Application of hybrid particle swarm optimization and genetic algorithm and 5G network slicing technology in intelligent transportation

In this paper, an scheduling optimization scheme based on particle swarm optimization and genetic hybrid algorithm is designed for the poor performance and low efficiency of the traditional bus scheduling algorithm with the analysis and research of the current technology of bus scheduling system, which has been applied to the intelligent bus system integrating GPS/GIS, sensor technology, wireless communication, computer network and other technologies. In the research of the core technologies of the scheme, the computational mechanism and the merit-seeking characteristics of particle swarm and genetic algorithms are analyzed in depth, the advantages of the two algorithms are complemented, and the computational flow and the optimal timing for the fusion of the hybrid algorithms when searching for the final solution are determined. Finally, a correlation analysis of the hybrid algorithm is conducted, and it is concluded that it is significantly better than the current scheduling scheme with a single algorithm in terms of operational performance and solution efficiency. Additionally, it has integrated the concept of 5G network slicing, where a single network and computing infrastructure is used to deploy customized service slices that meet specific needs, in order to meet the specific service needs of different application scenarios of intelligent transportation systems (ITS).