Genetic Algorithm-Based Optimization of UAV Placement for Fixed and Dynamic Target Scenarios

In order to maximize target coverage, reduce energy consumption, and guarantee quality of service (QoS), this work investigates the optimization of Unmanned aerial vehicle (UAV) deployments using Genetic Algorithm (GA). We perform a comparison between the cases of dynamic targets with predetermined mobility patterns and fixed targets. According to our research, dynamic target situations need dynamic repositioning, which raises energy consumption but may boost coverage adaptability, whereas fixed target scenarios enable steady and energy-efficient UAV deployments.