Hybrid Meta-Heuristic Framework for Network Lifetime Maximization in Dense Mobile Ad Hoc Networks

The proliferation of the Internet of Things (IoT) and mission-critical applications has led to the dense deployment of Mobile Ad Hoc Networks (MANETs), where energy efficiency is a paramount concern. The network lifetime, a key performance metric, is severely constrained by the limited battery capacity of constituent nodes. This paper addresses the complex, multi-constrained optimization problem of maximizing network lifetime in dense MANETs. We propose a novel hybrid meta-heuristic framework, the Grey Wolf-Levy Firefly Algorithm (GWL-FA), which synergistically combines the social hierarchy and hunting mechanisms of the Grey Wolf Optimizer (GWO) with the Lévy flight-enhanced exploration of the Firefly Algorithm (FA). The primary objective is to determine an optimal routing and power control strategy that balances traffic load, minimizes energy consumption, and mitigates hotspot formation. Simulation results, conducted in NS-3 under varying node densities (50-200 nodes), demonstrate that GWL-FA significantly outperforms standard GWO, FA, and Energy-Aware Dynamic Source Routing (EA-DSR) protocols. Specifically, GWL-FA achieves up to a 28.5% and 34.7% improvement in network lifetime over GWO and FA, respectively, and a 52.1% improvement over EA-DSR in high-density scenarios (200 nodes). The proposed algorithm also shows superior performance in terms of packet delivery ratio (maintained above 96%) and end-to-end delay. We present a comprehensive analysis of the results, discuss the convergence behavior, and outline pivotal future research directions, including the integration of machine learning and quantum computing principles for next-generation energy-aware MANETs.