现代信息科技2026，Vol.10Issue(5)：51-55,59,6.DOI:10.19850/j.cnki.2096-4706.2026.05.010
基于粒子群灰狼算法的路径规划
Path Planning Based on HHPSO-GWO
王荣秀 1杨敏2
作者信息
- 1. 无锡太湖学院,江苏 无锡 214064
- 2. 中国电子科技集团公司第五十八研究所,江苏 无锡 214000
- 折叠
摘要
Abstract
To address the problems of low computational efficiency,premature convergence or slow late convergence in path planning,a grid map is constructed and a Hierarchical Hybrid Particle Swarm Optimization-Grey Wolf Optimizer(HHPSO-GWO)is proposed.The algorithm divides the population into elite and ordinary particles and implements differentiated learning to dynamically balance search performance.Through simulation comparisons,the optimal path lengths planned by HHPSO-GWO are 16.650 0 and 44.284 3 respectively,which are improved by 30.33%and 16.24%compared with PSO,and by 6.98%and 11.93%compared with GWO.The numbers of convergence iterations are only 12 and 9,which are much fewer than 47 and 52 of the PSO algorithm,and better than 55 and 12 of the GWO algorithm.The results show that the algorithm can improve the convergence speed and effectively shorten the path length,providing a new method for path planning.关键词
路径规划/HHPSO-GWO/差异化学习/最优路径Key words
path planning/HHPSO-GWO/differentiated learning/optimal path分类
信息技术与安全科学引用本文复制引用
王荣秀,杨敏..基于粒子群灰狼算法的路径规划[J].现代信息科技,2026,10(5):51-55,59,6.
