A review on optimization of antenna array by evolutionary optimization techniques

Kala, D.D. Devisasi; Sundari, D. Thiripura

doi:10.1108/ijius-08-2021-0093

Cited by 7 publications

(2 citation statements)

References 79 publications

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“…The second area that has received little attention is the design of quantized control of the antenna array [17]. The recent review dedicated to the control synthesis methods was published in [18].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Quantized Control Synthesis Methods of Antenna Arrays

et al. 2022

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There is a great potential in small satellite technology for testing new sensors, processes, and technologies for space applications. Antennas need careful design when developing a small satellite to establish stable communication between the ground station and the satellite. This work is motivated by the design of an antenna array for a future rotatorless base station for the VZLUSAT group of Czech nano-satellites. The realized antenna array must cover a relatively broad range of elevation and azimuth angles, and the control must be fast enough to track the satellite in low Earth orbits. The paper deals with possibilities of synthesis of quantized control of the antenna array. It compares quantization influence for well-known deterministic synthesis methods. It shows the method for decreasing computational cost of synthesis using optimization approach and presents the multi-criteria optimization as a tool for reaching required radiation pattern shape and low sensitivity to quantization at the same time.

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Section: Introductionmentioning

confidence: 99%

Performance Comparison of Quantized Control Synthesis Methods of Antenna Arrays

et al. 2022

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“…It is referred to (Karaboga and Basturk, 2008) for the specifics of the artificial bee colony. The ABC algorithm has been applied to a variety of real-world problems since its inception, including chaotic system parameter estimation (Gu et al, 2017), reconfigurable antenna array (Kala and Sundari, 2021), leaf-constrained minimum spanning tree problem (Akay et al, 2021), digital IIR filters (Karaboga, 2009;Agrawal et al, 2021), and job shop scheduling (Alzaqebah et al, 2021). Meanwhile, the ABC algorithm has been extended to address multi-objective, constrained optimization problem, and other kinds problems in various fields (Bansal et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Bee Colony Algorithm for Multimodal Optimization Based on Crowding Method

Wang

Zhang

2022

Journal of Organizational and End User Computing

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Many real-world problems can be transformed into multimodal functional optimization. Each of these problems may include several globally optimal solutions, rendering the solution of the problem progressively more difficult. In our study, we present a crowding artificial bee colony, called IABC, which exploits the concepts of crowding and explores search solutions. A crowding approach formed in niches is used to make it capable of tracking and maintaining multiple optima, resulting in good convergence of the search space with a better chance of locating multiple optima. Two new solution search mechanisms are proposed to increase population diversity and explore new search spaces. Experiments were carried out on 14 benchmark functions selected from previous literature. The results of our experiments show that our method is both effective and efficient. In terms of the quality of the success rate, the average number of optima found, and the maximum peak ratio, IABC performs better, or at least comparably, to other cutting-edge approaches.

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