## 2 Related Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Optimization Techniques-Based UAV Path Planning**} \\ \hline
**Ref** & **Proposed Algorithm** & **Advantages** & **Limitations** \\ \hline \multirow{8}{*}{[35]} & An improved Equilibrium Optimizer & The improved EO algorithm & The effectiveness of the improved \\  & (EO) for unmanned aerial vehicle & demonstrated improved performance & EO algorithm may be limited by the \\  & (UAV) path planning, which includes a & in terms of finding optimal paths for & complexity of the environment and \\  & modified search strategy and a new & UAVs compared to other & the number of waypoints to be \\  & balance operator. & optimization algorithms. & visited. Additionally, the algorithm \\  & & optimization algorithms. & may require tuning its parameters \\  & & for optimal performance. \\ \hline \multirow{8}{*}{[36]} & Distributed 3D path planning for & Provides a distributed approach to & The proposed method may require \\  & multiple UAVs based on the Particle & path planning for multiple UAVs & a large number of UAVs to achieve \\  & Swarm Optimization (PSO) algorithm & with high accuracy and efficiency, as & full-area surveillance, and it may \\  & for full-area surveillance. & well as full-area surveillance with & not consider dynamic obstacles in \\  & & reduced time and cost. & real-time. \\ \hline \multirow{8}{*}{[37]} & Parallel Cooperative Coevolutionary & Improved convergence speed and & The proposed CCGWO method \\  & Grey Wolf Optimizer (CCGWO) for & solution quality compared to other & may require significant \\  & path planning of Unmanned Aerial & optimization algorithms, and the & computational resources to run, and \\  & Vehicles (UAVs) in complex & ability to handle complex & it may not perform well in cases \\  & environments. & environments with dynamic & where the search space is not well \\  & & obstacles. & defined. \\ \hline \multirow{8}{*}{[38]} & Path planning for multiple Unmanned & Improved efficiency and accuracy in & The proposed GWA-based method \\  & Aerial Vehicles (UAVs) with time & path planning with time constraints, & may struggle to find optimal \\  & windows using the Grey Wolf & as well as the ability to optimize & solutions in complex environments \\  & Algorithm (GWA). & multiple UAVs simultaneously. & with many obstacles, and it may not \\  & & mobility to handle complex & be suitable for real-time \\  & & mobility to handle complex & applications due to the \\  & & mobility to handle complex & computational resources required. \\ \hline \multirow{8}{*}{[39]} & An diagonform for path planning for & The proposed CPSO algorithm is & The GPSO algorithm’s performance \\  & Unmanned Aerial Vehicles (UAVs) & efficient at finding safe paths for & may be affected by the selection of \\  & based on an improved Harris Hawks & can handle complex environments & hyperparameters, and it may \\  & Optimization (IHHO) algorithm. & with multiple objectives. & struggle with time-constrained \\  & & mobility to handle complex & applications as the search process \\  & Adaptive Cylinder Vector Particle & The proposed ACVPSO-DE algorithm & The proposed algorithm may be impacted by the selection of \\  & Swarm Optimization with Differential & provides high-quality solutions for & hyperparameters, and it may \\  & Evolution (ACVPSO-DE) for & finding safe paths for UAVs while & struggle with complex \\  & unmanned aerial vehicle (UAV) path & avoiding obstacles in real-time & environments with multiple \\  & planning. & applications. & objectives. \\ \hline \multirow{8}{*}{[41]} & A hybrid algorithm based on the Grey & The proposed GWO-DE algorithm & The proposed algorithm may \\  & Wolf optimizer and Differential & provides an efficient and effective & struggle with complex \\  & Evolution (GWO-DE) for unmanned & solution for finding safe paths for & environments with multiple \\  & aerial vehicle (UAV) path planning. & UAVs while avoiding obstacles in & objectives, and the selection of \\  & & dynamic environments. & hyperparameters may impact the \\  & & performance. \\ \hline \hline \end{tabular}
\end{table}