基于遗传交叉改进粒子群算法的充电站布局

LAYOUT OF CHARGING STATIONS BASED ON IMPROVED PARTICLES SWARM OPTIMIZATION WITH GENETIC CROSSOVER

为了合理地规划城市电动汽车充电站布局,采用一种基于遗传交叉改进粒子群算法的寻优处理方案。在传统粒子群算法的基础上,引入局部极值对速度更新公式进行优化,采用自适应惯性权重,并且对当前种群的最优解和每个粒子最优解进行交叉操作产生新解。最后通过改进后算法对城市算例进行求解。结果验证了模型的有效性和准确性,表明改进算法在保持全局最优解的同时能提高70%收敛速度,有效降低总成本、提高便利性。

In order to plan the layout of electric vehicle charging station reasonably,this paper adopts an optimization scheme based on improved particle swarm optimization with genetic crossover. On the basis of traditional particle swarm optimization,the local extremum is introduced to optimize the speed updating formula,and the adaptive inertia weight is adopted. A new solution is generated by the cross operation of the optimal solution of the current population and each particle optimal solution. At last,the improved algorithm is used to solve the urban examples. The results verify the validity and accuracy of the model,and show that the improved algorithm can improve the convergence rate of 70% while getting the global optimal solution and effectively reduce the total cost,improve the convenience.