Skip to main content
SpringerLink
Log in

A hybrid chaotic quantum evolutionary algorithm for resource combinatorial optimization in manufacturing grid system

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Resource composition in manufacturing grid (MGrid) system is one of the recent critical issues of MGrid researches. Especially, resource combinatorial optimization (RCO) becomes more challenging when multiple optimal criteria are considered in MGrid system. Based on the quantum evolution theory, we propose a hybrid chaotic quantum evolutionary algorithm (CQEA) for RCO problems. We also propose a novel resource encoding method for CQEA, which is dynamic and flexible. The experimental results show that the proposed CQEA is effective, efficient, and scalable for the RCO problem in MGrid system.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Fan Y, Zhao D, Zhang L, Huang S and Liu B (2004) Manufacturing grid needs, concept, and architecture. Lecture Notes in Computer Science. Springer, Heidelberg, 3032:653–654

  2. Qiu RG (2004) Manufacturing grid: a next generation manufacturing. IEEE International Conference System, Man and Cybemetics. The Hague, The Netherlands, Oct. 10–13, pp. 4667–4672

  3. Zhang HJ, Hu YF, Tao F, Zhou ZD (2008) Study on semantic-aware manufacturing grid architecture. Fuzzy Systems and Knowledge Discovery, Jinan, China, Oct. 18–20, pp. 626–630

  4. Ding YF, Tao F, Sheng BY, Zhou ZD (2008) Modelling and application of optimal-selection evaluation for manufacturing grid resource. Int J Comput Integr Manuf 21(1):62–72

    Google Scholar 

  5. Tao F, Hu YF, Zhao DM, Zhou ZD, Zhang HJ, Lei Z (2009) Study on manufacturing grid resource service QoS modeling and evaluation. Int J Adv Manuf Technol 41(9–10):1034–1042

    Article  Google Scholar 

  6. Tao F, Hu YF, Zhao DM, Zhou ZD (2009) An approach to manufacturing grid resource service scheduling based on trust-QoS. Int J Comput Integr Manuf 22(2):100–111

    Article  Google Scholar 

  7. Zhang HJ, Hu YF, Zhou ZD (2009) Research on Co-reservation in the manufacturing grid system. Int J Adv Manuf Technol 47(5–8):699–717

    Google Scholar 

  8. Tao F, Hu YF, Zhao DM, Zhou ZD (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Transactions on Industrial Informatics 4(4):315–327

    Article  Google Scholar 

  9. Wieczorek M, Hoheisel A, Prodan R (2009) Towards a general model of the multi-criteria workflow scheduling on the grid. Future Gener Comput Syst 25(3):237–256

    Article  Google Scholar 

  10. T’kindt V, Billaut J (2002) Multicriteria scheduling. Springer, Berlin

    MATH  Google Scholar 

  11. Yu J, Buyya R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program 14(3–4):217–230

    Google Scholar 

  12. Masutti TAS, de Castro LN (2009) A self-organizing neural network using ideas from the immune system to solve the traveling salesman problem. Inf Sci 179(10):1454–1468

    Article  Google Scholar 

  13. Shu W (2009) Quantum-inspired genetic algorithm based on simulated annealing for combinatorial optimization problem. International Journal of Distributed Sensor Networks 5(1):64–65

    Article  Google Scholar 

  14. DiVincenzo DP (1995) Quantum computation. Science 270(5234):255–261

    Article  MathSciNet  Google Scholar 

  15. Han KH, Kim JH (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6(6):580–593

    Article  Google Scholar 

  16. Xiao J, Xu J, Chen Z, Zhang K, Pan L (2009) A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding. Comput Math Appl 57(11–12):1949–1958

    Article  Google Scholar 

  17. Li BB, Ling W (2006) A hybrid quantum-inspired genetic algorithm for multi-objective scheduling. Lecture Notes in Computer Science. : Springer, Berlin, 4113:511–522

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

    Article  Google Scholar 

  19. Vlachogiannis JG, Lee KY (2008) Quantum-inspired evolutionary algorithm for real and reactive power dispatch. IEEE Trans Power Syst 23(4):1627–1636

    Article  Google Scholar 

  20. Zeng L, Benatallah B, Ngu AHH, Dumas M, Kalagnanam J, Chang H (2004) QoS-aware middleware for Web services composition. IEEE Trans Softw Eng 30(5):311–327

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Haijun Zhang & Yefa Hu

  2. Hubei Digital Manufacturing Key Laboratory, Wuhan, 430070, China

    Haijun Zhang & Yefa Hu

Authors
  1. Haijun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yefa Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haijun Zhang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, H., Hu, Y. A hybrid chaotic quantum evolutionary algorithm for resource combinatorial optimization in manufacturing grid system. Int J Adv Manuf Technol 52, 821–831 (2011). https://doi.org/10.1007/s00170-010-2742-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-010-2742-z

Keywords

Search

Navigation