## 8 Challenges in Using Evolutionary Approach for Ad Hoc Networks  
Evolutionary algorithms have been used in mobile ad hoc networks for the past decade [52; 72]. In these complicated networks, however, there are still many optimization issues that can be fixed using an appropriate evolutionary algorithm. Different optimization techniques of evolutionary algorithms such as evolutionary algorithms and simultaneous evolutionary algorithms are frequently suggested. When evolutionary algorithms require powerful computer capital, parallelizing the evolutionary functions will allow genetic algorithms to be executed using various processors and core processors, thereby decreasing the time needed for computing. For instance, the DEAP Python [88] module makes it easy for consumers to compare progressive algorithms with the SCOOP module. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Protocol** & **Year** & **Mobility Model** & **Density** & **Broadcasting Ability** & **Traffic Model** & **Multi-Media Support** & **Coverage** & **Simulator** \\ \hline CFR MAC [35] & 2014 & Highway & High & Yes & Bi-directional & No & Low & N/A \\ HER-MAC [36] & 2014 & Highway & Low & Yes & Bi-directional & Yes & N/A & MATLAB \\ VeMAC [57] & 2011 & Highway/ & High & Yes & Bi-directional & Yes & Short & MATLAB \\ ATSA [88] & 2013 & Highway & Medium & No & Bi-directional & No & N/A & MATLAB \\ CS-TDMA [90] & 2014 & Highway & Medium & Yes & Bi-directional & Yes & N/A & MATLAB \\ VeOSMAC [40] & 2007 & Highway & Low & No & Unidirectional & Yes & Medium & N2S \\ STDMA [41] & 2009 & Highway & High & Yes & Bi-directional & No & Long & MATLAB \\ SDFTMAC [42] & 2009 & Highway & Low & N/A & Unidirectional & Yes & N/A & N/A \\ DAMAC [43] & 2010 & Highway & Medium & Yes & Unidirectional & N/A & Short & N2S \\ ADFA [87] & 2020 & Highway & Medium & Yes & Unidirectional & No & Yes & NETSIM \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MAC protocols for VANET. Another task is to use evolutionary tools in actual test beds to confirm and validate the outcomes of the experiment. It can also be an exciting option in decreasing the number of moments needed by experiments, which is likely the major bottleneck when introducing adaptive tools to ad hoc multi-hop networks. For example, ns-2, although it is the most renowned mobile multi-hop ad hoc network event-based network simulator, it can take up to several minutes (based on the computational energy of the simulation running). This may be impractical in some instances. A subject that may be researched further in ad hoc mobile multi-hop networks is genetic programming. Similar to genetic algorithms, genetic programming works by using computer programs. Theoretically, genetic programming can provide the optimal broadcast tools or implementation strategies [89]. Genetic programming, however, also has certain disadvantages such as calculation time or unnecessary development. Also prevalent is the use of fully integrated evolutionary algorithms [90; 91]. Most studies reviewed in this paper have employed distributed implementations. However, comprehensive data are highly expensive when it comes to message exchange in a portable multi-hop network. Figure 4 shows the widely used simulators identified in the study. The analysis shows that ns-2 is the most widely used simulator for ad hoc networks, followed by MATLAB. There are many works which have used custom simulators built by the authors for their research. Vehicle movement requires the constant sharing of data to renew data on the present state of the network. The dispersed application of evolutionary algorithms should contribute to a scarcity of funds because portable mobile wireless phones usually have fewer assets than laptops and PCs. The primary benefit of a dispersed application is that the algorithm works remotely so that the nodes can be adapted to the different local network circumstances.