Abstract
Transportation network design is non-deterministic polynomial-time hard due to its attributes of multi-objects, multi-constraints, and the non-convexity objective function. In this paper, a bi-level programming model is proposed for the transportation network design. The upper layer pursues the minimum total travel time of users and the total length of the road network simultaneously, while the lower layer is an equilibrium assignment model. A new algorithm for the network optimization based on the principle of leaf photosynthate transport in nature is proposed. The proposed algorithm simulates the natural selection of biological evolution and genetic transmission. It can retain the genetic idea of the evolutionary algorithm, together with the heuristic information update mechanism of swarm intelligence. Finally, empirical research is carried out with the Sioux Falls network to validate the performance of the proposed algorithm. The results show that although the total network length obtained by the proposed algorithm increases slightly compared with the ant colony algorithm and the genetic algorithm, the total travel time and objective function value reduce obviously. This indicates that the proposed algorithm has good performance on topology and efficiency.
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