Qubit Adoption Method of a Quantum Computing-Based Metaheuristics Algorithm for Truss Structures Analysis
Source abstract
Abstract
Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Topology Optimization in Engineering · Advanced Decision-Making Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Industrial Design & Architectural Engineering, Korea University of Technology & Education, 1600 Chungjeol-ro, Byeongcheon-myeon, Cheonan 31253, Republic of Korea
Funding
Ministry of Science and ICT
References (33)
Show source-provided references
1. Quantum computing10.1088/0034-4885/61/2/002 ↗
2. Simulating physics with computers10.1007/BF02650179 ↗
3. Quantum theory, the Church–Turing principle and the universal quantum computer10.1098/rspa.1985.0070 ↗
4. Quantum-inspired metaheuristic algorithms: Comprehensive survey and classification10.1007/s10462-022-10280-8 ↗
5. Horowitz, M., Grumbling, E., and National Academies of Sciences, Engineering, and Medicine (2019). Quantum Computing: Progress and Prospects, National Academies Press.10.17226/25196 ↗
6. Basics of quantum computation10.1016/S0079-6727(98)00004-4 ↗
7. McMahon, D. (2007). Quantum Computing Explained, John Wiley & Sons.10.1002/9780470181386 ↗
8. Choi, M.S. (2022). A Quantum Computation Workbook, Springer.10.1007/978-3-030-91214-7 ↗
9. Hidary, J.D., and Hidary, J.D. (2019). Quantum Computing: An Applied Approach, Springer.10.1007/978-3-030-23922-0 ↗
10. A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services10.1007/s00500-019-04155-4 ↗
11. Metaheuristic algorithms on feature selection: A survey of one decade of research (2009–2019)10.1109/ACCESS.2021.3056407 ↗
12. Lee, D., Kim, J., Shon, S., and Lee, S. (2023). An Advanced Crow Search Algorithm for Solving Global Optimization Problem. Appl. Sci., 13.10.3390/app13116628 ↗
13. Genetic quantum algorithm and its application to combinatorial optimization problem10.1109/CEC.2000.870809 ↗
14. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization10.1109/TEVC.2002.804320 ↗
15. A quantum particle swarm optimization10.1109/CEC.2004.1330874 ↗
16. A hybrid quantum inspired harmony search algorithm for 0–1 optimization problems10.1016/j.cam.2013.04.004 ↗
17. A quantum-inspired gravitational search algorithm for binary encoded optimization problems10.1016/j.engappai.2015.01.002 ↗
18. Gao, H., Zhang, X., Du, Y., and Diao, M. (2017, January 14–17). Quantum-inspired teaching-learning-based optimization for linear array pattern synthesis. Proceedings of the Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems, Harbin, China.
19. Metaheuristic research: A comprehensive survey10.1007/s10462-017-9605-z ↗
20. Hakemi, S., Houshmand, M., KheirKhah, E., and Hosseini, S.A. (2022). A review of recent advances in quantum-inspired metaheuristics. Evol. Intell., 1–16.10.1007/s12065-022-00783-2 ↗
21. A review of quantum-inspired metaheuristics: Going from classical computers to real quantum computers10.1109/ACCESS.2019.2962155 ↗
22. Mohammed, A.M., Elhefnawy, N., El-Sherbiny, M.M., and Hadhoud, M.M. (2012, January 14–16). Quantum crossover based quantum genetic algorithm for solving non-linear programming. Proceedings of the 2012 8th International Conference on Informatics and Systems (INFOS), Giza, Egypt.
23. Hakemi, S., Houshmand, M., Hosseini, S.A., and Zhou, X. (2023). A Modified Quantum-Inspired Genetic Algorithm Using Lengthening Chromosome Size and an Adaptive Look-Up Table to Avoid Local Optima. Axioms, 12.10.3390/axioms12100978 ↗
24. Yu, L., Ren, J., and Zhang, J. (2023). A Quantum-Based Beetle Swarm Optimization Algorithm for Numerical Optimization. Appl. Sci., 13.10.3390/app13053179 ↗
25. Wong, W., and Ming, C.I. (2019, January 28–30). A review on metaheuristic algorithms: Recent trends, benchmarking and applications. Proceedings of the 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia.10.1109/ICSCC.2019.8843624 ↗
26. A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems10.1016/j.swevo.2021.100888 ↗
27. A survey of recently developed metaheuristics and their comparative analysis10.1016/j.engappai.2022.105622 ↗
28. Lee, D., Shon, S., Lee, S., and Ha, J. (2023). Size and Topology Optimization of Truss Structures Using Quantum-Based HS Algorithm. Buildings, 13.10.3390/buildings13061436 ↗
29. Lee, S., Ha, J., Shon, S., and Lee, D. (2023). Weight Optimization of Discrete Truss Structures Using Quantum-Based HS Algorithm. Buildings, 13.10.3390/buildings13092132 ↗
30. A new heuristic optimization algorithm: Harmony search10.1177/003754970107600201 ↗
31. Quantum-inspired evolutionary algorithms with a new termination criterion, Hϵ gate, and two-phase scheme10.1109/TEVC.2004.823467 ↗
32. Truss topology optimization with static and dynamic constraints using modified subpopulation teaching–learning-based optimization10.1080/0305215X.2016.1150468 ↗
33. Modified meta-heuristics using random mutation for truss topology optimization with static and dynamic constraints