Abstract
The great deluge algorithm explores neighbouring solutions which are accepted if they are better than the best solution so far or if the detriment in quality is no larger than the current water level. In the original great deluge method, the water level decreases steadily in a linear fashion. In this paper,we conduct a computational study of a modified version of the great deluge algorithm in which the decay rate of the water level is non-linear. For this study, we apply the non-linear great deluge algorithm to difficult instances of the university course timetabling problem. The results presented here show that this algorithm performs very well compared to other methods proposed in the literature for this problem. More importantly, this paper aims to better understand the role of the non-linear decay rate on the behaviour of the non-linear great deluge approach.
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References
Aarts, E., Korts, J.: Simulated Annealing and Boltzman Machines. Wiley, Chichester (1998)
Abdullah, S., Burke, E.K., McCollum, B.: An Investigation of Variable Neighbourhood Search for University Course Timetabling. In: Proceedings of MISTA 2005: The 2nd Multidisciplinary Conference on Scheduling: Theory and Applications, pp. 413–427 (2005)
Abdullah, S., Burke, E.K., McCollum, B.: A Hybrid Evolutionary Approach to the University Course Timetabling Problem. In: Proceedings of CEC 2007: The 2007 IEEE Congress on Evolutionary Computation, pp. 1764–1768 (2007)
Abdullah, S., Burke, E.K., McCollum, B.: Using a Randomised Iterative Improvement Algorithm with Composite Neighborhood Structures for University Course Timetabling. In: Metaheuristics - Progress in Complex Systems Optimization, pp. 153–172. Springer, Heidelberg (2007)
Asmuni, H., Burke, E.K., Garibaldi, J.: Fuzzy Multiple Heuristic Ordering for Course Timetabling. In: Proceedings of the 5th United Kingdom Workshop on Computational Intelligence (UKCI 2005), pp. 302–309 (2005)
Burke, E.K., Bykov, Y., Newall, J., Petrovic, S.: A Time-predefined Approach to Course Timetabling. Yugoslav Journal of Operations Research (YUJOR) 13(2), 139–151 (2003)
Burke, E.K., Kendall, G., Soubeiga, E.: A Tabu-search Hyperheuristic for Timetabling and Rostering. Journal of Heuristics 9, 451–470 (2003)
Burke, E.K., Eckersley, A., McCollum, B., Petrovic, S., Qu, R.: Hybrid Variable Neighbourhood Approaches to University Exam Timetabling. Technical Report NOTTCS-TR-2006-2, University of Nottingham, School of Computer Science (2006)
Burke, E.K., McCollum, B., Meisels, A., Petrovic, S., Qu, R.: A Graph Based Hyper-heuristic for Educational Timetabling Problems. European Journal of Operational Research 176, 177–192 (2007)
Chiarandini, M., Birattari, M., Socha, K., Rossi-Doria, O.: An Effective Hybrid Algorithm for University Course Timetabling. Journal of Scheduling 9(5), 403–432 (2006)
Cooper, T., Kingston, H.: The Complexity of Timetable Construction Problems. In: Burke, E.K., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 283–295. Springer, Heidelberg (1996)
Dueck, G.: New Optimization Heuristic: The Great Deluge Algorithm and the Record-to-record Travel. Journal of Computational Physics 104, 86–92 (1993)
Even, S., Itai, A., Shamir, A.: On the Complexity of Timetabling and Multicommodity Flow Problems. SIAM Journal of Computation 5, 691–703 (1976)
Glover, F., Taillard, E., De Werra, D.: A User’s Guide to Tabu Search. Annals of Operations Research 41, 3–28 (1993)
Landa-Silva, D., Obit, J.-H.: Great Deluge with Nonlinear Decay Rate for Solving Course Timetabling Problems. In: Proceedings of the 2008 IEEE Conference on Intelligent Systems (IS 2008), pp. 8.11–8.18. IEEE Press, Los Alamitos (2008)
Rossi-Doria, O., Sampels, M., Birattari, M., Chiarandini, M., Dorigo, M., Gambardella, L., Knowles, J., Manfrin, M., Mastrolilli, M., Paechter, B., Paquete, L., Stuetzle, T.: A Comparion of the Performance of Different Metaheuristics on the Timetabling Problem. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 333–352. Springer, Heidelberg (2003)
Schaerf, A.: A Survey of Automated Timetabling. Artificial Intelligence Review 13(2), 87–127 (1999)
Socha, K., Knowles, J., Sampels, M.: A Max-min Ant System for the University Course Timetabling Problem. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 1–13. Springer, Heidelberg (2002)
Socha, K., Sampels, M., Manfrin, M.: Ant Algorithms for the University Course Timetabling Problem with Regard to the State-of-the-Art. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 334–345. Springer, Heidelberg (2003)
Wren, V.: Scheduling, Timetabling and Rostering A Specail Relationship? In: Burke, E.K., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 46–75. Springer, Heidelberg (1996)
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Obit, J.H., Landa-Silva, D. (2010). Computational Study of Non-linear Great Deluge for University Course Timetabling. In: Sgurev, V., Hadjiski, M., Kacprzyk, J. (eds) Intelligent Systems: From Theory to Practice. Studies in Computational Intelligence, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13428-9_14
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DOI: https://doi.org/10.1007/978-3-642-13428-9_14
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