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

  • Xiaoming You
  • Sheng Liu
  • Dianxun Shuai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


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.


Learn Mechanism Markov Chain Model Immune Algorithm Affinity Maturation Immune Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 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)CrossRefGoogle Scholar
  2. 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. 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)CrossRefGoogle Scholar
  4. 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. 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. 6.
    Ada, G.L., Nossal, G.J.V.: The clonal selection theory. Scientific American 257, 50–57 (1987)CrossRefGoogle Scholar
  7. 7.
    Enrique, A., Jose, M.T.: Improving flexibility and efficiency by adding parallelism to genetic algorithms. Statistics and Computing 12, 91–114 (2002)CrossRefMathSciNetGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 9.
    Pan, Z.J., Kang, L.S., Chen, Y.: Evolutionary Computation [M]. Tsinghua University Press, Beijing (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaoming You
    • 1
    • 2
  • Sheng Liu
    • 1
    • 2
  • Dianxun Shuai
    • 1
  1. 1.Dept of Computer Science and TechnologyEast China University of Science and TechnologyShanghaiChina
  2. 2.College of Electronic and Electrical EngineeringShanghai University of Engineering ScienceShanghaiChina

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