Timetable planning using the constraint-based reasoning
Computers and Operations Research
A Survey of Automated Timetabling
Artificial Intelligence Review
Parallel Hybrid Adventures with Simulated Annealing and Genetic Algorithms
ISPAN '02 Proceedings of the 2002 International Symposium on Parallel Architectures, Algorithms and Networks
Breeding swarms: a GA/PSO hybrid
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Constructing university timetable using constraint satisfaction programming approach
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Timetable Scheduling Using Particle Swarm Optimization
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Evaluating particle swarm intelligence techniques for solving university examination timetabling problems
A Study on PSO-Based University Course Timetabling Problem
ICACC '09 Proceedings of the 2009 International Conference on Advanced Computer Control
A Combination of PSO and Local Search in University Course Timetabling Problem
ICCET '09 Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 02
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid particle swarm optimization based algorithm for high school timetabling problems
Applied Soft Computing
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University Course Timetabling (UCT) is a complex problem and cannot be dealt with using only a few general principles. The complicated relationships between time periods, subjects and classrooms make it difficult to obtain feasible solution. Thus, finding feasible solution for UCT is a continually challenging problem. This paper presents a hybrid particle swarm optimization algorithm to solve University Course Timetabling Problem (UCTP). The proposed approach (hybrid particle swarm optimization with constraint-based reasoning) uses particle swarm optimization to find the position of room and timeslot using suitable objective function and the constraints-based reasoning has been used to search for the best preference value based on the student capacity for each lesson in a reasonable computing time. The proposed algorithm has been validated with other hybrid algorithms (hybrid particle swarm optimization with local search and hybrid genetic algorithm with constraint-based reasoning) using a real world data from Faculty of Science at Ibb University -- Yemen and results show that the proposed algorithm can provide more promising solution.