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Veuillez utiliser cette adresse pour citer ce document : http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/13565

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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