@article{Pliatsios2021b,

title = {Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches},

author = {Dimitrios Pliatsios and Sotirios K. Goudos and Thomas Lagkas and Vasileios Argyriou and Alexandros Apostolos A. Boulogeorgos and Panagiotis Sarigiannidis},

url = {https://www.researchgate.net/publication/356863442_Drone-Base-Station_for_Next-Generation_Internet-of-Things_A_Comparison_of_Swarm_Intelligence_Approaches},

doi = {10.1109/OJAP.2021.3133459},

issn = {2637-6431},

year  = {2021},

date = {2021-12-07},

journal = {IEEE Open Journal of Antennas and Propagation},

abstract = {The emergence of next-generation internet-of-things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.},

keywords = {Drone base station, evolutionary algorithms, mobile communications, Optimization methods, Swarm intelligence},

pubstate = {published},

tppubtype = {article}

}

@article{Boursianis2021c,

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

author = { A.D. Boursianis and M.S. Papadopoulou and M. Salucci and A. Polo and P. Sarigiannidis and K. Psannis and S. Mirjalili and S. Koulouridis and S.K. Goudos},

url = {https://www.researchgate.net/publication/354446201_Emerging_Swarm_Intelligence_Algorithms_and_Their_Applications_in_Antenna_Design_The_GWO_WOA_and_SSA_Optimizers},

doi = {10.3390/app11188330},

year  = {2021},

date = {2021-01-01},

journal = {Applied Sciences (Switzerland)},

volume = {11},

number = {18},

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

keywords = {Antenna design, Aperture-coupled antenna, Grey wolf optimizer, Meta-heuristics, Nature-inspired algorithms, Optimization technique, Salp swarm algorithm, Swarm intelligence, Whale optimization algorithm},

pubstate = {published},

tppubtype = {article}

}