摘要
针对电动出租车充电站优化选址问题,构建了以未满足的电动出租车充电需求量和新建充电站的固定成本最小为目标函数的电动出租车新建充电站选址模型,并提出基于改进的多目标粒子群算法的模型求解方法。为解决未满足充电需求量计算的性能瓶颈问题,设计了一个基于图形处理器(GPU)的未满足充电需求量并行计算算法,并通过实验验证其运行时间约为基于CPU串行算法运行时间的10%~12%。以北京为例,收集、处理相关多源数据,对提出的选址模型进行了应用示例分析,表明所提出的充电站优化选址方案具有可行性。
Aiming at the problem of optimal siting of charging station for electric taxis, a siting model of charging station for electric taxis was established with the unmet charging demand and the minimum fixed cost of constructing new charging station as objective functions, and a model solution method based on improved multi-objective particle swarm optimization was proposed. In order to solve the performance bottleneck of computing unmet charging demand, a Graphics Processing Unit (GPU)-based unmet charging demand parallel algorithm was designed. Experimental results demonstrat that the running time of the parallel algorithm is about10%-12% of that of CPU-based serial algorithm. Beijing was taken as an example of applying the proposed charging station siting model, and related multi-source data was collected and processed. The results show that the proposed optimal siting scheme for charging station is feasible.
作者
武旭晨
朴春慧
蒋学红
WU Xuchen;PIAO Chunhui;JIANG Xuehong(School of Information Science and Technology, Shijiazhuang Tiedao University,Shijiazhuang Hebei 050043, China;Hebei Branch, Bank of China, Shijiazhuang Hebei 050000, China;Information Center, Hebei Department of Housing & Urban-Rural Development,Shijiazhuang Hebei 050051, China)
出处
《计算机应用》
CSCD
北大核心
2019年第10期3071-3078,共8页
journal of Computer Applications
关键词
电动出租车
充电站选址
多目标粒子群算法
图形处理器
多源数据
electric taxi
charging stationsiting
multi-objective particle swarmoptimization algorithm
Graphics ProcessingUnit (GPU)
multi-source data
