软件导刊2025，Vol.24Issue(7)：54-60,7.DOI:10.11907/rjdk.241412
混合粒子群与改进灰狼算法的移动机器人路径规划
Hybrid Particle Swarm and Improved Grey Wolf Algorithm for Mobile Robot Path Planning
摘要
Abstract
Aiming at addressing the challenges of local optima trapping and slow convergence rates encountered by robots in path planning based on the grey wolf optimization algorithm,this paper proposes a hybrid algorithm called particle swarm optimization and improved grey wolf optimization(PGWO).The PGWO algorithm leverages particle swarm optimization to determine the initial fitness values of the wolf pack and enhances the convergence factor in traditional grey wolf optimization,thereby balancing its search capability and improving the algo-rithm's late-stage search rate.Additionally,dynamic allocation of population weights is employed to reduce the likelihood of falling into local optima.By blending particle swarm optimization with the improved grey wolf algorithm,the optimal solution is attained.Results demonstrate that,compared to traditional grey wolf algorithm,PGWO reduces the path length by 35.03%and 34.58%,decreases search time by 52.69%and 51.06%,and improves convergence speed by 30.62%and 34.30%on two grid maps,respectively.Compared to the improved grey wolf al-gorithm,PGWO reduces the path length by 22.03%and 23.04%,decreases search time by 33.05%and 25.81%,and improves convergence speed by 16.83%and 20.98%,respectively.Furthermore,compared to ant colony optimization,PGWO shortens the path length by 24.08%and 25.41%,reduces search time by 65.04%and 79.74%,and improves convergence speed by 23.53%and 32.34%,indicating the effective-ness of the PGWO algorithm in path planning optimization.关键词
移动机器人/路径规划/灰狼算法/粒子群算法Key words
mobile robots/path planning/GWO/PSO分类
信息技术与安全科学引用本文复制引用
莫定界,赵杰,凌港,张冬青,陈嘉晋..混合粒子群与改进灰狼算法的移动机器人路径规划[J].软件导刊,2025,24(7):54-60,7.基金项目
黑龙江省省属高等学校基本科研业务费项目(2022-KYYWF-0551) （2022-KYYWF-0551）
