Distributed Routing for Bottleneck Bandwidth Maximization Using ACO with a Node-Based Adaptive Mechanism

Maximizing the bottleneck bandwidth is a critical challenge for high-capacity data transmission. Existing distributed methods often fail in random topologies because local edge statistics do not necessarily reflect end-to-end path bottlenecks. This paper proposes a distributed routing method based on Ant Colony Optimization with a novel node-based learning mechanism that resolves the limitations of relying on local edge statistics by dynamically aligning the evaluation reference with actual path capacities using a finite history. Simulation results demonstrate a significant performance improvement, with the optimal path selection ratio from approximately 50% to 80% in random networks, while maintaining robustness in dynamic environments like Information-Centric Networking.