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

被引：0

作者：

Wang S.-S. ^{[1
,2
]}

Zhang W. ^{[2
]}

Dong R.-Y. ^{[1
,3
]}

Li W.-H. ^{[1
]}

机构：

[1] College of Computer Science and Technology, Jilin University, Changchun

[2] College of Software, Jilin University, Changchun

[3] College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2020年 / 41卷 / 02期

关键词：

Battery swapping station; Electric vehicle; Grasshopper optimization algorithm; Optimal dispatch; Swarm intelligence;

D O I：

10.12068/j.issn.1005-3026.2020.02.004

中图分类号：

学科分类号：

摘要：

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. © 2020, Editorial Department of Journal of Northeastern University. All right reserved.

引用

页码：170 / 175

页数：5

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