%0 Journal Article %A LI Jin %A WANG Feng %A YANG Shenyu %T Freight routing optimization model and algorithm of battery-swapping electric vehicle %D 2021 %R 10.11772/j.issn.1001-9081.2020091356 %J Journal of Computer Applications %P 1792-1798 %V 41 %N 6 %X To address the electric vehicle freight routing optimization problem considering the constrains of battery life and battery-swapping stations, a calculation method of electric vehicle carbon emissions considering multiple factors such as speed, load and distance was proposed. Firstly, with the goal of minimizing power consumption and travel time cost, a mixed integer programming model was established. Then, an adaptive genetic algorithm was proposed based on the mountain-climb optimization and batter-swapping neighborhood searching, and the crossover and mutation probabilities adaptively adjusting with the change of the population fitness were designed. Finally, the mountain-climb searching was used to enhance the local search capability of the algorithm. And the battery-swapping neighborhood searching strategy for the electric vehicle was designed to further improve the optimal solution, so as to meet the constraints of battery life and battery-swapping stations and obtain the final optimal feasible solution. The experimental results show that, the adaptive genetic algorithm can find satisfactory solution more quickly and effectively compared to the traditional genetic algorithm; the route arrangement considering power consumption and travel time can reduce the carbon emissions and total freight distribution costs; compared with the fixed parameter setting of the crossover and mutation probabilities, the adaptive parameter adjustment method can more effectively avoid the local optimum and improve the global search ability of the algorithm. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020091356