Enhancement in Performance Metrics for Mobile Ad Hoc Networks (MANETs) Using Hybrid Ant Lion Optimization (HALO) Algorithm

Mobile ad hoc networks (MANETs) are constantly confronted with issues including variable quality of service (QoS) metrics, route optimization, and flexible topology. Here, a novel hybrid ant lion optimization (HALO) algorithm is proposed that integrates the cooperative foraging behavior of ant colonies from ant colony optimization and the hunting strategies of lion prides from Lion optimization algorithm along with ad hoc on‐demand multipath distance vector (AOMDV) protocol. This bio‐inspired algorithm dynamically adapts routing paths to optimize QoS metrics, including throughput, delay, packet delivery ratio, energy efficiency, and routing overhead. Simulation experiments conducted using network simulator‐3 (NS‐3) demonstrates HALO’s effectiveness in optimizing paths and enhancing performance metrics across various network scenarios. According to results, the suggested HALO algorithm works better than the conventional methods like lion optimization algorithm (LOA) and ant colony optimization (ACO) with ad hoc on‐demand distance vector (AODV) and AOMDV protocols. Simulation demonstrates that the suggested approach improves throughput by 30%–35%, PDR is increased by approximately 30%, E2E delay is reduced by 100 to 125 msec, energy consumption is reduced by 75 to 85 mJ, and routing overhead is reduced by 10% compared to the traditional routing protocols.