Skip to main content
SpringerLink
Log in

Quantum Genetic Algorithm with Fuzzy Control Based on Clustering Analysis

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14267))

Included in the following conference series:

  • 462 Accesses

Abstract

Finding the solution of NP hard problem is an interesting puzzle, and numerous evolutionary algorithms are proposed to accelerate the speed of searching. In recent decades, the quantum computer technology has gradually improved, which is a new tool to reduce the searching time. In order to extend the genetic algorithm in quantum computer for faster convergence, the quantum genetic algorithm with fuzzy control based on clustering analysis is proposed. The operations of genetic algorithm are replaced by the quantum gate circuit First, quantum genetic algorithm is put forward, and the quantum superposition is applied to replace the mutation operation in genetic algorithm. Second, clustering analysis is employed to depict the distribution of solutions. Third, fuzzy control is used to adjust the reproduction parameter adaptively based on the results of clustering analysis. The experiments are designed for proving the performance of the proposed method with different algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alhanjouri, M.A., Alfarra, B.: Ant colony versus genetic algorithm based on travelling salesman problem. Int. J. Comput. Tech. Appl 2(3), 570–578 (2013)

    Google Scholar 

  2. Huang, T., Gong, Y.-J., Chen, W.-N., Wang, H., Zhang, J.: A probabilistic niching evolutionary computation framework based on binary space partitioning. IEEE Trans. Cybern. 52(1), 51–64 (2020)

    Article  Google Scholar 

  3. Chen, Z.-G., Zhan, Z.-H., Wang, H., Zhang, J.: Distributed individuals for multiple peaks: A novel differential evolution for multimodal optimization problems. IEEE Trans. Evol. Comput. 24(4), 708–719 (2019)

    Article  Google Scholar 

  4. Liu, W.-L., Gong, Y.-J., Chen, W.-N., Liu, Z., Wang, H., Zhang, J.: Coordinated charging scheduling of electric vehicles: A mixed-variable differential evolution approach. IEEE Trans. Intell. Transp. Syst. 21(12), 5094–5109 (2019)

    Article  Google Scholar 

  5. Rao, T.S.: An evaluation of ACO and GA TSP in a supply chain network. Mater. Today Proc. 5(11), 25350–25357 (2018)

    Article  Google Scholar 

  6. Hacizade, U., Kaya, I.: Ga based traveling salesman problem solution and its application to transport routes optimization. IFAC-PapersOnLine 51(30), 620–625 (2018)

    Article  Google Scholar 

  7. Zhou, Z.-W., Ding, T.-M.: Research on holes machining path planning optimization with tsp and ga. Modular Mach. Tool Autom. Manuf. Tech. 66(7), 30–32 (2007)

    Google Scholar 

  8. Chen, Y., Wu, J.-F., He, C.-S., Zhang, S.: Intelligent warehouse robot path planning based on improved ant colony algorithm. IEEE Access 11, 12360–12367 (2023)

    Article  Google Scholar 

  9. Hu, X., Wu, H., Sun, Q.-L., Liu, J.: Robot time optimal trajectory planning based on improved simplified particle swarm optimization algorithm. IEEE Access 11, 44496–44508 (2023)

    Article  Google Scholar 

  10. Narayanan, A., Moore, M.: Quantum-inspired genetic algorithms. In: Proceedings of IEEE International Conference on Evolutionary Computation. IEEE, pp. 61–66 (1996)

    Google Scholar 

  11. Han, K.-H., Kim, J.-H.: Genetic quantum algorithm and its application to combinatorial optimization problem. In: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512). IEEE, vol. 2, pp. 1354–1360 (2000)

    Google Scholar 

  12. Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput. 6(6), 580–593 (2002)

    Article  Google Scholar 

  13. Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithms with a new termination criterion, H/sub/spl epsi//gate, and two-phase scheme. IEEE Trans. Evol. Comput. 8(2), 156–169 (2004)

