An adaptive routing selection algorithm based on improved ant colony optimisation for mobile ad hoc networks

Swarm intelligence methods like ant colony optimisation (ACO) have been proven to be effective in developing routing selection for mobile ad hoc networks (MANETs). However, the traditional ACO algorithm mainly focuses on the path length or hop count of routes, which converges very slowly. This paper proposes an adaptive routing selection (ARACO) algorithm based on ACO. By improving the state transfer function and pheromone update mechanism, and introducing a dynamic weight update strategy, the algorithm maintains strong exploration capability in the early stage and achieves rapid converge in the later stage. The algorithm comprehensively considers the delay, jitter and packet loss rate when updating pheromones. This paper proposes a path utility function to measure the transmission rate and hop count, and the algorithm aims to maximise the value of the path utility function while taking into account the quality of service (QoS) requirements. Simulation results show that the ARACO algorithm is significantly better than the ACO and genetic algorithm (GA) in terms of transmission rate, delay, jitter and packet loss rate of the optimal route.