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| Titre: | An Enhanced white shark optimization algorithm for unmanned aerial vehicles placement |
| Auteur(s): | Saadi, Amylia Ait
Soukane, Assia
Meraihi, Yassine
Gabis, Asma Benmessaoud
Ramdane-Cherif, Amar
Yahia, Selma |
| Mots-clés: | Elite opposition-based learning
UAVs placement
Unmanned Aerial Vehicles (UAVs)
White Shark Optimization Algorithm |
| Date de publication: | 2024 |
| Editeur: | Springer Nature |
| Collection/Numéro: | Future Research Directions in Computational Intelligence : EAI/Springer Innovations in Communication and Computing /3rd EAI International Conference on Computational Intelligence and Communications, CICom 2022, Springer, Cham;pp. 27 - 42 |
| Résumé: | In this chapter, we propose an Elite Opposition-Based White Shark Optimization (ELWSO) Algorithm, for tackling the Unmanned Aerial Vehicles (UAVs) Placement problem in smart cities. The proposed EWSO scheme is based on the incorporation of the Elite opposition-based strategy to ameliorate the optimization efficiency of the original WSO. EWSO was assessed in terms of fitness, coverage, and connectivity metrics under 23 cases with different numbers of UAVs and users. The results of simulated experiments, conducted using MATLAB 2021b version, revealed that the EWSO algorithm outperforms the basic WSO, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bat Algorithm (BA). |
| URI/URL: | https://doi.org/10.1007/978-3-031-34459-6_3
https://link.springer.com/chapter/10.1007/978-3-031-34459-6_3
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/13565 |
| ISBN: | 978-3-031-34458-9 |
| ISSN: | 2522-8595 |
| Collection(s) : | Communications Internationales
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