Optimization of Linear Antenna Array Using Binary Cat Swarm Optimization (BCSO)

Author(s): Krishanu Kundu*, Narendra Nath Pathak, Atul Kumar Dwivedi

Journal Name: International Journal of Sensors, Wireless Communications and Control

Volume 10 , Issue 5 , 2020


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Abstract:

Background: Antennas serve a vital aspect in modern wireless communication. Designing antennas with very high directivity is very important to solve the long-distance communication problem. Though regularly excited and evenly spaced linear antenna arrays delivers good directivity but also leads to problem related to higher side lobe. For diminishing the level of side lobe, the array can be constructed either by amending the excitation amplitudes non-uniformly with all physical spaces of the antenna elements keeping consistent or vice versa.

Methods: In this work, a novel mathematical objective function has been formulated. The objective function has been solved using a recently developed evolutionary optimization technique, i.e., Binary cat swarm optimization. So for better efficiency, the cat swarm optimization technique has been modified.

Results: The results have been compared with the popular algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) in terms of Side Lobe Level (SLL), achieved fitness and execution time. The proposed algorithm achieves 0.5dB, 1.7 dB and 3dB smaller SLL as compared to CSO, PSO and GA respectively. In addition to SLL, achieved fitness using BCSO is in the range of 0.001 which is smallest among the compared algorithms.

Conclusion: It was found that the modified version namely binary cat swarm optimization algorithm outperform other well-known evolutionary optimization algorithms.

Keywords: Antenna array, linear antenna array, directivity, side lobe level, artificial intelligence, optimization, evolutionary algorithms, genetic algorithm, binary cat swarm optimization.

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Article Details

VOLUME: 10
ISSUE: 5
Year: 2020
Published on: 10 July, 2019
Page: [669 - 676]
Pages: 8
DOI: 10.2174/2210327909666190710121921
Price: $25

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