Abstract
In the practical application fields such as intelligent transportation system, routing network and communication network, time-dependent networks make it difficult for traditional ant colony optimization (ACO) to solve path planning problem. In view of the limitations of traditional ant colony algorithm in time-dependent networks’ path planning problem, this paper focuses on the renewal strategy of residual pheromones and the route selection strategy of bionic ants according to the path information provided by congestion degree. For the first time, an improved ACO algorithm is proposed to solve time-dependent road networks’ (TDRN) planning problem. The experimental results show that compared with the classical ant colony algorithm, the improved ant colony algorithm proposed in this paper works better in TDRN problem solution, and the shortest path calculation time in TDRN can be reduced by 4.21%, up to 11.70%. Path quality improvement of time-dependent weight increase more than 40%.
Published Version
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