Abstract
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness, decentralized and self-organizing nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum. Simulation tests on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
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