Improvement of Multi-vehicle Queue Transformation Paths Based on MO-GWO

In this paper, an optimization method based on a group cooperation strategy is proposed to make reasonable improvements to the problems of low transformation efficiency and high operational loss in the formation process of a multi-vehicle cooperative system. Three objective functions (cost loss degree, obstacle avoidance constraint, queue compactness) are constructed by analysing the multi-vehicle system. Secondly, the grey wolf optimization (GWO) introduces a multi-period cosine convergence factor and a hunting search strategy based on dimensional learning, which effectively improves the algorithm's tendency to fall into the phenomenon of locally optimal solutions. Finally, the multi-objective grey wolf optimization (MO-GWO) is used to iterate to obtain the corresponding Pareto frontiers and simulation results, and the value coupling function is established to find out the optimal path that reduces the distance by 60% compared with the original, which reasonably achieves the path improvement of the multi-vehicle system at the overall level.