# Routing Enhancement in MANET Using Particle Swarm Algorithm
## 3 The Proposed Method
Various routing systems protocols manage paths differently, often balancing between complexity, efficiency,

Figure 4: Throughput of proposed PSO, Fixed-PSO, and AODV. Figure 5: E2E delay of proposed PSO, Fixed-PSO, and AODV. and redundancy. Figure 6 shows the number of valid paths of AODV, Fixed-PSO, and the proposed PSO algorithm as the network size increases. AODV discovers a single valid path while PSO discovers multiple valid paths simultaneously; PSO is more suitable for large-scale networks where different criteria needed be balanced. AOOV finds a path on demand, and PSO continuously enhances paths. The proposed PSO consistently outperforms the AODV, and Fixed-PSO as the network size scales up. Figure 7 shows a comparison between the proposed PSO and Fixed-PSO in terms of their average execution time across different network sizes. In both algorithms, the execution time increases as the number of nodes increases. The lines are very close to each other, suggesting similar performance. There is a slight divergence between the two algorithms at higher node counts (250-300). The proposed PSO has marginally higher execution times at the maximum node count. The nearly overlapping lines indicate that algorithms have very similar computational efficiency across different network sizes, with minimal performance differences. Figure 6: Number of valid paths for proposed PSO, Fixed-PSO, and AODV. Figure 7: Execution time of proposed PSO and Fixed-PSO. ## 7 Conclusions

Particle Swarm Optimization (PSO) was employed in MANET routing, resulting in a promising solution. The PSO algorithm consists of particles that move in the search space looking for the appropriate solution. The PSO algorithm evaluates each solution using a fitness function to find the best solution. In this paper, an improved PSO routing algorithm was proposed to enhance MANET routing. The proposed algorithm improves MANET performance by considering additional metrics for finding the optimal path. The metrics considered in this work were short distance, path delay, and energy consumption. Therefore, results show the proposed EPSO performs better than traditional PSO; the comparison considered network throughput, the PDR, and the number of valid paths. The results show the proposed algorithm is more suitable for large networks. In the future, we will test the potential of EPSO in different network scenarios and applications. O.A. : Conceptualization, methodology, formal analysis, resources, data curation, and writing--original draft preparation. E.K. : Conceptualization, writing--review, editing,and supervision. A.A.: writing--review and editing. R.A.: Conceptualization, writing--review and editing, and supervision. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflicts of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Mobile ad-hoc networks applications and its challenges. _Commun. Netw._**2016**, \(8\), 131-136. [CrossRef]
* (2) [PERSON]; [PERSON] Introduction to MANET. _Int. Res. J. Eng. Technol._**2018**, \(5\), 17-20. * (3) [PERSON]; [PERSON] An overview of MANETs: Applications, characteristics, challenges and recent issues. _Int. J. Eng. Adv. Technol. (JELAT)_**2017**, \(6\),128-133
* (4) [PERSON]; [PERSON] IRDFPR-CMDNN An energy efficient and reliable routing protocol for improved data transmission in MANET. _Sci. Tech. J. Inf. Technol. Meth. Opt._**2022**, _22_, 364-375. [CrossRef]
* (5) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]. A critical review of Optimization MANET routing protocols. _Wasit J. Comput. Math. Sci._**2022**, \(1\), 44-54. [CrossRef]
* (6) [PERSON]; [PERSON] Survey on power-aware optimization solutions for MANETs. _Electronics_**2020**, \(9\), 1129. [CrossRef]
* (7) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A review of mobile ad hoc NETwork (MANET) Protocols and their Applications.