TY - JOUR
T1 - Multi-objective quantum atom search optimization algorithm for electric vehicle charging station planning
AU - Asna, Madathodika
AU - Shareef, Hussain
AU - Muhammad, Munir Azam
AU - Ismail, Leila
AU - Prasanthi, Achikkulath
N1 - Funding Information:
This research was funded by the United Arab Emirates University with fund code 31R224‐RTTSC (1)‐2019. Funding information
Publisher Copyright:
© 2022 John Wiley & Sons Ltd.
PY - 2022/10/10
Y1 - 2022/10/10
N2 - This paper presents an effective planning methodology for electric vehicle (EV) fast-charging stations (CS) using a multi-objective binary version of the atom search optimization (ASO) algorithm. The proposed method uses quantum operations to binarize the algorithm and achieve a higher convergence rate than the existing binary ASO algorithm. Additionally, a modified atom selection function is used to improve the searching capability of the ASO algorithm. Furthermore, the nondominated sorting procedure and pareto concepts are infused to solve the CS location problem (CSLP) considering the EV travel time, CS costs, and grid power loss as independent multi-objectives. The efficacy of the proposed multi-objective quantum ASO (MO-QASO) algorithm is evaluated using performance metrics namely, inverted generational distance (IGD), spacing (SP), and maximum spread (MS). The MO-QASO simulation results are compared with the results of other heuristic algorithms. MO-QASO achieves the best IGD (0.0021), SP (0.0002), and MS (0.9982) values, verifying the convergence and diversity of the algorithm. Importantly, the best CS planning solution obtained from MO-QASO is similar to the true solution obtained from the exhaustive search method. The MO-QASO efficiency is further validated by solving a CSLP from literature. Thus, the MO-QASO algorithm is a promising optimization tool for solving CSLP.
AB - This paper presents an effective planning methodology for electric vehicle (EV) fast-charging stations (CS) using a multi-objective binary version of the atom search optimization (ASO) algorithm. The proposed method uses quantum operations to binarize the algorithm and achieve a higher convergence rate than the existing binary ASO algorithm. Additionally, a modified atom selection function is used to improve the searching capability of the ASO algorithm. Furthermore, the nondominated sorting procedure and pareto concepts are infused to solve the CS location problem (CSLP) considering the EV travel time, CS costs, and grid power loss as independent multi-objectives. The efficacy of the proposed multi-objective quantum ASO (MO-QASO) algorithm is evaluated using performance metrics namely, inverted generational distance (IGD), spacing (SP), and maximum spread (MS). The MO-QASO simulation results are compared with the results of other heuristic algorithms. MO-QASO achieves the best IGD (0.0021), SP (0.0002), and MS (0.9982) values, verifying the convergence and diversity of the algorithm. Importantly, the best CS planning solution obtained from MO-QASO is similar to the true solution obtained from the exhaustive search method. The MO-QASO efficiency is further validated by solving a CSLP from literature. Thus, the MO-QASO algorithm is a promising optimization tool for solving CSLP.
KW - atom search optimization
KW - charging station planning
KW - pareto solutions
KW - power loss
KW - quantum binary
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U2 - 10.1002/er.8399
DO - 10.1002/er.8399
M3 - Article
AN - SCOPUS:85134933036
SN - 0363-907X
VL - 46
SP - 17308
EP - 17331
JO - International Journal of Energy Research
JF - International Journal of Energy Research
IS - 12
ER -