Efficient Coronavirus Herd Immunity Optimizer for the UAV Base Stations Placement Problem - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Efficient Coronavirus Herd Immunity Optimizer for the UAV Base Stations Placement Problem

Sylia Mekhmoukh Taleb
  • Fonction : Auteur correspondant
  • PersonId : 1134871

Connectez-vous pour contacter l'auteur
Yassine Meraihi
Selma Yahia
  • Fonction : Auteur
Asma Gabis
Dalila Acheli
  • Fonction : Auteur

Résumé

This paper proposes an improved version of the Coronavirus Herd Immunity Optimizer (CHIO) algorithm, called RFDB-CHIO, for solving the Unmanned Aerial vehicle carried Base Stations (UAV-BSs) placement problem in 5G networks. The proposed RFDB-CHIO is based on the integration of the Roulette Fitness Distance Balance (RFDB) selection mechanism into the original CHIO algorithm. RFDB-CHIO is validated in terms of user coverage and mean coverage radius under 16 scenarios with different numbers of drones and users. The simulation results demonstrated that RFDB-CHIO obtained better results than CHIO, Whale optimization algorithm (WOA), and Grey Wolf Optimization (GWO) algorithms.
Fichier non déposé

Dates et versions

hal-04122368 , version 1 (08-06-2023)

Identifiants

Citer

Sylia Mekhmoukh Taleb, Yassine Meraihi, Selma Yahia, Amar Ramdane-Cherif, Asma Gabis, et al.. Efficient Coronavirus Herd Immunity Optimizer for the UAV Base Stations Placement Problem. 7th International Symposium on Modeling and Implementation of Complex Systems, MISC 2022, 2022, unknow, France. pp.292-305, ⟨10.1007/978-3-031-18516-8_21⟩. ⟨hal-04122368⟩
23 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More