Hybrid Metaheuristic Adaptive QoS Routing Using Dual-Layer Deep Reinforced Swarm Learning in 5G-Enabled Materials with Secure Packet Clustering

QoS routing in MANETs continues to present significant challenges in the era of emerging wireless technologies, particularly with the integration of 5G networks, silicon-based antenna systems, and advanced material-based communication devices. The dynamic nature of node mobility, energy limitations, and vulnerability to security attacks requires robust and intelligent routing frameworks for modern MANET environments. Conventional swarm intelligence-based routing models, although effective, often lack adaptivity to dynamic topologies and fail to ensure secure packet transmission in heterogeneous material-driven wireless environments. To overcome these limitations, this paper proposes a novel Hybrid Metaheuristic Adaptive QoS Routing Framework that seamlessly integrates Ant Colony Optimization (ACO), Red Deer Algorithm (RDA), and Butterfly Optimization Algorithm (BOA), reinforced by a Deep Q-Learning (DQL) controller. The hybrid architecture intelligently adapts its optimization strategies based on real-time network parameters such as energy, link stability, and packet loss, leveraging DQL for parameter tuning. Furthermore, the proposed model incorporates a dual-layer secure packet scheduling system comprising modified TDMA scheduling and secure fuzzy-based packet clustering, ensuring encrypted data transmission even in complex 5G-enabled silicon antenna-based MANET nodes. The proposed framework was extensively validated through simulations and exhibited superior performance across multiple QoS metrics. It achieved a 15.3% increase in throughput, 13.8% improvement in packet delivery ratio, 22.5% reduction in delay, and 18.1% energy savings compared to existing approaches like MACOPM, RDA-EQRP, and ISMBOQAR-PS. The study establishes this hybrid approach as a highly adaptive and secure solution for next-generation MANETs integrated with advanced materials, 5G technologies, and silicon-based antenna infrastructures.