Optimal control for problem of vehicle path tracking based on improved sparrow search algorithm

Abstract

In order to solve the problem of slow convergence speed and low accuracy of traditional sparrow search algorithm (SSA), an improved sparrow search algorithm (ISSA) based on Tent chaotic map was proposed to improve the distribution of the sparrow initial population. This method initialized the population through Tent chaotic map and elite reverse seeding and updated the position of the population using the golden sine strategy. At the same time, it reduced the number of scouts using the cosine strategy and selected and updated the optimal solution of the population using the greedy strategy. Simulation results show that the proposed method can enhance the quality of the initial solution of the algorithm and balance the global exploration ability of the algorithm and local exploration ability. And also, the proposed method can enhance the ability of jumping out of local optimal solutions which can be applied to optimize vehicle path tracking problems. The ISSA proposed in this study has the advantages of fast convergence speed and high accuracy compared to the original SSA.