An analytic modelling approach for network routing algorithms that use ant-like mobile agents
In this paper, we introduce an analytic modelling approach to the study of a novel class of adaptive network routing algorithm, which is inspired by the emergent problem-solving behaviours observed in biological ant colonies. This class of algorithm utilizes ant-like agents which traverse the network and collectively construct routing policies. Previous studies have focused exclusively on simulation experiments, which indicate that such algorithms perform well in response to real-time changes in traffic demands and network conditions. The analytic model presented in this paper permits useful insights into certain fundamental aspects of ant-based algorithms, which have not been discussed in previous ant-based routing literature. In particular, the work presented in this paper motivates our proposal of a number of modifications to the basic design of ant-based routing algorithms, which result in improved performance with respect to equilibrium performance measures.
ant-based routing adaptive routing reinforcement learning wardrop equilibria