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文章摘要
(12期拿下)基于权重自适应调整的混沌量子粒子群算法的城市电动汽车充电站优化布局
Optimal Planning of Charging Station for Electric Vehicle Based on Quantum ACQPSO Algorithm
Received:May 14, 2016  Revised:June 13, 2016
DOI:
中文关键词: 电动汽车  权重自适应调整的混沌量子粒子群算法  充电站  选址  定容
英文关键词: electric vehicle  chaotic quantum particle swarm optimization algorithm of weighted adaptive adjustment  charging station  site selection  determining capacity
基金项目:
Author NameAffiliationE-mail
yuqing* dongbeidianlidaxue 1432190378@qq.com 
lijinghua dongbeidianlidaxue 143290378@qq.com 
zhaoqianfu dongbeidianlidaxue 1432190378@qq.com 
xingchunyang dongbeidianlidaxue 1432190378@qq.com 
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中文摘要:
      针对城市电动汽车充电站的定容和选址的问题,从实际情况出发,建立将土地价格、建设成本、运行成本、交通流量、服务距离、服务能力考虑在内的数学模型,该模型以年均综合费用最小为目标,以充电能力和充电距离为约束条件。本文采用基于权重自适应调整的混沌量子粒子群算法对北方的某市某区进行规划,该算法在迭代过程中会根据粒子不同的适应值,对惯性权重做出相应的调整,从而调整对粒子的搜索能力。利用混沌算子的遍历性,使得该算法具有很好的收敛速度和精度。利用该算法对本文建立的数学模型进行求解,确定了该地区充电站的建址坐标及容量。
英文摘要:
      In view of the problem of the capacity and location of the electric vehicle charging station, we establish the mathematical model considered of the land price, construction cost, operation cost, traffic flow, service range and service ability, starting from the actual situation. The target of the model is to minimum annual comprehensive cost, and the constraints of the model are charging ability and distance. In this paper, based on adaptive weight adjusting the chaotic quantum particle swarm optimization algorithm, we plan the city district in the north. In the iteration process, basing on the different adaptive values of particles, the algorithm will adjust inertia weight correspondingly to adjust the search ability of particle. Using the ergodicity of chaotic operator makes the algorithm have good convergence speed and accuracy. We use this algorithm to solve this established mathematical model in this paper. Eventually, the coordinates and capacity of charging stations in the region are determined.
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