# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 2 Related Work
[PERSON]'s paper provides evidence that potential fields can be used to generate a static mesh network similar to that generated by Delaunay triangulation or the circle packing theorem. [PERSON] [28] presented a centralized algorithm and a potential-field-based distributed algorithm for UAV deployment while maintaining connectivity among UAVs. However, the authors considered only two different types of user distributions: uniformly random and in three clusters spread around the AOI with a command center in the middle. All of these approaches work well in a relatively uniform distribution of users but not as well when users are distributed non-uniformly. Machine-learning-based approaches have been used for optimal UAV deployment [29]. Genetic algorithms have been particularly popular for deploying static networks in the AOI. For example, [PERSON] [30] presented a multi-layout multi-subpopulation genetic algorithm that optimizes coverage, fault-tolerance, and redundancy. [PERSON] [31] used a variable-length genetic algorithm to optimize the area coverage and deployment cost using non-homogeneous sensors. [PERSON] [32] proposed a GA-based deployment algorithm that maximizes longevity by activating and deactivating sensor nodes. [PERSON] [33] developed a GA-based area optimizer that maximizes the covered area. [PERSON] [34] used a hybrid of particle swarm optimization (PSO) and Voronoi diagrams to find the optimal deployment of nodes/UAVs for best coverage, and [PERSON] [35] used the artificial bee colony (ABC) algorithm to maximize throughput. [PERSON] [36] used the Harris hawk's optimization (HHO) algorithm to find the optimal sensor placement for maximum coverage and connectivity. These approaches typically encode locations in chromosomes and are not generalizable. Table 1 compares different network deployment methods based on their control mechanism, applicability toward uniform and non-uniform user distributions, and generalizability, providing readers with a clear overview of the current state of research in this field. Unlike previous works that solely focus on UAV position optimization, this paper introduces a genetic algorithm to evolve potential field parameters that guide real-time collision-free UAV movement toward optimal positions. By fine-tuning the potential parameters, the proposed algorithm can optimally place UAVs regardless of the distribution of users and other UAVs in the area of interest. The resulting UAV network is deployed in real-time and is highly generalizable. This approach not only provides UAV positions but also ensures safe and efficient movement to these positions. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Papers** & **Methods** & **Network** & **Control** & **User Distribution** & **Generalizable** \\ \hline  & & **Static (S)** & **Centralized (CC)** & **Uniform (U)** & \\  & & **Dynamic (D)** & **Distributed (DC)** & **Non-Uniform (NU)** & \\ \hline  
HO & S & CC & U & No \\ \hline
**proposed** & GA and PF & D & DC & U, NU & **Yes** \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of wireless network deployment methods. The next section presents the problem formulation, fitness computation, and an elitist genetic algorithm.