基于改进粒子群算法的电动汽车充电站布局优化

发布时间：2018-03-30 18:15

本文选题：交通工程　切入点：布局优化　出处：《公路交通科技》2017年06期

【摘要】：针对带有充电站服务要求和用户充电需求限制的电动汽车充电站布局优化问题,构建了以充电站服务成本和用户需求成本之和最小为目标的优化模型。将K中心点算法和云模型混合自适应粒子群算法相结合,提出了一种提高全局搜索能力的自适应参数改变算法。利用云模型混合自适应粒子群算法的特点,构建了求解电动汽车充电站布局优化问题的K中心点云模型混合自适应粒子群算法。仿真结果表明:求解带有充电站服务要求和用户充电需求限制的电动汽车充电站布局优化问题时,改进的K中心点云模型混合自适应粒子群算法优于云模型粒子群算法和基本粒子群算法;与基本算法相比,改进的算法效率更好,收敛性更好,证明了改进算法的有效性与可行性。
[Abstract]:Aiming at the problem of layout optimization of charging stations for electric vehicles with charging station service requirements and user charging requirements, An optimization model with the goal of minimizing the sum of service cost of charging station and cost of user demand is constructed, which combines the K-center algorithm with the hybrid adaptive particle swarm optimization algorithm of cloud model. In this paper, an adaptive parameter changing algorithm is proposed to improve the global search ability. The K-center point cloud model hybrid adaptive particle swarm optimization algorithm for the layout optimization of charging stations of electric vehicles is constructed. The simulation results show that the charging of electric vehicles with charging requirements and user charging requirements is limited. When the power plant layout optimization problem, The improved K-center point cloud model hybrid adaptive particle swarm optimization algorithm is superior to the cloud model particle swarm optimization algorithm and the basic particle swarm optimization algorithm, compared with the basic algorithm, the improved algorithm has better efficiency and better convergence. The effectiveness and feasibility of the improved algorithm are proved.
【作者单位】：上海理工大学管理学院;
【分类号】：TP18;U491.8

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