Open AccessResearch on Optimum Algorithm of Charging Pile Location for New Energy Electric Vehicle
Author(s) -
Shufen Guo,
Lizong Zhu,
Suping Jiang,
Biqing Li
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/677/3/032087
Subject(s) - ant colony optimization algorithms , artificial neural network , computer science , cluster analysis , genetic algorithm , ant colony , ranking (information retrieval) , artificial intelligence , data mining , algorithm , mathematical optimization , machine learning , mathematics
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.