Research On Distribution Of Base Station In Vehicular Ad Hoc Network Based On The Hybrid PSO Algorithm

Vehicle network base station construction,which is internationally recognized as one of the most economic and effective way to solve urban traffic congestion problems,received widespread attention in the community.The node coverage problem is one of the key technologies in Vehicular Ad Hoc Networks,plays an important role in balancing the dynamic allocation of urban road traffic flow.This thesis is mainly based on backgrounds as mentioned and aims at Service station extended coverage algorithm research based on Hybrid Particle Swarm Algorithm,which is the hard core of the base station node deployment components.The first part:After the abstract description of city road network,according to the characteristic of city traffic,and the dynamic vehicle mobile nodes,The theoretical analysis shows that the relation between service base station node coverage probability and extended service station of a certain region of extended coverage radius,combined with the actual situation by setting extended coverage probability value greater than a specific value,it is concluded that the covering radius of minimum.The second part:According to the deficiency of the particle swarm algorithm,this paper proposes a double population particle swarm optimization algorithm based on diffusion mechanism.The main improvements are in three aspects:in order to improve the algorithm at the end of the global searching ability and ensure the diversity of particle swarm species introduced a variety of group thinking;in order to improve the convergence rate of the algorithm for the late and the precision of the optimization,introduced the diffusion mechanism;in order to ensure the efficient iterative algorithm for computing the increased diffusion cell to remove the global search optimization ability with small particle and to achieve a variety of group information exchange and diffusion,avoid getting stuck in local optima.The final experimental results show that the validity of the improved model and algorithm is superior to the basic Artificial Fish Swarm Algorithm and traditional Genetic Algorithm from angle of efficiency and precision.