- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 853430, 8 pages
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.
- H. S. Meyerhoff, F. Papenmeier, and M. Huff, “Object-based integration of motion information during attentive tracking,” Perception, vol. 42, pp. 119–121, 2013.
- 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.
- 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.
- 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.
- J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995.
- 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.
- 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.
- 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.
- Y. Zhang and L. Wu, “A hybrid TS-PSO optimization algorithm,” Journal of Convergence Information Technology, vol. 6, no. 5, pp. 169–174, 2011.
- R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME, vol. 82, no. 1, pp. 35–45, 1960.
- 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.
- M. Li, “Approximating ideal filters by systems of fractional order,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 365054, 6 pages, 2012.
- 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.
- 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.
- D. P. Bertsekas, Dynamic Programming and Optimal Control. Volume 1, Athena Scientific, Nashua, NH, USA, 3rd edition, 2005.
- 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.
- 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.
- F. S. Levin, An Introduction to Quantum Theory, Cambridge University Press, New York, NY, USA, 2002.
- 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.
- 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.
- 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.
- 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.
- 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.