Emerging swarm intelligence algorithms and their applications in antenna design: The gwo, woa, and ssa optimizers

Achilles D. Boursianis, Maria S. Papadopoulou, Marco Salucci, Alessandro Polo, Panagiotis Sarigiannidis, Konstantinos Psannis, Seyedali Mirjalili, Stavros Koulouridis, Sotirios K. Goudos

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear antenna arrays for optimizing the peak sidelobe level (pSLL). Numerical tests show that the WOA outperforms the GWO and the SSA algorithms, as well as the well-known Particle Swarm Optimizer (PSO), in terms of average ranking. Finally, the WOA is exploited for solving a more computational complex problem concerned with the synthesis of an dual-band aperture-coupled E-shaped antenna operating in the 5G frequency bands.

Original languageEnglish
Article number8330
JournalApplied Sciences (Switzerland)
Volume11
Issue number18
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Antenna design
  • Aperture-coupled antenna
  • Grey wolf optimizer
  • Meta-heuristics
  • Nature-inspired algorithms
  • Optimization technique
  • Salp swarm algorithm
  • Swarm intelligence
  • Whale optimization algorithm

Fingerprint

Dive into the research topics of 'Emerging swarm intelligence algorithms and their applications in antenna design: The gwo, woa, and ssa optimizers'. Together they form a unique fingerprint.

Cite this