Saadi, Amylia AitSoukane, AssiaMeraihi, YassineGabis, Asma BenmessaoudRamdane-Cherif, AmarYahia, Selma2024-02-262024-02-262024978-3-031-34458-92522-8595https://doi.org/10.1007/978-3-031-34459-6_3https://link.springer.com/chapter/10.1007/978-3-031-34459-6_3https://dspace.univ-boumerdes.dz/handle/123456789/13565In 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).enElite opposition-based learningUAVs placementUnmanned Aerial Vehicles (UAVs)White Shark Optimization AlgorithmBook chapter