An Ant Colony Based Approach to multi-Constraint Routing Problem in LEO Satellite Networks

As the scale of the low earth orbit (LEO) satel-lite networks grows, the routing problem has become a great challenge in academia and industry. In this paper, we propose a multi-constraint Quality of Service (QoS) ant colony routing algorithm (MQACRA) to address this issue. Firstly, the topo-logical path weight is calculated based on the channel gain approach. Secondly, the positive feedback property of the ant colony algorithm (ACO)is used to dynamize the static pheromone concentration significance factor and boost the global search capability of the algorithm in the early stage. Thirdly, decreasing dynamic parameters enhance volatile coefficients and connect pheromone update rules to limit algorithm search local optimum and expedite algorithm convergence. Furthermore, by improving the heuristic function of the ACO, the QoS information is considered an essential basis for ants to select the next hop node. Finally, combining path-finding cost with MQACRA's convergence speed delivers the optimal pheromone significance factor, lowest pheromone volatility factor, and link length important factor. Simulation results demonstrate that MQACRA can address ACO's insufficient global search capabilities and slow convergence rate while maintaining low path-finding costs.