On Parallel Immune Quantum Evolutionary Algorithm Based on Learning Mechanism and Its Convergence | SpringerLink
Skip to main content
Springer Nature Link
Log in

On Parallel Immune Quantum Evolutionary Algorithm Based on Learning Mechanism and Its Convergence

  • Conference paper
Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4221))

Included in the following conference series:

Abstract

A novel Multi-universe Parallel Immune Quantum Evolutionary Algorithm based on Learning Mechanism (MPMQEA) is proposed, in the algorithm, all individuals are divided into some independent sub-colonies, called universes. Their topological structure is defined, each universe evolving independently uses the immune quantum evolutionary algorithm, and information among the universes is exchanged by adopting emigration based on the learning mechanism and quantum interaction simulating entanglement of quantum. It not only can maintain quite nicely the population diversity, but also can help to accelerate the convergence speed and converge to the global optimal solution rapidly. The convergence of the MPMQEA is proved and its superiority is shown by some simulation experiments in this paper.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Narayanan, A., Moore, M.: Genetic quantum algorithm and its application to combinatorial optimization problem. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC1996), pp. 61–66. IEEE Press, Los Alamitos (1996)

    Chapter  Google Scholar 

  2. You, X.M., Shuai, D.X., Liu, S.: Research and Implementation of Quantum Evolution Algorithm Based on Immune Theory. In: Proceedings of the 6th World Congress on Intelligent Control and Automation (WCICA 2006), Da Lian, China (2006) (accepted for publication).

    Google Scholar 

  3. Han, K.H., Kim, J.H.: Quantum-Inspired Evolutionary Algorithms with a New Termination Criterion, Hε Gate, and Two-Phase Scheme. IEEE Transactions on Evolutionary Computation 8, 156–169 (2004)

    Article  Google Scholar 

  4. Fukuda, T., Mori, K., Tsukiyama, M.: Parallel search for multi-modal function optimization with diversity and learning of immune algorithm. In: Artificial Immune Systems and Their Applications, pp. 210–220. Springer, Berlin (1999)

    Google Scholar 

  5. Mori, K., Tsukiyama, M., Fukuda, T.: Adaptive scheduling system inspired by immune systems. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, vol. 3833–3837, pp. 12–14 (1998)

    Google Scholar 

  6. Ada, G.L., Nossal, G.J.V.: The clonal selection theory. Scientific American 257, 50–57 (1987)

    Article  Google Scholar 

  7. Enrique, A., Jose, M.T.: Improving flexibility and efficiency by adding parallelism to genetic algorithms. Statistics and Computing 12, 91–114 (2002)

    Article  MathSciNet  Google Scholar 

  8. Han, K.H., Kirn, J.H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation 6, 580–593 (2002)

    Article  Google Scholar 

  9. Pan, Z.J., Kang, L.S., Chen, Y.: Evolutionary Computation [M]. Tsinghua University Press, Beijing (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept of Computer Science and Technology, East China University of Science and Technology, Shanghai, 200237, China

    Xiaoming You, Sheng Liu & Dianxun Shuai

  2. College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 200065, China

    Xiaoming You & Sheng Liu

Authors
  1. Xiaoming You
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dianxun Shuai
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., 710071, Xi’an, P.R. China

    Xin-bo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

You, X., Liu, S., Shuai, D. (2006). On Parallel Immune Quantum Evolutionary Algorithm Based on Learning Mechanism and Its Convergence. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_119

Download citation

  • DOI: https://doi.org/10.1007/11881070_119

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45901-9

  • Online ISBN: 978-3-540-45902-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics