A Hybrid Fish Swarm Optimisation Algorithm for Solving Examination Timetabling Problems

1. Abdullah, S., Burke, E.K.: A Multi-start large neighbourhood search approach with local search methods for examination timetabling. In: International Conference on Automated Planning and Scheduling (ICAPS 2006), Cumbria, UK, pp. 334–337 (2006)

   Google Scholar 

2. Abdullah, S., Shaker, K., McCollum, B., McMullan, P.: Dual sequence simulated annealing with round-robin approach for university course timetabling. In: Cowling, P., Merz, P. (eds.) EvoCOP 2010. LNCS, vol. 6022, pp. 1–10. Springer, Heidelberg (2010)

   Chapter  Google Scholar 

3. Turabieh, H., Abdullah, S.: An integrated hybrid approach to the examination timetabling problem. OMEGA (2011), doi:10.1016/j.omega.2010.12.005

   Google Scholar 

4. Burke, E.K., Bykov, Y.: Solving exam timetabling problems with the flex-deluge algorithm. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, pp. 370–372. Springer, Heidelberg (2007) ISBN: 80-210-3726-1

   Chapter  Google Scholar 

5. Burke, E.K., Eckersley, A.J., McCollum, B., Petrovic, S., Qu, R.: Hybrid variable neighbourhood approaches to university exam timetabling. European Journal of Operational Research 206, 46–53 (2010)

   Article  MathSciNet  MATH  Google Scholar 

6. Burke, E.K., Kingston, J., de Werra, D.: Applications to timetabling. In: Gross, J., Yellen, J. (eds.) Handbook of Graph Theory, pp. 445–474. Chapman Hall/CRC Press (2004)

   Google Scholar 

7. Caramia, M., Dell’Olmo, P., Italiano, G.F.: New algorithms for examination timetabling. In: Näher, S., Wagner, D. (eds.) WAE 2000. LNCS, vol. 1982, pp. 230–241. Springer, Heidelberg (2001)

   Chapter  Google Scholar 

8. Carter, M.W., Laporte, G., Lee, S.: Examination timetabling: Algorithmic strategies and applications. Journal of the Operational Research Society 47(3), 373–383 (1996)

   Article  Google Scholar 

9. Carter, M.W.: A survey of practical applications of examination timetabling algorithms. Operations Research 34(2), 193–202 (1986)

   Article  MathSciNet  Google Scholar 

10. Casey, S., Thompson, J.: GRASPing the examination scheduling problem. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 232–244. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

11. Côté, P., Wong, T., Sabourin, R.: A hybrid multi-objective evolutionary algorithm for the uncapacitated exam proximity problem. In: Burke, E.K., Trick, M.A. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 294–312. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

12. Dowsland, K., Thompson, J.: Ant colony optimization for the examination scheduling problem. Journal of the Operational Research Society 56(4), 426–438 (2005)

    Article  MATH  Google Scholar 

13. Dueck, G.: New Optimization Heuristics. The great deluge algorithm and the record-to-record travel. Journal of Computational Physics 104, 86–92 (1993)

    Article  MATH  Google Scholar 

14. Fernandes, E.M.G.P., Martins, T.F.M.C., Rocha, A.M.A.C.: Fish Swarm Intelligent Algorithm for Bound Constrained Global Optimization. In: Proceedings of the International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2009, June 30 , July 1-3 (2009)

    Google Scholar 

15. Fox, M.S., Sadeh-Koniecpol, N.: Why is scheduling so difficult? A csp perspective. In: Proceedings of the European Conference on Artificial Intelligence, pp. 754–767 (1990)

    Google Scholar 

16. Gao, S., Yang, J.Y.: Swarm intelligence algorithms and applications. China Waterpower Press, Beijing (2006)

    Google Scholar 

17. Gaspero, L.D., Schaerf, A.: Tabu search techniques for examination timetabling. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 104–117. Springer, Heidelberg (2001)

    Google Scholar 

18. Jiang, M., Mastorakis, N., Yuan, D., Lagunas, M.A.: Image segmentation with improved artificial fish swarm algorithm. In: Mastorakis, N., Mladenov, V., Kontargyri, V.T. (eds.) Proceedings of the European Computing Conference. Lecture Notes in Electrical Engineering, vol. 28, pp. 133–138. Springer, Heidelberg (2009) ISBN: 978-0-387-84818-1

    Chapter  Google Scholar 

19. Jiang, M., Wang, Y., Pfletschinger, S., Lagunas, M.A., Yuan, D.: Optimal multiuser detection with artificial fish swarm algorithm. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. CCIS, vol. 2, pp. 1084–1093. Springer, Heidelberg (2007)

    Google Scholar 

20. Lewis, R.: A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum 30(1), 167–190 (2008)

    Article  MathSciNet  MATH  Google Scholar 

21. Li, X.L., Shao, Z.J., Qian, J.X.: An optimizing method based on autonomous animate: fish swarm algorithm. System Engineering Theory and Practice 11, 32–38 (2002)

    Google Scholar 

22. Malim, M.R., Khader, A.T., Mustafa, A.: Artificial immune algorithms for university. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, pp. 234–245. Springer, Heidelberg (2007)

    Google Scholar 

23. McCollum, B.: A perspective on bridging the gap between theory and practice in university timetabling. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, pp. 3–23. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

24. Merlot, L.T.G., Boland, N., Hughes, B.D., Stuckey, P.J.: A Hybrid Algorithm for the Examination Timetabling Problem. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 207–231. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

25. Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)

    Article  MathSciNet  MATH  Google Scholar 

26. Petrovic, S., Burke, E.K.: Chapter 45: University timetabling. In: Leung, J. (ed.) Handbook of Scheduling: Algorithms Models and Performance Analysis. CRC Press, Boca Raton (2004)

    Google Scholar 

27. Qu, R., Burke, E.K., McCollum, B.: Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems. European Journal of Operational Research (EJOR) 198(2), 392–404 (2009)

    Article  MATH  Google Scholar 

28. Qu, R., Burke, E.K.: Hybridisations within a graph based hyper-heuristic framework for university timetabling problems. Journal of Operational Research Society (JORS) 60, 1273–1285 (2009)

    Article  MATH  Google Scholar 

29. Qu, R., Burke, E.K., McCollum, B., Merlot, L.T.G., Lee, S.Y.: A survey of search methodologies and automated system development for examination timetabling. Journal of scheduling, 55–89 (2009)

    Google Scholar 

30. Sadeh, N., Kaujnunn, M.: Micro-opportunistic scheduling: The micro-boss factory scheduler. In: Intelligent Scheduling, pp. 99–135. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

31. Schaerf, A.: A survey of automated timetabling. Artificial Intelligence Review 13(2), 87–127 (1999)

    Article  Google Scholar 

32. Wang, C.-R., Zhou, C.-L., Ma, J.-W.: An improved artificial fish swarm algorithm and its application in feed-forward neural networks. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, pp. 2890–2894 (2005)

    Google Scholar 

33. Wang, X., Gao, N., Cai, S., Huang, M.: An Artificial Fish Swarm Algorithm Based and ABC Supported QoS Unicast Routing Scheme in NGI. In: Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds.) ISPA Workshops 2006. LNCS, vol. 4331, pp. 205–214. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

34. Yang, Y., Petrovic, S.: A Novel Similarity Measure for Heuristic Selection in Examination Timetabling. In: Burke, E.K., Trick, M.A. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 247–269. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

Download references