基于改进鲸鱼优化算法的电动汽车充电设施选址  

Location Selection of Electric Vehicle Charging Facilities Based on Improved Whale Optimization Algorithm

作　　者：梁迪[1] 姜廷霖 郭启航 LIANG Di;JIANG Tinglin;GUO Qihang(School of Mechanical Engineering,Shenyang University,Shenyang 110044,China)

机构地区：[1]沈阳大学机械工程学院,辽宁沈阳110044

出　　处：《沈阳大学学报（自然科学版）》2022年第1期24-29,共6页Journal of Shenyang University：Natural Science

基　　金：国家留学基金委资助项目(201908210398)。

摘　　要：基于不同区域用户对充电需求的不同,以充电站覆盖范围内最小化充电站数目为限制条件,各需求点的需求度权重与充电站到需求点距离的乘积之和最小为目标构建选址优化模型。针对传统算法收敛速度慢,容易陷入局部最优的特点,引入余弦递减的策略对鲸鱼优化算法(WOA)的收敛因子取值方式进行优化。通过实例将优化鲸鱼算法(IWOA)与粒子群优化算法(PSO)和遗传算法(GA)进行对比,结果表明,IWOA可以更合理计算出选址位置,为充电站的选址提供更优方案。Based on the different charging needs of users in different regions,taking the minimum number of charging stations within the coverage of charging stations as the restriction condition,the site selection optimization model is constructed with the goal of minimizing the sum of the product of the demand degree weight of each demand point and the distance from the charging station to the demand point.Due to the traditional algorithm has the characteristics of slow convergence speed and easy to fall into local optimization,the strategy of diminishing cosine is introduced to optimize the convergence factor value method of whale optimization algorithm(WOA).To test the performance of the algorithm,the optimized whale algorithm is compared with particle swarm optimization algorithm and genetic algorithm through an example,the results show that the improved algorithm could calculate the location of the charging station more reasonably and provide a better solution for the location of the charging station.

关 键 词：充电站 改进鲸鱼优化算法 粒子群算法 遗传算法 余弦递减策略 

分 类 号：U491.8[交通运输工程—交通运输规划与管理]