基于梯度统计变异量子遗传算法的车辆路径规划  

Vehicle Path Planning Based on Gradient Statistical Mutation Quantum Genetic Algorithm

作　　者：李晖[1] 秦慧萍 卢凯 韩子傲 LI Hui;QIN Hui-Ping;LU Kai;HAN Zi-Ao(School of Computer and Information Engineering,Harbin University of Commerce,Harbin 150028,China)

机构地区：[1]哈尔滨商业大学计算机与信息工程学院,哈尔滨150028

出　　处：《计算机系统应用》2023年第12期161-170,共10页Computer Systems & Applications

基　　金：黑龙江省自然科学基金优秀青年项目(YQ2020G002);黑龙江省普通本科高等学校青年创新人才培养计划(UNPYSCT-2020212)。

摘　　要：针对传统路径规划算法收敛速度慢、稳定性差、易陷入局部极值的问题,提出一种基于梯度统计变异量子遗传算法的车辆路径规划方法.首先在依据染色体适应度值动态调整旋转角步长的基础上,引入梯度下降思想对量子旋转门调整策略进行改进;根据染色体变化趋势的统计特性,设计基于梯度统计的变异算子实现变异操作,提出基于量子位概率密度的自适应变异策略;以路径最短为指标建立车辆路径规划模型,通过仿真实验验证改进算法在车辆路径规划中的有效性,与其他优化算法相比,本文改进算法所规划路径长度更短,搜索稳定性更好,能有效控制算法陷入局部最优.To solve the slow convergence,poor stability,and proneness to fall into local extremes of traditional path planning algorithms,this study proposes a vehicle path planning method based on a gradient statistical mutation quantum genetic algorithm.Firstly,based on the dynamic adjustment of the rotation angle step by the chromosome fitness value,the idea of gradient descent is introduced to improve the adjustment strategy of the quantum rotation gate.According to the statistical characteristics of chromosome variation trend,a mutation operator based on gradient statistics is designed to realize mutation operation,and an adaptive mutation strategy based on Qubit probability density is put forward.Then the vehicle path planning model is built with the shortest path as the index.Finally,the effectiveness of the improved algorithm in vehicle path planning is verified by simulation experiments.Compared with other optimization algorithms,the proposed algorithm has a shorter path and better search stability to avoid the algorithm from falling into the local optimum.

关 键 词：量子遗传算法 路径规划 梯度下降 自适应变异算子 量子旋转门 变异策略 

分 类 号：U492.22[交通运输工程—交通运输规划与管理] TP18[交通运输工程—道路与铁道工程]