改进蚱蜢算法在电动汽车充换电站调度中的应用

doi:10.12068/j.issn.1005-3026.2020.02.004

东北大学学报:自然科学版 ›› 2020, Vol. 41 ›› Issue (2): 170-175.DOI: 10.12068/j.issn.1005-3026.2020.02.004

改进蚱蜢算法在电动汽车充换电站调度中的应用

王生生^1,2，张伟²，董如意^1,3，李文辉¹

(1. 吉林大学计算机科学与技术学院，吉林长春130012; 2. 吉林大学软件学院，吉林长春130012; 3. 吉林化工学院信息与控制工程学院，吉林省吉林市 132022)

收稿日期:2018-12-26 修回日期:2018-12-26 出版日期:2020-02-15 发布日期:2020-03-06
通讯作者: 王生生
作者简介:王生生(1974-)，男，吉林长春人，吉林大学教授，博士生导师.冯明杰(1971-)，男，河南禹州人，东北大学副教授; 王恩刚(1962-)，男，辽宁沈阳人，东北大学教授，博士生导师.
基金资助:
吉林省科技发展计划项目(20190302117GX，20180101334JC，20170204020GX); 吉林省发展改革委员会创新能力建设(高技术产业部分)项目(2019C053-3).

Modified Grasshopper Optimization Algorithm and Applications in Optimal Dispatch of Electric Vehicle Battery Swapping Station

WANG Sheng-sheng^1,2， ZHANG Wei²， DONG Ru-yi^1,3， LI Wen-hui¹

1. College of Computer Science and Technology， Jilin University， Changchun 130012， China; 2. College of Software， Jilin University， Changchun 130012， China; 3. College of Information and Control Engineering， Jilin Institute of Chemical Technology， Jilin 132022， China.

Received:2018-12-26 Revised:2018-12-26 Online:2020-02-15 Published:2020-03-06
Contact: DONG Ru-yi
About author:-
Supported by:
-

摘要/Abstract

摘要： 电动汽车充换电站调度优化问题一般采用群智能优化算法求解，但现有算法存在陷入局部最优、早熟收敛等缺陷，因此提出一种改进的蚱蜢算法:采用边界反弹机制，提高算法效率;引入正余弦搜索机制，加强算法的全局搜索能力;采用Lévy飞行对粒子进行随机扰动，防止种群陷入局部最优;采用非线性收敛策略加快算法后期的收敛速度.实验结果表明，该算法在电动汽车充换电站调度优化问题上，性能优于原始蚱蜢算法以及其他现有群智能算法.

关键词: 电动汽车, 充换电站, 优化调度, 群智能, 蚱蜢算法

Abstract: The dispatch of electric vehicle battery swapping station is usually optimized by swarm intelligence algorithms. However， the existing algorithms are easily trapped in local optimum and premature convergence. Thus， an improved grasshopper optimization algorithm(IGOA)is proposed to achieve optimal dispatch. In the IGOA， the boundary bounce strategy is adopted to improve the efficiency; the sine/cosine algorithm is introduced to enhance the global searching ability; the Lévy flight is applied to perturb the particles randomly to keep the algorithm from being trapped in local optimum; the nonlinear operation is used to accelerate the convergence rate at the later stage of the algorithm. The simulation results show that the IGOA outperforms GOA and several other swarm intelligence algorithms as to the optimal dispatch of electric vehicle battery swapping station.

Key words: electric vehicle, battery swapping station, optimal dispatch, swarm intelligence, grasshopper optimization algorithm

中图分类号:

TP319

王生生，张伟，董如意，李文辉. 改进蚱蜢算法在电动汽车充换电站调度中的应用[J]. 东北大学学报:自然科学版, 2020, 41(2): 170-175.

WANG Sheng-sheng， ZHANG Wei， DONG Ru-yi， LI Wen-hui. Modified Grasshopper Optimization Algorithm and Applications in Optimal Dispatch of Electric Vehicle Battery Swapping Station[J]. Journal of Northeastern University Natural Science, 2020, 41(2): 170-175.

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改进蚱蜢算法在电动汽车充换电站调度中的应用

Modified Grasshopper Optimization Algorithm and Applications in Optimal Dispatch of Electric Vehicle Battery Swapping Station

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