# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 2 Literature Review  
Several works have been developed in the field of UAV path-planning algorithms, highlighting different applications, optimization approaches, and advancements in the field. The author in [41] discussed the development of a path-planning technique for UAVs in target coverage problems. The paper presented an algorithm that combined a genetic algorithm with three different approaches during the initial population phase to address altitude limitations and ensure safe paths. Another framework presented in [42], called Sparrow Particle Swarm Algorithm (SPSA), was proposed to solve path planning in UAVs. To reduce blind search, this framework enhances initialization and updated positions, and gives importance to the start-end line. The target reach in deadlocked areas is improved by utilizing adaptive variable speed escapes. Simplification and interpolation techniques using cubic splines are employed to enhance path smoothness for real flight trajectories. In a survey presented in [43], an overview of 3D path-planning algorithms for UAVs was provided, categorizing them into five categories: node-based algorithms, bio-inspired algorithms, mathematical model-based algorithms, sampling-based algorithms, and multi-fusion-based algorithms. Another survey in [44] focused on computational intelligence (CI) methods for UAV path planning, specifically exploring time domain-based methods in both 2D and 3D environments. Additionally, the work in [45] provided a comprehensive survey of UAV path-planning algorithms, with a particular emphasis on applications, such as disaster management, communication networks, and general scenarios. The survey highlighted that 67% of UAV path-planning algorithms focus on general applications, 22% on disaster management, and only 11% focus on communication networks. Additionally, researchers proposed an integrated IoT-powered UAV-based smart city management system to address earthquakes, disasters, and bushfires. In [46], a multi-frequency vibrational genetic algorithm (mVGA) was introduced for UAV path planning in city and sinusoidal environmental models. The algorithm incorporated a new mutation application strategy and emphasized both local random and global random diversity. The initial population phase of the algorithm integrated conceptsfrom the Voronoi diagram and clustering methods. The mVGA algorithm demonstrated reduced computational time compared to three other genetic algorithms. The PSO algorithm has played an important role in numerous UAV path-planning works due to its effectiveness in optimization problems [47]. Furthermore, PSO offers several advantages that make it suitable for real-world applications, relative to other path-planning algorithms. These advantages are fast convergence speed, the capability to search for optimal paths in large search spaces, robustness, and the ability to handle dynamic environments. Additionally, PSO is easy to implement and can be adapted to different path-planning problems by adjusting the fitness function or the acceleration coefficients. Inspired by these advantages, ref. [48] proposed a spherical vector-based particle swarm optimization (SPSO) approach. The approach formulated a cost function based on path length to convert path-planning into an optimization problem that incorporated all constraints for safe and feasible UAV operation. In [49], an improved PSO algorithm was presented for real-time UAV path planning. A chaos strategy was utilized to prevent particles from converging to local optima and the path quality was enhanced using the Dijkstra algorithm. The work in [50] introduced a path-planning approach that considered the dynamic properties of the UAV and the complexity of real 3D environments. It combined two non-deterministic algorithms, the genetic algorithm (GA) and PSO, to address complexity and reduce computation time. Furthermore, the GBPSO, an enhanced version of PSO, was proposed in [51] to improve the performance of 3D path planning for UAVs.