摘要
灰狼算法(Grey Wolf Optimizer,GWO)用于路径规划会出现收敛速度慢和陷入局部最优的问题,因此本文提出了一种改进GWO。该算法在栅格环境下采用32邻域48方向的搜索方式,利用混沌法初始化灰狼种群,在与差分进化算法相融合,提高算法的搜索范围和全局搜索最优解的效果,避免算法陷入局部最优。仿真结果表明,本文算法与GWO相比,收敛速度和规划出的路径长度都有明显的提升。
The gray wolf algorithm is used for path planning,which has the problems of slow convergence and falling into local optimum.The algorithm adopts a 32-neighborhood 48-direction search method in a raster environment,uses chaos method to initialize the grey wolf population,and integrates with the differential evolution algorithm to improve the search range of the algorithm and the effect of global search for optimal solutions to avoid the algorithm falling into a local optimum.The simulation results show that the convergence speed and the length of the planned path are significantly improved compared with the Grey Wolf Optimizer(GWO)algorithm in this paper.
作者
李素
黄友锐
LI Su;HUANG Yourui(College of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan Anhui 232001,China)
出处
《信息与电脑》
2022年第15期67-70,共4页
Information & Computer
关键词
路径规划
灰狼优化算法
栅格图
混沌初始化
差分进化
path planning
gray wolf optimization algorithm
grid diagram
chaos initialization
differential evolution
