基于群集智能优化算法的城市共享单车优化分布研究

Study on urban sharing bike optimization distribution based on swarm intelligent optimization algorithm

为解决城市共享单车供需与分布的不均衡状态,研究基于群集智能优化算法的城市共享单车优化分布方法。通过分析城市共享单车优化分布问题了解到共享单车的合理有效调度是实现城市共享单车优化分布的基础。以共享单车停放点满意度最大化为目标函数,引入模糊时间理念,创建带模糊时间窗的车辆调度模型,通过蚂蚁移动规则和信息素全局更新规则构成蚁群算法,并对蚁群算法进行适度改进,利用改进蚁群算法结合算法过程求解车辆调度模型,获取共享单车最优调度路径,完成城市共享单车合理有效的调度,实现城市共享单车的优化分布。以广州市越秀区15个共享单车停放点为实验对象,结果表明,该方法获取的最优路径简洁便利,没有出现重复交叉路段,且最优路径寻得次数相对较为稳定。

An urban sharing bike optimization distribution method based on swarm intelligent optimization algorithm is studied to eliminate the imbalance between supply&demand and distribution of the sharing bikes.By analyzing the optimization distribution of urban sharing bikes,it is concluded that the rational and effective scheduling of sharing bikes is the basis of their optimization distribution.The sharing bike parking lot satisfaction maximization is taken as the objective function.The fuzzy time concept is introduced to create the vehicle scheduling model with fuzzy time window.Ant movement rules and global pheromone updating rules are used to create the ant colony optimization(ACO)algorithm,which is then improved moderately.The improved ACO algorithm combining algorithm process is used to solve the vehicle scheduling model and obtain the optimal scheduling path for the sharing bikes,so as to complete their rational and effective scheduling and achieve their optimization distribution.15 sharing bike parking lots in Yuexiu District,Guangzhou City were taken as the experimental subjects.The experimental results show that there is no repetition and intersection for the optimal path,and the optimizing times for the optimal path is relatively even.Therefore,the optimal path obtained by the proposed method is concise and convenient.