CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

UCTP based on Hybrid PSO with Tabu Search Algorithm using Mosul university dataset

by Aseel Ismael Ali, Ruba Talal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 9
Year of Publication: 2014
Authors: Aseel Ismael Ali, Ruba Talal
10.5120/15911-5113

Aseel Ismael Ali, Ruba Talal . UCTP based on Hybrid PSO with Tabu Search Algorithm using Mosul university dataset. International Journal of Computer Applications. 91, 9 ( April 2014), 25-33. DOI=10.5120/15911-5113

@article{ 10.5120/15911-5113,
author = { Aseel Ismael Ali, Ruba Talal },
title = { UCTP based on Hybrid PSO with Tabu Search Algorithm using Mosul university dataset },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 9 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number9/15911-5113/ },
doi = { 10.5120/15911-5113 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:19.642215+05:30
%A Aseel Ismael Ali
%A Ruba Talal
%T UCTP based on Hybrid PSO with Tabu Search Algorithm using Mosul university dataset
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 9
%P 25-33
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the most well-known constraint problem is a university course timetabling problem (UCTP), which become more difficult and more complex specially when we have a course with more than one teacher or a teacher with more than one course. This constraint problem take a lot of times to construct. In this paper we solve hard and soft constraint and take less time than usual by used one of artificial techniques, branch of swarm intelligent (SI), called particle swarm optimization (PSO). After a lot of researches we find that UTP can be solved with less time and more efficient based on PSO than other artificial intelligent (AI), or SI. In this paper, some improvements has been added to the algorithm to fit with the existing parameters in a dataset Mosul University College of Computer Science and Mathematics.

References
  1. Safaai, D. , Sigeru, O. , Hiroshi, O. , Puteh, S. : Incorporating Constraint Propagation in Genetic Algorithm for University Timetable Planning. Engineering Applications of Artificial Intelligence 12, pp. 231-253. (1999)
  2. Ho Sheau Fen @ Irene, Deris, Safaai, MohdHashim, SitiZaiton:Solving University Course Timetable Problem Using Hybrid Particle Swarm Optimization. Faculty of Comp. Sc. & Info. UniversitiTeknologiMalysia. 81310. Johor, Malaysia. (2013)
  3. Elizabeth Montero, Mar´?a-Cristina Riff, LeopoldoAltamirano:A PSO algorithm to solve a Real Course+ExamTimetabling Problem. Universit´e de Nice2000 route des Lucioles, 06903 Sophia AntipolisC´edex, France. (2011)
  4. Burke EK, McCollum B, Meisels A, Petrovic S, Qu R : Agraph-based hyper heuristic for educational timetabling problems. Eur J Oper Res 176:177–192 . (2007)
  5. D. Datta, K. Deb, and C. M. Fonseca, "Solving class timetabling problem of IIT kanpur using multi-objective evolutionary algorithm", (2006).
  6. P. Pongcharoen, W. Promtetn, P. Yenradee, and C. Hicks, "Stochastic optimization timetabling tool for university course scheduling", (2007).
  7. Mohammed Azmi Al-Betar , AhamadTajudinKhader:A harmony search algorithm for university course timetabling. Springer Science. (2012)
  8. Jensen , B. J. &Gutin , Gregory : Digraphs Theory ,Algorithms and Applications . (2008)
  9. J. Digalakis , K. Margaritis: Parallel evolutionary algorithms on mpi. University of Macedonia ,Department of Applied Informatics. (2003)
  10. Ruey-Maw Chen,Hsiao-Fang Shih:Solving University Course Timetabling Problems Using Constriction Particle Swarm Optimization with Local Search. Department of Computer Science and Information Engineering, National Chinyi University of Technology, Taichung, Taiwan. (2013).
  11. S. Kitayama, K. Yamazaki, M. Arakawa " adaptive range particle swarm optimization". Springer Science + Business Media, Journal: Optimization and Engineering ISSN: 13894420 Year: 2009 Volume: 10 Issue: 4 pages:575-597 DOI:10. 1007/s11081-009-9081-7.
  12. D. Bratton, J. Kennedy "defining a standard for particle swarm optimization", IEEE, Journal: 2007 IEEE Swarm Intelligence Symposium ISBN: 1424407087 Year: 2007 Pages: 120-127 Provider: IEEE Publisher: IEEE DOI: 10. 1109/SIS. 2007. 368035
  13. D. P. Rini, S. M. Shamsuddin, S. S. yuhaniz "Particle Swarm Optimization: Technique, System and Challenges" International Journal of Computer Applications (0975 – 8887) Volume 14– No. 1, January 2011.
  14. D. Souravilas, K. parsopoulos "Particle Swarm Optimization with Budget Allocation through Neighborhood Ranking", GECCO'13, July 6–10, 2013, Amsterdam, The Netherlands. 2013 ACM 978-1-4503-1963-8/13/07.
  15. Shiau, D. F. , "A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences " Journal: Expert Systems With Applications ISSN: 09574174 Year: 2011 Volume: 38 Issue: 1 Pages: 235-248.
  16. Engelbrecht A. P. , (2007):"Computational Intelligence An Introduction", Second Edition, John Wiley & Sons Ltd, West Sussex, England.
  17. F. Glover and C. McMillan . The general employee scheduling prob-lem: an integration of MS and AI. Computers and Operations Re-search,1986.
  18. F. Glover, M. Laguna, Tabu Search, Univ. Boulder, 1997.
  19. A. hertz, E. Taillard, D. De werra, A Tutorial on Tabu Search, OR Spektrum 11, 1989, 131-141.
  20. Z. Zainuddin, O. Pauline. Function Approximation Using Artificial Neural Network. WSEAS transaction on Mathematics, issue 6, Vol 7, June 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Timetabling hard constraints PSO NP-complete.

Powered by PhDFocusTM