A Multigroups Cooperative Particle Swarm Algorithm for Optimization of Multivehicle Path Planning in Internet of Vehicles

In order to solve the problem of low road traffic efficiency of intelligent transportation, especially when multiple vehicles simultaneously optimize and cooperate with multiple destinations, it is necessary to consider the efficiency of road traffic, flow, and load balancing. In the traffic path, improve the traffic capacity, reduce traffic congestion, and intelligent traffic path. This article presents a multivehicle path planning method based on multigroup cooperative particle swarm optimization (MVPP-MGC-PSO). When different vehicles choose the path, the individual vehicles cooperate to pass the target path. The algorithm takes into account the vehicle's speed, capacity, traffic light time, and other factors in Internet of Vehicles to choose the best path to reduce travel time, fuel consumption, and traffic congestion. The results show that the optimization efficiency of MVPP-MGC-PSO is as high as 38% when the traffic density is 70%. In addition, the number of feasible paths has a significant impact on the efficiency of the algorithm, and too few or too many feasible paths will reduce the efficiency of the algorithm. When the number of feasible paths is 3, the optimization effect improvement ratio of the algorithm is 30%. The MVPP-MGC-PSO algorithm has better performance than other algorithms in terms of different path efficiency, feasibility path, and multigroup cooperation path passage time.