Design of array antennas via atom search optimization

Pavani, T.; Kora, Padmavathi; Kumari, Ch. Usha; Ushasree, A.

doi:10.1016/j.matpr.2021.06.111

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…In order to optimize multiple targets at the same time, moth-flame optimization (MFO) was used to improve the far-field radiation characteristics of the antenna [55], resulting in a narrower zero beamwidth and a lower peak sidelobe level. In addition to the above methods, many emerging intelligent algorithms have been applied to the optimization design of sparse planar arrays, such as invasive weed optimization (IWO) [56], atomic search optimization (ASO) [57], brainstorming optimization (BSO) [11], and firefly algorithm (FA) [58]. More and more new methods have been introduced in this field, and it is still an important research direction to deal with sparse planar array optimization problems based on traditional intelligent algorithms.…”

Section: Sparse Area Array Optimization Based On Traditional Intellig...mentioning

confidence: 99%

Optimization Design for Sparse Planar Array in Satellite Communications

He¹,

Wang²

2023

Electronics

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The antenna is one of the key components of satellite communication load. To address the evolving requirements of future satellite communication systems, the sparse planar array has become an important device for transmitting and receiving electromagnetic waves in emerging antenna systems. The advantages of this technology include low cost, low system complexity, and robust anti-interference ability, which have attracted widespread attention within the industry. In this paper, we investigate an optimization design of sparse planar arrays in satellite communication scenarios. Firstly, we introduce the mathematical foundation of the array antennas and establish the optimization design model of the sparse planar array. Secondly, we analyze and compare the impact of different array layout methods on the sparse planar array antenna pattern, and then introduce the latest design trend of array material design. Thirdly, we review some classical optimization methods for optimizing sparse planar arrays and the recent research advancements in promising and novel methods. Lastly, on the basis of the present research status, we propose three future research directions and two critical challenges for optimal design of sparse planar arrays in satellite communication scenarios, which can facilitate the development and realization of array technology under future B5G and 6G wireless networks.

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Section: Sparse Area Array Optimization Based On Traditional Intellig...mentioning

confidence: 99%

Optimization Design for Sparse Planar Array in Satellite Communications

He¹,

Wang²

2023

Electronics

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“…Previously, the conventional Taylor's method was employed for pattern syn with low SLL [7]. With the development of mathematics, several evolutionary algor have proven effective in the electromagnetic field, many of which are applied in th tern synthesis of array antennas, such as the grey wolf optimization (GWO) algorith particle swarm optimization (PSO) algorithm [9], biogeography-based optimi (BBO) algorithm [10], firefly algorithm (FA) [11], genetic algorithm (GA) [12], diffe evolution (DE) algorithm [13], exponential chaotic differential evolution (ECDE grasshopper optimization algorithm (GOA) [15], quantum particle swarm optimi (QPSO) algorithm [16], spider monkey optimization (SMO) algorithm [17], mayfly rithm (MA) [18], wind-driven optimization (WDO) algorithm [19], invasive weed ( algorithm [20], and atom search optimization (ASO) algorithm [21]. These algor have demonstrated commendable performance within their capabilities; howeve growing demand for anti-interference in information transmission has underscore necessity for continuous research in the realm of intelligent antenna optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm

Zhao,

Liu,

2023

Electronics

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The grey wolf optimization (GWO) algorithm is a new nature-inspired meta-heuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. In this paper, the GWO algorithm is improved to overcome previous shortcomings of being easily trapped in local optima and having a low convergence rate. The proposed enhancement of the GWO algorithm utilizes logistic-tent double mapping to generate initialized populations, which enhances its global search capability and convergence rate. This improvement is called the nonlinear chaotic grey wolf optimization (NCGWO) algorithm. The performance of the NCGWO algorithm was evaluated with four representative benchmark functions. Then, the NCGWO algorithm was applied to perform an optimal pattern synthesis of linear array antennas (LAAs) using two distinct approaches: optimizing the amplitudes of the antenna currents while preserving uniform spacing and optimizing the positions of the antennas while assuming uniform excitation. To validate the effectiveness of the proposed approach, the results obtained by the NCGWO algorithm were compared with those obtained by other intelligent algorithms. Additionally, the NCGWO algorithm was applied to a more complex planar antenna array to further validate its performance. Our results demonstrate that the NCGWO algorithm exhibits superior performance regarding electromagnetic optimization problems compared to widely recognized algorithms.

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“…In addition, the required antenna parameters can be determined easily by using these applications without fabrication and measurement. [1][2][3][4] Especially heuristic algorithms provide serious convenience in long-lasting simulation processes. One of these heuristic algorithms is partial swarm optimization, which is also used in this study.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, these applications are frequently used in antenna performance enhancement such as gain, directivity and bandwidth of microwave antennas. In addition, the required antenna parameters can be determined easily by using these applications without fabrication and measurement 1–4 . Especially heuristic algorithms provide serious convenience in long‐lasting simulation processes.…”

Section: Introductionmentioning

confidence: 99%

High‐gain UWB antenna with optimized metamaterial lens layer via particle swarm optimization algorithm for satellite communication applications

Urul

2022

Int J RF Mic Comp-Aid Eng

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The effect of metamaterials, whose positions are determined with particle swarm optimization (PSO) algorithm on the performance of the defected ground structure antenna is investigated. For this purpose, four different optimization studies are carried out. In Case I, the positions of the flower shape metamaterial (FSMM) structures on the antenna are determined classically at equal intervals and added to the antenna to obtain highest gain, and it is revealed that the gain is increased by 26%. In Case II, the positions and rotation angles of the FSMMs on the antenna are determined and the gain increase by 36%. In Case III, FSMMs are placed on a layer at equal intervals with the classical method. In this case, 56% increase of antenna gain is achieved. In Case IV, the positions and rotation angles of the FSMMs is formed on the lens independently of each other with PSO. In this last case, with dimensions of 24 Â 36 mm 2 , a 77% increase in antenna gain is achieved compared to the reference antenna at the resonant frequency of 12.65 GHz in dBi. As a result, it is observed that the use of metamaterials with the help of the PSO algorithm has a higher effect on antenna performance than the use of MMs with the classical method.

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Design of array antennas via atom search optimization

Cited by 5 publications

References 19 publications

Optimization Design for Sparse Planar Array in Satellite Communications

Optimization Design for Sparse Planar Array in Satellite Communications

Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm

High‐gain UWB antenna with optimized metamaterial lens layer via particle swarm optimization algorithm for satellite communication applications

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