İnsansız hava aracı baz istasyonlarının metasezgisel algoritmalar kullanılarak 3 boyutlu yerleştirilmesi

Unmanned aerial vehicles (UAVs) have witnessed a remarkable growth in application and utilization over the past decade, owing to significant advancements in various industries and domains. Key factors contributing to the increased use of UAVs include improved battery life, payload capacity, and flight control systems. One noteworthy application of UAVs lies in their role as wireless base stations, particularly in disaster-stricken areas. However, the issue of energy consumption poses a common challenge for UAV base stations. In this study, we address this problem by employing three meta-heuristic algorithms: Animal Migration Optimization (AMO), Immune Plasma Algorithm (IPA), and Firefly Algorithm (FA). These algorithms are utilized to solve three distinct 3D UAV placement problems, namely single UAV placement, multi UAV placement, and backhaul-aware placement. Experimental studies conducted in this research demonstrate the effectiveness of meta-heuristic algorithms as a promising approach for optimizing UAV 3D placement and reducing energy consumption. Specifically, the experimental results reveal that all three algorithms perform reasonably well for single UAV placement. However, in the cases of multi UAV placement and backhaul-aware placement, the Firefly Algorithm (FA) exhibits lower performance compared to the Immune Plasma Algorithm (IPA) and Animal Migration Optimization (AMO). These findings underscore the significance of meta-heuristic algorithms in addressing UAV energy consumption challanges and optimizing 3D placement. The results obtained from this study contribute valuable insights into the practical application of meta-heuristic approaches for enhancing UAV performance in wireless base station scenarios. Furthermore, they highlight the potential for further exploration and refinement of meta-heuristic algorithms in the context of UAV technology.