基于改进麻雀搜索算法智能车的路径规划
Path planning of intelligent vehicle based on improved sparrow search algorithm
祁升升 1申彩英 1孙涛1
作者信息
- 1. 辽宁工业大学 汽车与交通工程学院,辽宁 锦州 121001
- 折叠
摘要
针对传统麻雀搜索算法在种群迭代后期种群多样性差、收敛精度和稳定性差,易陷入局部最优值等问题,提出了一种改进的麻雀搜索算法.首先,在麻雀种群初始化阶段引入ICMIC混沌映射,提高了麻雀种群对于环境的遍历性,解决搜索范围不足的问题;其次在发现者位置更新阶段,引入自适应惯性因子,在算法迭代前期加大对全局的搜索能力,在算法迭代后期专注于局部搜索;改进麻雀种群警戒行为位置更新公式,与正弦余弦算法进行结合,提高算法的收敛精度.最后将改进的麻雀搜索算法应用于智能车的路径规划,经过实验与分析,改进后的麻雀搜索算法在不同的环境地图中平均路径长度分别减小了 4.36%和 6.72%;寻找最短路径用时分别快了 43.57%和44.62%.
Abstract
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.
关键词
麻雀搜索算法/ICMIC混沌映射/正弦余弦算法/路径规划Key words
sparrow search algorithm/ICMIC chaotic mapping/sine-cosine algorithm/path planning引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据