Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 730749, 10 pages
http://dx.doi.org/10.1155/2013/730749
Research Article

The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization

1College of Field Engineering, PLA University of Science and Technology, Nanjing 210007, China
2Zhenjiang Watercraft College, Zhenjiang 212003, China

Received 22 January 2013; Revised 29 March 2013; Accepted 29 March 2013

Academic Editor: Zheng-Guang Wu

Copyright © 2013 Huaixiao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Shi, H. Wang, L. Yu, and F. Hu, Analyze of 30 Cases of MATLAB Intelligent Algorithms, Beihang University Press, Beijing, China, 2010.
  2. A. Narayanan and M. Moore, “Quantum-inspired genetic algorithms,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '96), pp. 61–66, Nagoya, Japan, May 1996. View at Scopus
  3. R. van Meter, K. Nemoto, and W. J. Munro, “Communication links for distributed quantum computation,” IEEE Transactions on Computers, vol. 56, no. 12, pp. 1643–1653, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Zhou, W. Pan, B. Luo, W. L. Zhang, and Y. Ding, “A novel quantum genetic algorithm based on particle swarm optimization method and its application,” Acta Electronica Sinica, vol. 34, no. 5, pp. 897–901, 2006. View at Google Scholar · View at Scopus
  5. J. Huang, R. A. Berry, and M. L. Honig, “Auction-based spectrum sharing,” Mobile Networks and Applications, vol. 11, no. 3, pp. 405–418, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Y. Yang, F. Liu, and L. C. Jiao, “Novel genetic algorithm based on the quantum chromosome,” Journal of Xidian University, vol. 31, no. 1, pp. 76–81, 2004. View at Google Scholar · View at Scopus
  7. J. G. Proakis, Digital Communications, Mc Graw-Hill, New York, NY, USA, 4th edition, 2000.
  8. Y. Xiong, H. H. Chen, F. Y. Miao, and X. F. Wang, “Quantum genetic algorithm to solve combinatorial optimization problem,” Acta Electronica Sinica, vol. 32, no. 11, pp. 1855–1858, 2004. View at Google Scholar · View at Scopus
  9. K. H. Han and J. H. Kim, “Genetic quantum algorithm and its application to combinatorial optimization problem,” in Proceedings of the Congress on Evolutionary Computation, pp. 1354–1360, July 2000. View at Scopus
  10. C. J. Rieser, Biologically Inspired Cognitive Radio Engine Model Utilizing Distributed Genetic Algorithms for Secure and Robust Wireless Communications and Networking, Virginia Tech, Blacksburg, Va, USA, 2004.
  11. S. Lou, Y. Li, Y. Wu, and X. Xiong, “Multi-objective reactive power optimization using quantum genetic algorithm,” High Voltage Engineering, vol. 31, no. 9, pp. 69–83, 2005. View at Google Scholar · View at Scopus
  12. G. X. Zhang, N. Li, and W. D. Jin, “A novel quantum genetic algorithm and it’s application,” Acta Electronica Sinica, vol. 32, no. 3, pp. 476–479, 2004. View at Google Scholar
  13. H. Zheng and C. Peng, “Collaboration and fairness in opportunistic spectrum access,” in Proceedings of the 40th Annual IEEE International Conference on Communications (ICC '05), vol. 5, pp. 3132–3136, Seoul, Korea, 2005.
  14. G. Zhang and H. Rong, “Quantum-inspired genetic algorithm based time-frequency atom decomposition,” in Proceedings of the 7th International Conference on Computational (ICCS '07), pp. 243–250, 2007.
  15. J.-A. Yang and Z.-Q. Zhuang, “Actuality of research on quantum genetic algorithm,” Journal of Computer Science & Technology, vol. 30, no. 11, pp. 13–15, 2003. View at Google Scholar
  16. N. H. Abbasy and H. M. Ismail, “A unified approach for the optimal PMU location for power system state estimation,” IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 806–813, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Shu and B. He, “A quantum genetic simulated annealing algorithm for task scheduling,” in Advances in Computation and Intelligence, vol. 4683 of Lecture Notes in Computer Science, pp. 169–176, 2007. View at Google Scholar
  18. F. Aminifar, C. Lucas, A. Khodaei, and M. Fotuhi-Firuzabad, “Optimal placement of phasor measurement units using immunity genetic algorithm,” IEEE Transactions on Power Delivery, vol. 24, no. 3, pp. 1014–1020, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. J. Zhao, S. L. Zheng, J. N. Shang, and X. Z. Kong, “Study of cognitive radio decision engine based on quantum genetic algorithm,” Acta Physica Sinica, vol. 56, no. 11, pp. 6760–6766, 2007 (Chinese). View at Google Scholar · View at Scopus
  20. J. Neel, Analysis and Design of Cognitive Radio Networks and Distributed Radio Resource Management Algorithms, Virginia Tech, Blacksburg, Va, USA, 2006.
  21. C. Peng, H. Zheng, and B. Y. Zhao, “Utilization and fairness in spectrum assignment for opportunistic spectrum access,” Mobile Networks and Applications, vol. 11, no. 4, pp. 555–576, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. C. L. Liao, J. Cheng, Y. X. Tang, and S. Q. Li, “Improved quantum genetic algorithm and its application,” Electronic and Information Technology, vol. 29, p. 1608, 2007. View at Google Scholar
  23. A. Narayanan and M. Moore, “Quantum-inspired genetic algorithms,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '96), pp. 61–66, May 1996. View at Scopus
  24. D. Aharonov, W. van Dam, J. Kempe, Z. Landau, S. Lloyd, and O. Regev, “Adiabatic quantum computation is equivalent to standard quantum computation,” in Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science (FOCS '04), pp. 42–51, October 2004. View at Scopus
  25. S. H. W. van der Ploeg, A. Izmalkov, M. Grajcar et al., “Adiabatic quantum computation with flux qubits, first experimental results,” IEEE Transactions on Applied Superconductivity, vol. 17, no. 2, pp. 113–119, 2007. View at Google Scholar
  26. Y. Li, Y. Zhang, Y. Cheng, and X. Jiang, “A novel immune quantum-inspired genetic algorithm,” in Advances in Natural Computation, vol. 3612 of Lecture Notes in Computer Science, pp. 215–218, Springer, Berlin, Germany, 2005. View at Google Scholar
  27. K. H. Han and J. H. Kim, “Genetic quantum algorithm and its application to combinatorial optimization problem,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1354–1360, San Diego, Calif, USA, July 2000. View at Scopus
  28. H. Miao, H. Wang, and Z. Deng, “Quantum genetic algorithm and its application in power system reactive power optimization,” in Proceedings of International Conference on Computational Intelligence and Security, pp. 107–111, Beijing, China, 2009.
  29. H. Talbi, M. Batouche, and A. Draa, “A quantum-inspired genetic algorithm for multi-source affine image registration,” in Image Analysis and Recognition, vol. 3211 of Lecture Notes in Computer Science, pp. 147–154, Springer, Berlin, Germany, 2004. View at Publisher · View at Google Scholar
  30. R. Zhou and J. Cao, “Quantum novel genetic algorithm based on parallel subpopulation computing and its application,” in Artificial Intelligence Review, Springer, Berlin, Germany, 2012. View at Google Scholar