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A comparison of swarm-based optimization algorithms in linear antenna array synthesis

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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 a low side lobe level (SLL) at a desired half power beam width 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 metaheuristic algorithms including particle swarm optimization (PSO), artificial bee colony (ABC), mayfly algorithm (MA) and jellyfish search (JS) are compared to determine the 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 the performance of the novel algorithms, MA and JS, is better than that of the well-known PSO and ABC methods.

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Authors and Affiliations

  1. Department of Electricity and Energy, Vocational College, Kayseri University, Kayseri, Turkey

    Ali Durmus

  2. Department of Computer Technologies, Vocational College, Kayseri University, Kayseri, Turkey

    Rifat Kurban

  3. Department of Natural Sciences, Engineering & Architecture and Design Faculty, Kayseri University, Kayseri, Turkey

    Ercan Karakose

Authors
  1. Ali Durmus
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  2. Rifat Kurban
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  3. Ercan Karakose
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Correspondence to Ali Durmus.

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Durmus, A., Kurban, R. & Karakose, E. A comparison of swarm-based optimization algorithms in linear antenna array synthesis. J Comput Electron 20, 1520–1531 (2021). https://doi.org/10.1007/s10825-021-01711-w

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  • DOI: https://doi.org/10.1007/s10825-021-01711-w

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