Performance Evaluation of PSO and ACO-Based Clustering Algorithms in MANET with QoS Perspective

In mobile Adhoc Network and various network optimization situations, swarm intelligence strategies like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are crucial for enhancing Quality of Service (QoS) metrics. This research examines the efficacy of PSO and ACO in enhancing important QoS metrics, such as energy usage, latency, packet delivery ratio (PDR), and throughput. The study highlights the advantages of bioinspired optimization techniques for enhancing network performance and identifies the trade-offs among different QoS parameters. The findings provide direction for researchers and engineers in selecting the appropriate optimization algorithm based on specific application requirements. Future investigations could focus on hybrid approaches that integrate PSO and ACO to achieve an optimal balance between energy efficiency and network performance. The findings indicate that ACO excels over PSO in the majority of parameters, showing a lower delay of 38.92 ms compared to 46.75 ms, a higher PDR at 89.62% versus 85.43%, and an increase in throughput at 579.83 kbps against 532.76 kbps. Nevertheless, PSO displays marginally better energy efficiency, utilizing 50.42 mJ in contrast to ACO's 52.85 mJ. Both algorithms were executed and tested in MATLAB to assess their performance. These results underscore the promise of ACO for applications that demand greater network reliability and efficiency, while PSO continues to be a useful choice for scenarios with limited energy resources.