TY - GEN
T1 - Quantum Inspired Binary Atom Search Optimization Algorithm for Charging Station Placement Problem
AU - Asna, Madathodika
AU - Shareef, Hussain
AU - Prasanthi, Achikkulath
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Charging station location and capacity optimization is a critical issue in the field of electric vehicle (EV) infrastructure planning as it involves multiple objectives and constraints. In this case, finding optimal solution for charging station placement problem requires an efficient optimization tool that could find quality solutions in short time. With this in mind, this work proposes a novel binary variant of atom search optimization (ASO) algorithm, utilizing the concept of quantum binarization technique, namely quantum-inspired binary ASO (QBASO) algorithm. The proposed QBASO possesses the same structure as original ASO algorithm, but the binarization procedure is performed using quantum gates and quantum bits. The performance of the proposed QBASO is tested using well-known benchmark functions and is then applied on EV charging station placement problem. The experimental results and statistical analyses show that QBASO algorithm is a promising optimization technique for solving charging station placement problem.
AB - Charging station location and capacity optimization is a critical issue in the field of electric vehicle (EV) infrastructure planning as it involves multiple objectives and constraints. In this case, finding optimal solution for charging station placement problem requires an efficient optimization tool that could find quality solutions in short time. With this in mind, this work proposes a novel binary variant of atom search optimization (ASO) algorithm, utilizing the concept of quantum binarization technique, namely quantum-inspired binary ASO (QBASO) algorithm. The proposed QBASO possesses the same structure as original ASO algorithm, but the binarization procedure is performed using quantum gates and quantum bits. The performance of the proposed QBASO is tested using well-known benchmark functions and is then applied on EV charging station placement problem. The experimental results and statistical analyses show that QBASO algorithm is a promising optimization technique for solving charging station placement problem.
KW - atom search optimization algorithm
KW - charging station placement problem
KW - quantum computing
KW - Quantum inspired algorithm
UR - http://www.scopus.com/inward/record.url?scp=85195456724&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85195456724&partnerID=8YFLogxK
U2 - 10.1109/TrustCom60117.2023.00327
DO - 10.1109/TrustCom60117.2023.00327
M3 - Conference contribution
AN - SCOPUS:85195456724
T3 - Proceedings - 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom/BigDataSE/CSE/EUC/iSCI 2023
SP - 2315
EP - 2322
BT - Proceedings - 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom/BigDataSE/CSE/EUC/iSCI 2023
A2 - Hu, Jia
A2 - Min, Geyong
A2 - Wang, Guojun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2023
Y2 - 1 November 2023 through 3 November 2023
ER -