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


Journal of Computational Electronics, vol.20, no.4, pp.1520-1531, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 4
  • Publication Date: 2021
  • Doi Number: 10.1007/s10825-021-01711-w
  • Journal Name: Journal of Computational Electronics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC
  • Page Numbers: pp.1520-1531
  • Keywords: Linear antenna array synthesis, Side lobe level, Particle swarm optimization, Artificial bee colony algorithm, Mayfly algorithm, Jellyfish search algorithm, DIFFERENTIAL EVOLUTION
  • Kayseri University Affiliated: Yes


© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.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.