An adaptive and hybrid Ant colony optimization algorithm for multi-constrained QoS routing in MANETs

Introduction Multi-constrained Quality of Service (QoS) routing is a core technology for ensuring efficient data transmission in Mobile Ad Hoc Networks (MANETs) in critical applications such as emergency communications and military reconnaissance. However, due to the high mobility of nodes and limited network resources, traditional routing protocols struggle to balance multiple conflicting QoS metrics in complex topological environments. Additionally, conventional metaheuristic algorithms often exhibit inherent limitations when addressing such problems, including slow convergence, susceptibility to local optima, and premature convergence. Methods To address these challenges, this paper proposes an Adaptive and Hybrid Ant Colony Optimization (AH-ACO) algorithm. The algorithm first constructs a comprehensive weighted cost model that encompasses multiple QoS parameters, transforming multi-dimensional QoS constraints into quantifiable optimization objectives. Next, an adaptive pheromone evaporation mechanism that dynamically adjusts during iterations is introduced to effectively balance the global exploration capability in the early search stage with the local exploitation capability in the later stage. Finally, a hybrid pheromone updating strategy combining global best and elite path reinforcement is employed to accelerate convergence toward the global optimum while maintaining population diversity. Results Simulation results indicate that, compared with the traditional Ant Colony Optimization (ACO) algorithm, AH-ACO exhibits superior convergence characteristics. Furthermore, under scenarios with varying network scales and node mobility, the proposed algorithm consistently outperforms traditional methods in key performance metrics, including end-to-end delay, packet loss rate, and effective bandwidth. Discussion The proposed AH-ACO effectively mitigates the limitations of traditional metaheuristic approaches in multi-constrained routing. The performance improvements across varying conditions demonstrate the algorithm’s strong robustness and scalability, making it a highly viable solution for complex MANET environments.