An Enhanced white shark optimization algorithm for unmanned aerial vehicles placement

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dc.contributor.authorSaadi, Amylia Ait
dc.contributor.authorSoukane, Assia
dc.contributor.authorMeraihi, Yassine
dc.contributor.authorGabis, Asma Benmessaoud
dc.contributor.authorRamdane-Cherif, Amar
dc.contributor.authorYahia, Selma
dc.date.accessioned2024-02-26T08:47:10Z
dc.date.available2024-02-26T08:47:10Z
dc.date.issued2024
dc.description.abstractIn 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).en_US
dc.identifier.isbn978-3-031-34458-9
dc.identifier.issn2522-8595
dc.identifier.urihttps://doi.org/10.1007/978-3-031-34459-6_3
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-031-34459-6_3
dc.identifier.urihttps://dspace.univ-boumerdes.dz/handle/123456789/13565
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofseriesFuture 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
dc.subjectElite opposition-based learningen_US
dc.subjectUAVs placementen_US
dc.subjectUnmanned Aerial Vehicles (UAVs)en_US
dc.subjectWhite Shark Optimization Algorithmen_US
dc.titleAn Enhanced white shark optimization algorithm for unmanned aerial vehicles placementen_US
dc.typeBook chapteren_US

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