    Article  MathSciNet  Google Scholar 

  14. Cruz, A., Vellasco, M., Pacheco, M.:Quantum-inspired evolutionary algorithm for numerical optimization. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 2630–2637 (2006)

    Google Scholar 

  15. Li, P., Li, S.: Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits. Neurocomputing 72(1–3), 581–591 (2008)

    Article  Google Scholar 

  16. Zhao, S., Xu, G., Tao, T., Liang, L.: Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks. Comput. Math. Appl. 57(11–12), 2009–2015 (2009)

    Google Scholar 

  17. Wang, Y., et al.: A novel quantum swarm evolutionary algorithm and its applications. Neurocomputing 70(4–6), 633–640 (2007)

    Article  Google Scholar 

  18. Zhang, G.-X., Gheorghe, M., Wu, C.-Z.: A quantum-inspired evolutionary algorithm based on P systems for knapsack problem. Fund. Inform. 87(1), 93–116 (2008)

    MathSciNet  MATH  Google Scholar 

  19. Zhao, D., Tao, F.: A new idea for addressing multi-objective combinatorial optimization: Quantum multi-agent evolutionary algorithms. In: 2009 43rd Annual Conference on Information Sciences and Systems. IEEE, pp. 28–31 (2009)

    Google Scholar 

  20. Hossain, M.A., Hossain, M.K., Hashem, M.: Hybrid real-coded quantum evolutionary algorithm based on particle swarm theory. In: 2009 12th International Conference on Computers and Information Technology. IEEE, pp. 13–18 (2009)

    Google Scholar 

  21. Kim, Y., Kim, J.-H., Han, K.-H.: Quantum-inspired multiobjective evolutionary algorithm for multiobjective 0/1 knapsack problems. In: 2006 IEEE International Conference on Evolutionary Computation. IEEE, pp. 2601–2606 (2006)

    Google Scholar 

  22. Wang, Y., Li, Y., Jiao, L.: Quantum-inspired multi-objective optimization evolutionary algorithm based on decomposition. Soft. Comput. 20(8), 3257–3272 (2016). https://doi.org/10.1007/s00500-015-1702-9

    Article  Google Scholar 

  23. Dey, S., Bhattacharyya, S., Maulik, U.: Quantum inspired nondominated sorting based multi-objective GA for multi-level image thresholding. Hybrid Metaheuristics: Research and Applications, pp. 141–170 (2018)

    Google Scholar 

  24. Konar, D., Sharma, K., Sarogi, V., Bhattacharyya, S.: A multi-objective quantum-inspired genetic algorithm (Mo-QIGA) for real-time tasks scheduling in multiprocessor environment. Procedia Comput. Sci. 131, 591–599 (2018)

    Article  Google Scholar 

  25. Liu, T., Sun, J., Wang, G., Lu, Y.: A multi-objective quantum genetic algorithm for MIMO radar waveform design. Remote Sens. 14(10), 2387 (2022)

    Article  Google Scholar 

Download references

Acknowledgment

The authors would like to thank the associate editor and reviewers for their valuable comments and suggestions that improved the paper’s quality.

Author information

Authors and Affiliations

  1. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China

    Weipeng Tang & Yisu Ge

  2. State Grid WenZhou Electric Power Supply Company, Wenzhou, China

    Yan Pan & Haojie Xu

Authors
  1. Weipeng Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haojie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yisu Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yisu Ge .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Huayong Yang

  2. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  3. Zhejiang University, Hangzhou, China

    Jun Zou

  4. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  5. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  6. Zhejiang University, Hangzhou, China

    Geng Yang

  7. Zhejiang University, Hangzhou, China

    Xiaoping Ouyang

  8. Harbin Institute of Technology, Shenzhen, China

    Zhiyong Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tang, W., Pan, Y., Xu, H., Ge, Y. (2023). Quantum Genetic Algorithm with Fuzzy Control Based on Clustering Analysis. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14267. Springer, Singapore. https://doi.org/10.1007/978-981-99-6483-3_28

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-6483-3_28

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6482-6

  • Online ISBN: 978-981-99-6483-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation