probable designs is obtained in this process. Targeted solutions may be chosen from the Pareto front to satisfy the different requirements demonstrating the superiority of the proposed approach over multiobjective binary particle swarm optimization method (MO-BPSO). A comparative study is carried out to quantify the performance of the two algorithms using two performance metrics.

Minimizing sidelobe levels and facilitating null placements of nonlinear antenna arrays using an improved particle swarm optimization method

2013 ◽  
Vol 33 (1/2) ◽  
pp. 65-73 ◽  
Author(s):  
Huijun Deng ◽  
Xue Li ◽  
Libao Sun ◽  
Shiyou Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Antenna Array ◽  
Antenna Arrays ◽  
Particle Swarm ◽  
Optimization Method ◽  
Entropy Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Cross Entropy Method ◽  
Improved Pso

Purpose – The aim of this paper is to explore the potential of particle swarm optimization (PSO) methods for minimizing the sidelobe levels (SLL) and placing null at arbitrary angles of a nonlinear antenna array. Design/methodology/approach – An improved PSO algorithm is designed. Findings – The improved PSO method is an efficient and robust global optimizer for minimizing the SLL and placing null at arbitrary angles of a nonlinear antenna array. Originality/value – Some improvements, such as the design of some new formulae for both position and velocity updating, the introduction of an age variable, and the devise of an intensification searches using the cross entropy method, are proposed.

scholarly journals Null Steering in Failed Antenna Arrays

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Om Prakash Acharya ◽  
Amalendu Patnaik ◽  
Sachendra N. Sinha
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Correction Procedure ◽  
Swarm Optimization ◽  
Array Pattern ◽  
Null Steering ◽  
Simulation Results ◽  
Pattern Simulation

Antenna array pattern nulling is desirable in order to suppress the interfering signals. But in large antenna arrays, there is always a possibility of failure of some elements, which may degrade the radiation pattern with an increase in side lobe level (SLL) and removal of the nulls from desired position. In this paper a correction procedure is introduced based on Particle Swarm Optimization (PSO) which maintains the nulling performance of the failed antenna array. Considering the faulty elements as nonradiating elements, PSO reoptimizes the weights of the remaining radiating elements to reshape the pattern. Simulation results for a Chebyshev array with imposed single, multiple, and broad nulls with failed antenna array are presented.

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.

A review on optimization of antenna array by evolutionary optimization techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
D.D. Devisasi Kala ◽  
D. Thiripura Sundari
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Optimization Algorithm ◽  
Firefly Algorithm ◽  
Side Lobe ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Content Type ◽  
Gain Enhancement ◽  
Whale Optimization

PurposeOptimization involves changing the input parameters of a process that is experimented with different conditions to obtain the maximum or minimum result. Increasing interest is shown by antenna researchers in finding the optimum solution for designing complex antenna arrays which are possible by optimization techniques.Design/methodology/approachDesign of antenna array is a significant electro-magnetic problem of optimization in the current era. The philosophy of optimization is to find the best solution among several available alternatives. In an antenna array, energy is wasted due to side lobe levels which can be reduced by various optimization techniques. Currently, developing optimization techniques applicable for various types of antenna arrays is focused on by researchers.FindingsIn the paper, different optimization algorithms for reducing the side lobe level of the antenna array are presented. Specifically, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search algorithm (CSA), invasive weed optimization (IWO), whale optimization algorithm (WOA), fruitfly optimization algorithm (FOA), firefly algorithm (FA), cat swarm optimization (CSO), dragonfly algorithm (DA), enhanced firefly algorithm (EFA) and bat flower pollinator (BFP) are the most popular optimization techniques. Various metrics such as gain enhancement, reduction of side lobe, speed of convergence and the directivity of these algorithms are discussed. Faster convergence is provided by the GA which is used for genetic operator randomization. GA provides improved efficiency of computation with the extreme optimal result as well as outperforming other algorithms of optimization in finding the best solution.Originality/valueThe originality of the paper includes a study that reveals the usage of the different antennas and their importance in various applications.

Sign in / Sign up

Export Citation Format

Share Document