Citation
Directing selection within an extended great deluge optimization algorithm. In proceedings of the 6th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2013), 27 - 30 Aug 2013, Ghent, Belgium, pages 499-508, 2013.
Paper
Abstract
The original Extended Great Deluge algorithm uses a stochastic method for selecting which examination to use in the neighborhood search process. This paper a variation on the Extended Great Deluge optimization algorithm, by directing examination selection within an optimization heuristic, utilizing a weighted list gathered during adaptive construction to influence examination selection. Two approaches to utilizing the weighted list of examinations are studied; utilizing the list at the end of the construction phase and evolving the list over the duration of the improvement phase. The approach described in this paper is able to produce feasible solutions for all of the publicly available datasets from the 2007 International Timetabling Competition, within a time limit specified by the competition organizers. In addition, the results generated with the new selection technique compare favorably with those of the original Extended Great Deluge approach. The results are also competitive with those of the competition winner and with research conducted post competition which observe the time limits specified within the competition rules.
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Bibtex
@INPROCEEDINGS{2013-499-508-P, author = { R. Hamilton-Bryce and P. McMullan and B. McCollum },
title = {Directing selection within an extended great deluge optimization algorithm },
booktitle = {In proceedings of the 6th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2013), 27 - 30 Aug 2013, Ghent, Belgium},
year = {2013},
editor = {G. Kendall and B. McCollum and G. {Venden Berghe}},
pages = {499--508},
note = {Paper},
abstract = {The original Extended Great Deluge algorithm uses a stochastic method for selecting which examination to use in the neighborhood search process. This paper a variation on the Extended Great Deluge optimization algorithm, by directing examination selection within an optimization heuristic, utilizing a weighted list gathered during adaptive construction to influence examination selection. Two approaches to utilizing the weighted list of examinations are studied; utilizing the list at the end of the construction phase and evolving the list over the duration of the improvement phase. The approach described in this paper is able to produce feasible solutions for all of the publicly available datasets from the 2007 International Timetabling Competition, within a time limit specified by the competition organizers. In addition, the results generated with the new selection technique compare favorably with those of the original Extended Great Deluge approach. The results are also competitive with those of the competition winner and with research conducted post competition which observe the time limits specified within the competition rules. },
owner = {Graham},
timestamp = {2017.01.16},
webpdf = {2013-499-508-P.pdf} }