基于粒子群灰狼算法的路径规划OA

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.