摘要
针对仓储环境中无人导引车(AGV)搬运货物的路径规划问题,提出一种基于改进的麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)的方法。使用虫口混沌序列取代种群的初始化随机分布,提高初代解的丰富性和均衡性,从而解决种群分布集中、搜索空间不够大等问题;通过在发现者中引入时变权重因子,在前期不断拓展搜索区域,在后期提升收敛效率,协调整体与局部的开发拓展能力;通过反向学习策略对种群进行扰动,防止算法后期种群单一和陷入局部最优。结果表明,ISSA在路径长度、拐角次数以及迭代次数方面均优于原算法和蚁群算法;在相同的环境模型下,ISSA在路径长度和拐角次数方面均优于文献算法。
To solve the problem of path planning of AGV transporting goods in warehousing environment,a method based on Improved Sparrow Search Algorithm(ISSA)is proposed.Firstly,the logistic chaotic sequence is used to replace the initial random distribution of the population to improve the richness and equilibrium of the primary solution,so as to solve the problems of concentrated population distribution and insufficient search space.Secondly,time-varying weight factors is introduced into the discoverer to expand the search scope in the early stage,accelerate the convergence speed in the later stage,and balance the global exploration and local development capabilities of the algorithm.Finally,the opposition-based learning strategy is used to perturb the population to prevent the single population and fall into the local optimization in the later stage of the algorithm.The results show that the improved sparrow search algorithm is better than the original algorithm and ant colony algorithm in terms of path length,number of corners and number of iterations.Under the same environmental model,the ISSA algorithm is superior to the algorithm in the literature in terms of path length and number of corners.
作者
王洪铃
谭功全
李静
李易念
郑佳钰
WANG Hongling;TAN Gongquan;LI Jing;LI Yinian;ZHENG Jiayu(School of Automation and Information Engineering,Sichuan University of Science&Engineering,Zigong 643000,China;Artificial Intelligence Key Laboratory of Sichuan Province,Yibin 644005,China)
出处
《无线电工程》
北大核心
2023年第8期1917-1924,共8页
Radio Engineering
