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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 853430, 8 pages
http://dx.doi.org/10.1155/2013/853430
Research Article

Sensor Scheduling with Intelligent Optimization Algorithm Based on Quantum Theory

1Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, School of IoT Engineering, Jiangnan University, Wuxi 214122, China
2Research Centre of Environment Science and Engineering, Wuxi 214063, China

Received 18 July 2013; Revised 3 September 2013; Accepted 4 September 2013

Academic Editor: Ming Li

Copyright © 2013 Zhiguo Chen 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. H. S. Meyerhoff, F. Papenmeier, and M. Huff, “Object-based integration of motion information during attentive tracking,” Perception, vol. 42, pp. 119–121, 2013.
  2. G. Jahn, J. Wendt, M. Lotze, F. Papenmeier, and M. Huff, “Brain activation during spatial updating and attentive tracking of moving targets,” Brain and Cognition, vol. 78, no. 2, pp. 105–113, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Rana, S. Jasola, and R. Kumar, “A review on particle swarm optimization algorithms and their applications to data clustering,” Artificial Intelligence Review, vol. 35, no. 3, pp. 211–222, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. A. A. A. Esmin and S. Matwin, “HPSOM: a hybrid particle swarm optimization algorithm with genetic mutation,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 5, pp. 1919–1934, 2013.
  5. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995. View at Scopus
  6. R. Thangaraj, M. Pant, A. Abraham, and V. Snasel, “Modified particle swarm optimization with timevarying velocity vector,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 1, pp. 201–218, 2012. View at Scopus
  7. S. Baskar and P. N. Suganthan, “A novel concurrent particle swarm optimization,” in Proceedings of the Congress on Evolutionary Computation (CEC '04), vol. 1, pp. 792–796, June 2004. View at Scopus
  8. Y. V. Pehlivanoglu, “A new particle swarm optimization method enhanced with a periodic mutation strategy and neural networks,” IEEE Transactions on Evolutionary Computation, vol. 17, no. 3, pp. 436–452, 2013.
  9. Y. Zhang and L. Wu, “A hybrid TS-PSO optimization algorithm,” Journal of Convergence Information Technology, vol. 6, no. 5, pp. 169–174, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME, vol. 82, no. 1, pp. 35–45, 1960.
  11. L. Xie, P. Yang, T. Yang, and M. Li, “Dual-EKF-based real-time celestial navigation for lunar rover,” Mathematical Problems in Engineering, vol. 2012, Article ID 578719, 16 pages, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  12. M. Li, “Approximating ideal filters by systems of fractional order,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 365054, 6 pages, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  13. M. Li, S. C. Lim, and S. Chen, “Exact solution of impulse response to a class of fractional oscillators and its stability,” Mathematical Problems in Engineering, vol. 2011, Article ID 657839, 9 pages, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  14. A. C. Nearchou, “Solving the single machine total weighted tardiness scheduling problem using a hybrid simulated annealing algorithm,” in Proceedings of the 2nd IEEE International Conference on Industrial Informatics (INDIN '04), pp. 513–516, June 2004. View at Scopus
  15. D. P. Bertsekas, Dynamic Programming and Optimal Control. Volume 1, Athena Scientific, Nashua, NH, USA, 3rd edition, 2005. View at MathSciNet
  16. H. T. Yao, H. Q. Chen, and T. F. Qin, “Niche PSO particle filter with particles fusion for target tracking,” Applied Mechanics and Materials, vol. 239, pp. 1368–1372, 2013.
  17. M. J. A. Hasan and S. Ramakrishnan, “A survey: hybrid evolutionary algorithms for cluster analysis,” Artificial Intelligence Review, vol. 36, no. 3, pp. 179–204, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. F. S. Levin, An Introduction to Quantum Theory, Cambridge University Press, New York, NY, USA, 2002. View at MathSciNet
  19. Z. Chen, Y. Bo, P. Wu, and W. Zhou, “A new particle filter based on organizational adjustment particle swarm optimization,” Applied Mathematics & Information Sciences, vol. 7, no. 1, pp. 179–186, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  20. Y. Bo, Z. Chen, J. Zhang, and J. Zhu, “New clone particle swarm optimization-based particle filter algorithm and its application,” Applied Mathematics & Information Sciences, vol. 7, no. 1, pp. 171–177, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  21. S. M. Mikki and A. A. Kishk, “Quantum particle swarm optimization for electromagnetics,” IEEE Transactions on Antennas and Propagation, vol. 54, no. 10, pp. 2764–2775, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281–295, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240–255, 2004. View at Publisher · View at Google Scholar · View at Scopus