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Mobile Ad Hoc Networks (MANETs) are networks in which all nodes are mobile and communicate with each other via wireless connections. One of the reasons that routing in MANETs is a particularly challenging task is due to the fact that the topology of this type of network changes constantly and paths which were initially efficient can quickly become inefficient or even infeasible. Moreover, control information flow over the network is very restricted because the bandwidth of the wireless medium is limited. AntHocNet is a hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. The algorithm is based on the Nature-inspired Ant Colony Optimization framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. AntHocNet's performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks.
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