Path planning of intelligent vehicle based on improved sparrow search algorithm
An improved sparrow search algorithm was proposed to address the problem of poor population diversity,low convergence accuracy and easy to fall into local optimum in the late iteration of sparrow search algorithm.Firstly,ICMIC chaotic mapping was introduced in the initialization stage of sparrow population,which improves the traversability of sparrow population to the environment and solves the problem of insufficient search range.Secondly,adaptive inertia factor was introduced in the stage of the finder position updating,which increases the ability of searching globally in the early stage of the algorithm iteration,and focuses on local searching in the late stage of the algorithm iteration;and the formula of the position updating of the alert behaviour of sparrow population was improved to combine with the sine-cosine algorithm to improve the convergence accuracy of the algorithm.cosine algorithm to improve the convergence accuracy of the algorithm.Finally,the improved sparrow search algorithm was applied to the path planning of intelligent vehicles.After experiments and analysis,the average path length of the improved sparrow search algorithm in different environment maps was reduced by 4.36%and 6.72%,respectively;and the time taken to find the shortest path was faster by 43.57%and 44.62%,respectively.
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