摘要
机器人路径规划问题通常采用不同算法来对其进行规划,为发挥算法中改进遗传算法和鲸鱼优化法的优势,弥补遗传算法出现优化准确率和收敛度不高等问题,将改进遗传算法和鲸鱼优化法融合,增强移动机器人路径规划对动态环境的适应性能。对算法适应度函数进行优化,改善了基本遗传算法、提升了原算法对函数的求解效率。通过遗传算法、对遗传算法进行改进的算法、改进遗传算法与鲸鱼算法相融合的算法所运行的路径长度与运行时间进行比较,结果表明融合改进优化算法可以有效获取最优算子,减少运算时的迭代次数,同时提升算法的规划准确率。
In order to make full use of the advantages of genetic algorithm and the whale method in the algorithm,the improved genetic algorithm is integrated with the whale method to enhance the adaptability of the mobile robot path planning to the dynamic environment.By optimizing the fitness function of the algorithm,the basic genetic algorithm is improved so that the solving speed of the original algorithm is improved.Compared the path length and running time of the three algorithms:genetic algorithm,improved genetic algorithm and improved genetic whale algorithm,the results show that the improved genetic whale algorithm can continuously extract the optimal operator,reduce the number of iterations,and improve the accuracy of path planning.
作者
谢冲冲
李莹
XIE Chongchong;LI Ying(School of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650504,P.R.China)
出处
《重庆大学学报》
CSCD
北大核心
2021年第12期140-148,共9页
Journal of Chongqing University
基金
云南省科技厅资助项目(KKST201801138,KKST201901002)。
关键词
遗传算法
鲸鱼算法
适应度函数
动态环境
路径规划
genetic algorithm
whale algorithm
adaptability function
dynamic environment
path planning
