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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 712752, 22 pages
http://dx.doi.org/10.1155/2012/712752
Research Article

Test-Sheet Composition Using Analytic Hierarchy Process and Hybrid Metaheuristic Algorithm TS/BBO

1School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
2Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
3Graduate School of Chinese Academy of Sciences, Beijing 100039, China
4School of Electronic and Information Engineering, Yili Normal University, Yining, Xinjiang 835000, China

Received 10 June 2012; Accepted 27 August 2012

Academic Editor: Jun-Juh Yan

Copyright © 2012 Hong Duan 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. G. J. Hwang, B. M. T. Lin, and T. L. Lin, “An effective approach for test-sheet composition with large-scale item banks,” Computers & Education, vol. 46, no. 2, pp. 122–139, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. C. Lin, Y. T. Lin, and Y. M. Huang, “Development of a diagnostic system using a testing-based approach for strengthening student prior knowledge,” Computers & Education, vol. 57, no. 2, pp. 1557–1570, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. E. S. M. El-Alfy and R. E. Abdel-Aal, “Construction and analysis of educational tests using abductive machine learning,” Computers & Education, vol. 51, no. 1, pp. 1–16, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. F. Wang, W. H. Wang, Q. K. Pan, and G. Cheng, “Intelligent test-sheet composition research based on harmony search algorithm,” Computer Simulation, vol. 27, pp. 298–301, 2010.
  5. Y. Liu, Y. Wang, Y. Du, and J. Zhang, “Multi-object intellectual test paper assembling based on adaptive operator genetic algorithm,” Computer Applications, no. S1, pp. 22–24, 2008.
  6. H. Y. Lin, J. M. Su, and S. S. Tseng, “An adaptive test sheet generation mechanism using genetic algorithm,” Mathematical Problems in Engineering, vol. 2012, Article ID 820190, 18 pages, 2012.
  7. T. F. Ho, P. Y. Yin, G. J. Hwang, S. J. Shyu, and Y. N. Yean, “Multi-objective parallel test-sheet composition using enhanced particle swarm optimization,” Educational Technology & Society, vol. 12, no. 4, pp. 193–206, 2009. View at Scopus
  8. D. Simon, “Biogeography-based optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702–713, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Wang, L. Guo, H. Duan, L. Liu, and H. Wang, “Dynamic deployment of wireless sensor networks by biogeography based optimization algorithm,” Journal of Sensor and Actuator Networks, vol. 1, no. 2, pp. 86–96, 2012. View at Publisher · View at Google Scholar
  10. G. Wang, L. Guo, H. Duan, L. Liu, H. Wang, and M. Shao, “Path planning for uninhabited combat aerial vehicle using hybrid meta-heuristic DE/BBO algorithm,” Advanced Science, Engineering and Medicine, vol. 4, no. 6, pp. 550–564, 2012.
  11. X. Li, J. Wang, J. Zhou, and M. Yin, “A perturb biogeography based optimization with mutation for global numerical optimization,” Applied Mathematics and Computation, vol. 218, no. 2, pp. 598–609, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  12. G. J. Hwang, H. C. Chu, P. Y. Yin, and J. Y. Lin, “An innovative parallel test sheet composition approach to meet multiple assessment criteria for national tests,” Computers & Education, vol. 51, no. 3, pp. 1058–1072, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. T. L. Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York, NY, USA, 1980.
  14. F. Zahedi, “The analytic hierarchy process-a survey of the method and its applications,” Interfaces, vol. 16, no. 4, pp. 96–108, 1986.
  15. M. V. Lomolino, B. R. Riddle, R. J. Whittaker, and J. H. Brown, Biogeography, Sinauer Associates, Sunderland, Mass, USA, 4th edition, 2010.
  16. H. Ma, “An analysis of the equilibrium of migration models for biogeography-based optimization,” Information Sciences, vol. 180, no. 18, pp. 3444–3464, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  17. D. Simon, “The Matlab code of biogeography-based optimization,” http://academic.csuohio.edu/simond/bbo/.
  18. F. Glover, “Tabu search-part I,” ORSA Journal on Computing, vol. 1, no. 3, pp. 190–206, 1989. View at Publisher · View at Google Scholar
  19. M. Dorigo, M. Birattari, and T. Stützle, “Ant colony optimization—artificial ants as a computational intelligence technique,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Dorigo, L. M. Gambardella, M. Middendorf, and T. Stützle, “Guest editorial: special section on ant colony optimization,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, pp. 317–320, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Ketabi and R. Feuillet, “Ant colony search algorithm for optimal generators startup during power system restoration,” Mathematical Problems in Engineering, vol. 2010, Article ID 906935, 11 pages, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. Z. Rui and W. Cheng, “A hybrid differential evolution and tree search algorithm for the job shop scheduling problem,” Mathematical Problems in Engineering, vol. 2011, Article ID 390593, 20 pages, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  23. A. Ketabi and M. J. Navardi, “Optimization shape of variable capacitance micromotor using differential evolution algorithm,” Mathematical Problems in Engineering, vol. 2010, Article ID 909240, 15 pages, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  24. W.-H. Ho and A. L.-F. Chan, “Hybrid Taguchi-differential evolution algorithm for parameter estimation of differential equation models with application to HIV dynamics,” Mathematical Problems in Engineering, vol. 2011, Article ID 514756, 14 pages, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  25. A. H. Gandomi, X.-S. Yang, S. Talatahari, and S. Deb, “Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization,” Computers & Mathematics with Applications, vol. 63, no. 1, pp. 191–200, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  26. T. Bäck, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, Oxford, Miss, USA, 1996.
  27. H.-G. Beyer, The Theory of Evolution Strategies, Springer, New York, NY, USA, 2001.
  28. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
  29. P. C. Chen, C. W. Chen, and W. L. Chiang, “GA-based fuzzy sliding mode controller for nonlinear systems,” Mathematical Problems in Engineering, vol. 2008, Article ID 325859, 16 pages, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  30. S. T. Pan, “CSD-coded genetic algorithm on robustly stable multiplierless IIR filter design,” Mathematical Problems in Engineering, vol. 2012, Article ID 560650, 15 pages, 2012.
  31. M. Shahsavar, A. A. Najafi, and S. T. A. Niaki, “Statistical design of genetic algorithms for combinatorial optimization problems,” Mathematical Problems in Engineering, vol. 2011, Article ID 872415, 17 pages, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  32. A. H. Gandomi and A. H. Alavi, “Multi-stage genetic programming: a new strategy to nonlinear system modeling,” Information Sciences, vol. 181, no. 23, pp. 5227–5239, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. I. C. Parmee, Evolutionary and Adaptive Computing in Engineering Design, Springer, Berlin, Germany, 2001.
  34. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1945, Perth, Australia, 1995.
  35. W. Khatib and P. Fleming, “The stud GA: a mini revolution?” in Proceedings of the 5th International Conference on Parallel Problem Solving from Nature, pp. 683–691, Springer, 1998.