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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。
In order to solve the layout optimization problem of EV charging station with charging station service requirements and user charging requirements, an optimization model aiming at minimizing the sum of charging station service cost and user demand cost is constructed. By combining K-center algorithm and cloud model hybrid adaptive particle swarm optimization algorithm, an adaptive parameter change algorithm for improving global search capability is proposed. By using the characteristics of cloud model hybrid adaptive particle swarm optimization algorithm, a hybrid adaptive particle swarm optimization algorithm for K-center cloud model is proposed to solve the layout optimization problem of EV charging station. Simulation results show that the improved K-centered cloud model hybrid adaptive particle swarm optimization is superior to the cloud model particle swarm optimization and basic particle swarm optimization when solving the layout optimization problem of charging stations with charging station service requirements and user charging requirements. Compared with the basic algorithm, the improved algorithm is more efficient and convergent, which proves the effectiveness and feasibility of the improved algorithm.