## 8 Challenges in Using Evolutionary Approach for Ad Hoc Networks
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Network** & **Reference** & **EA Used** & **Key Issues** & **Optimization Problem** \\ \hline \multirow{7}{*}{MANET} & [PERSON] et al. [49] & GA & To improve the range of notification. & Topology \\ \cline{2-4}  & [PERSON] et al. [50] & GA & To improve communication. & Topology \\ \cline{2-4}  & [PERSON] et al. [51] & PSO & To improve communication. & Topology \\ \cline{2-4}  & [PERSON] et al. [99] & GA with Game theory & To improve communication. & Topology \\ \cline{2-4}  & [PERSON] and [PERSON] [100] & GA with local search & To minimize the energy of the network. & Broadcast \\ \cline{2-4}  & [PERSON] et al. [101] & GA & To maximize accessibility and minimize delaying and re-transmittance packages. & Broadcast \\ \cline{2-4}  & [PERSON] et al. [102] & Parallel GA & To enhance exposure and reduce power, retransmission and limit the number of channels. & Broadcast \\ \cline{2-4}  & [PERSON] et al. [103] & GA & To reduce energy and costs. & Routing \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Key challenges in MANET and VANET. This article discusses research works in the field of VANET and MANETs which have utilized the capabilities of EAs. Research works related to ad hoc network components such as clustering, routing, and scheduling which take advantage of the effectiveness of EAs are portrayed and have been compared and shown in Tables 1-3. A brief analysis of simulators used in the study is also given in the article. Further discussions on the key challenges faced in VANET and MANET are depicted in Table 4.  
Optimization problems are ubiquitous in various fields, including engineering, finance, and computer science. Tabu search and neighborhood algorithms are two powerful techniques for solving these problems. Tabu search is a metaheuristic algorithm that utilizes a memory-based mechanism to avoid revisiting previously explored solutions [109; 110]. The algorithm utilizes a tabu list that records previously explored solutions to avoid revisiting them. This mechanism helps to prevent the algorithm from getting stuck in local optima and encourages the exploration of the entire search space. Tabu search also uses aspiration criteria to enable the algorithm to revisit previously explored solutions if they provide a significant improvement to the current solution [111; 112]. Neighborhood algorithms, on the other hand, focus on exploring the search space around the current solution. These algorithms explore different neighborhoods of the current solution to identify better solutions. Neighborhood algorithms can be divided into two categories: deterministic and stochastic. Deterministic neighborhood algorithms explore all possible solutions in the neighborhood, while stochastic neighborhood algorithms explore a random subset of the neighborhood. The effectiveness of tabu search and neighborhood algorithms has been demonstrated in various optimization problems, including the traveling salesman problem, the job shop scheduling problem, and the vehicle routing problem [110]. These techniques have also been used in various fields, including logistics, transportation, and finance.