Multi-objective quantum atom search optimization algorithm for electric vehicle charging station planning
Madathodika Asna
Department of Electrical Engineering, College of Engineering, UAE University, Al Ain, United Arab Emirates
Search for more papers by this authorCorresponding Author
Hussain Shareef
Department of Electrical Engineering, College of Engineering, UAE University, Al Ain, United Arab Emirates
Correspondence
Hussain Shareef, Department of Electrical Engineering, College of Engineering, UAE University, Al Ain, United Arab Emirates.
Email: [email protected]
Search for more papers by this authorMunir Azam Muhammad
Faculty of Engineering Sciences and Technology, Iqra University, Karachi City, Pakistan
Search for more papers by this authorLeila Ismail
Department of Computer Science & Software Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates
Search for more papers by this authorAchikkulath Prasanthi
Department of Electrical Engineering, College of Engineering, UAE University, Al Ain, United Arab Emirates
Search for more papers by this authorMadathodika Asna
Department of Electrical Engineering, College of Engineering, UAE University, Al Ain, United Arab Emirates
Search for more papers by this authorCorresponding Author
Hussain Shareef
Department of Electrical Engineering, College of Engineering, UAE University, Al Ain, United Arab Emirates
Correspondence
Hussain Shareef, Department of Electrical Engineering, College of Engineering, UAE University, Al Ain, United Arab Emirates.
Email: [email protected]
Search for more papers by this authorMunir Azam Muhammad
Faculty of Engineering Sciences and Technology, Iqra University, Karachi City, Pakistan
Search for more papers by this authorLeila Ismail
Department of Computer Science & Software Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates
Search for more papers by this authorAchikkulath Prasanthi
Department of Electrical Engineering, College of Engineering, UAE University, Al Ain, United Arab Emirates
Search for more papers by this authorFunding information: This research was funded by the United Arab Emirates University with fund code 31R224-RTTSC (1)-2019.
Summary
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.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
Open Research
DATA AVAILABILITY STATEMENT
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Supporting Information
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| er8399-sup-0001-Supinfo.docxWord 2007 document , 172.1 KB | Appendix S1 Supplementary Information |
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