An Efficient Multipath Transmission Technique using Enhanced Hybrid Metaheuristic Ant Lion Optimization in Mobile Ad hoc Networks (MANETs)

Abstract

Mobile Ad hoc Networks (MANETs) have emerged as a flexible networking paradigm; however, they are associated with several challenges, including low throughput, poor packet delivery ratio, high energy consumption, increased latency, and significant routing overhead. The Enhanced Hybrid Metaheuristic Ant Lion Optimization (EHMALO) method is suggested as a solution to these limitations. EHMALO applies the RSSI-based Link Expiration Time (R-LET), Context-Aware Dynamic Weight Adjustment Model (C-DWAM), and Dual Phase Exploration-Exploitation Technique (D-PET). These techniques are incorporated along with an enhanced Ant Colony Optimization (ACO) algorithm that uses the foraging techniques to explore the path, and an improved Lion Optimization Algorithm (LOA) hunting strategy is chosen to optimize the routing path. The Ad hoc On-demand Multipath Distance Vector (AOMDV) routing algorithm is used in conjunction with the combined algorithm. To select the optimal path, an adaptive adjustment parameter for the feedback mechanism is considered after the iterations. This suggested approach is then used to assess performance metrics such as packet delivery ratio, throughput, latency, energy efficiency, and routing overhead. The effectiveness of the proposed EHMALO algorithm is illustrated using Network Simulator-3 (NS-3) simulations for various network settings. The results are compared to traditional techniques such as ACO, LOA, Particle Swarm Optimization-Genetic Algorithm (PSO-GA), and Hybrid Ant Lion Optimization (HALO) with AOMDV techniques. The Quality of Service (QoS) metrics have improved significantly as per the results The suggested EHMALO method performs better, surpassing the drawbacks of conventional bio-inspired methods. When route discovery and route maintenance are considered, EHMALO maximizes energy efficiency, throughput, and packet delivery ratio while minimizing the routing overhead ratio and end-to-end delay. Thus, the proposed EHMALO performs significantly better than ACO with AOMDV, LOA with AOMDV, PSO-GA, and HALO algorithms. The outcomes show that EHMALO has the ability to improve MANET performance in dynamic contexts in a dependable and scalable manner.