Research on Optimum Algorithm of Charging Pile Location for New Energy Electric Vehicle

Content Provider	Scilit
Author	Guo, Shufen Zhu, Lizong Jiang, Suping Li, Biqing
Copyright Year	2019
Description	Journal: Iop Conference Series: Materials Science and Engineering Artificial intelligence algorithms such as ant colony algorithm and neural network do not need to rely on a large amount of gradient information when solving, especially for large-scale complex optimization problems which are difficult to solve by traditional methods, which provides a new perspective and thinking direction for solving such problems. Based on the analysis of the principles and advantages and disadvantages of RBF neural network and ant colony algorithm, this paper proposes a RBF neural network based on genetic mutation improved ant colony clustering algorithm to evaluate the location of charging station. The improved ant colony clustering algorithm based on genetic variation is used to determine the number of hidden layers of RBF neural network, so as to solve the problem that the initial parameters of RBF neural network can not be accurately selected without scientific methods. An example is used to prove the scientific and effectiveness of this method. Finally, the optimal scheme of charging station location is determined by comparing the comprehensive ranking values obtained by various methods used in this paper to judge the advantages and disadvantages of charging station location.
Related Links	https://iopscience.iop.org/article/10.1088/1757-899X/677/3/032087/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/677/3/032087
Journal	Iop Conference Series: Materials Science and Engineering
Issue Number	3
Volume Number	677
Language	English
Publisher	IOP Publishing
Publisher Date	2019-12-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering Industrial Engineering Algorithm and Neural Network Charging Station
Content Type	Text
Resource Type	Article