TY - JOUR
T1 - Locating electric vehicle charging stations with service capacity using the improved whale optimization algorithm
AU - Zhang, Hao
AU - Tang, Lei
AU - Yang, Chen
AU - Lan, Shulin
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/8
Y1 - 2019/8
N2 - This study proposes an Improved Whale Optimization Algorithm (IWOA) that, on the basis of Whale Optimization Algorithm (WOA) designed by Mirjalili and Lewis (2016), introduces Gaussian mutation operator, differential evolution operator, and crowding degree factor to the algorithm framework. Test results with nine classic examples show that IWOA significantly improves WOA's precision and computing speed. We also model the locating problem of Electric Vehicle (EV) charging stations with service risk constraints and apply IWOA to solve it. This paper introduces service risk factors, which include the risk of service capacity and user anxiety, establishing the EV charging station site selection model considering service risk. Computational results based on a large-scale problem instance suggest that both the model and the algorithm are effective to apply in practical locating planning projects and help reduce social costs.
AB - This study proposes an Improved Whale Optimization Algorithm (IWOA) that, on the basis of Whale Optimization Algorithm (WOA) designed by Mirjalili and Lewis (2016), introduces Gaussian mutation operator, differential evolution operator, and crowding degree factor to the algorithm framework. Test results with nine classic examples show that IWOA significantly improves WOA's precision and computing speed. We also model the locating problem of Electric Vehicle (EV) charging stations with service risk constraints and apply IWOA to solve it. This paper introduces service risk factors, which include the risk of service capacity and user anxiety, establishing the EV charging station site selection model considering service risk. Computational results based on a large-scale problem instance suggest that both the model and the algorithm are effective to apply in practical locating planning projects and help reduce social costs.
KW - Crowding factor
KW - Differential evolution
KW - Electric vehicle charging station location
KW - Gaussian variation
KW - Service risk
KW - Whale optimization algorithm
UR - http://www.scopus.com/inward/record.url?scp=85066154035&partnerID=8YFLogxK
U2 - 10.1016/j.aei.2019.02.006
DO - 10.1016/j.aei.2019.02.006
M3 - Article
AN - SCOPUS:85066154035
SN - 1474-0346
VL - 41
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
M1 - 100901
ER -