The single mechanism based Grey Wolf Optimizer (GWO) is easy to fall into local optimum and has slow convergence speed. In order to solve the problems, an Improved Grey Wolf Optimization (IGWO) was proposed to solve the actual location selection problem of railway logistics distribution center. Firstly, based on the basic GWO, the theory of good point set was introduced to initialize the population, which improved the diversity of the initial population. Then, the D-value Elimination Strategy (DES) was used to increase the global optimization ability, so as to achieve an efficient optimization mode. The simulation results show that, compared with the standard GWO, IGWO has the fitness value increased by 3%, and the accuracy of the optimal value increased by up to 7 units in 10 test functions. Compared with Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and Genetic Algorithm (GA), IGWO has the location selection speed increased by 39.6%, 46.5% and 65.9% respectively, and the location selection velocity is significantly improved. The proposed algorithm can be used for railway logistics center location selection.

Since the data forwarding in Vehicular Ad Hoc Network (VANET) is inefficient, a technology on data forwarding and routing selection based on Software Defined Network (SDN) was proposed. Firstly, a hierarchical architecture of SDN based VANET, which was consist of local controller and global controller, was used to separate data forwarding from transmission control and decide the direction of data forwarding flexibly. Secondly, a vehicular routing mechanism of single road section was designed and data could be transmitted stably by predicting vehicular position and using greedy strategy. Thirdly, to achieve the goal of path disjoint which could avoid the bandwidth bottleneck between multiple demands, a road section routing mechanism was proposed, which combined the Breadth First Search (BFS) and edge set. Finally, compared with Ad Hoc On-demand Distance Vector (AODV) routing, the proposed algorithm could increase data reception rate by 40% and reduce average delay by 60%. The simulation results show that the technology on data forwarding and routing for software defined VANET can effectively improve the data delivery rate, and reduces the average packet delay.

Aiming at the problem of low computational performance of distributed database in analysis applications, taking MongoDB database as an example, a method was put forward to improve the performance of database based on chip and index. Firstly, the characteristics of the business was analyzed to guide the choice of shard key field, and the selected key field needed to ensure that the data is evenly distributed on the cluster nodes. Secondly, by studying the index efficiency of the distributed database, the method of deleting the query field index was used to further improve the computing performance, which could make full use of hardware resources to improve the performance of aggregation computing. The analysis and experimental results show that the shard key field with high cordinality can distribute data evenly on each data node in the cluster, and the use of full table query can effectively improve the convergence speed, thus the optimization method can effectively improve the performance of aggregation computing.