融合粒子群算法与改进灰狼算法的机器人路径规划  

Robot Path Planning by Fusing Particle Swarm Algorithm and Improved Grey Wolf Algorithm

作　　者：曹梦龙[1] 赵文彬 陈志强 Cao Mengong;Zhao Wenbin;Chen Zhiqiang(College of Automation and Electronic Enginnering,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学自动化与电子工程学院,山东青岛266061

出　　处：《系统仿真学报》2023年第8期1768-1775,共8页Journal of System Simulation

基　　金：山东省自然科学基金(ZR2020MF087)。

摘　　要：针对灰狼算法在处理机器人路径规划问题时存在路径较长、收敛速度较慢等问题,提出一种基于PSO与改进GWO的粒子群-灰狼混合算法(PSO-GWO)。通过多次运行PSO算法来确定初始狼群规模及初始适应度值;引入非线性收敛因子平衡GWO算法的探索和开发能力,提出动态惯性权重因子来保证头狼领导制度及促进种群交流;运用莱维飞行和贪婪策略使算法有效避免局部最优,获得最优解。仿真结果表明:该算法相较于GWO算法在3种地图下平均路径长度缩短17%、16%、16.2%;平均运行时间缩短13%、8%、16%。Aiming at the long paths and slow convergence speed of GWO algorithm in robot path planning,a hybrid PSO-GWO algorithm based on PSO algorithm and the improved GWO algorithm is proposed.By running PSO algorithm for many times,the initial wolf group size and initial fitness value are determined.A nonlinear convergence factor is introduced to balance the exploration and development capabilities of GWO algorithm,and a dynamic inertia weight factor is proposed to ensure the leadership system of alpha wolf and to promote the population communication.Levy flight and greedy strategy are used to effectively avoid the local optima and obtain the optimal solution.Simulation experiments show that the average path length of the proposed algorithm is reduced by 17%,16% and 16.2% compared with the GWO algorithm under three maps.The average running time is reduced by 13%,8%,16%.

关 键 词：GWO PSO 机器人路径规划 莱维飞行 非线性收敛因子 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]