New methods to color the vertices of a graph
Communications of the ACM
Computers and Operations Research
Novel Local-Search-Based Approaches to University Examination Timetabling
INFORMS Journal on Computing
INFORMS Journal on Computing
Ant algorithms for the exam timetabling problem
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
A novel similarity measure for heuristic selection in examination timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
The variants of the harmony search algorithm: an overview
Artificial Intelligence Review
Nurse rostering using modified harmony search algorithm
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
Data clustering using harmony search algorithm
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
Office-space-allocation problem using harmony search algorithm
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Hi-index | 0.00 |
In this paper, three selection mechanisms in memory consideration operator for Examination Timetabling Problem with Harmony Search Algorithm (HSA) are investigated: Random memory consideration which uses a random selection mechanism, global-best memory consideration which uses a selection mechanism inspired by a global best concept of Particle Swarm Optimisation (PSO), and Roulette-Wheel memory consideration which uses the survival for the fittest principle. The HSA with each proposed memory consideration operator is evaluated against a de facto dataset defined by Carter et al., (1996). The results suggest that the HSA with Roulette-Wheel memory consideration can produce good quality solutions. The Results are also compared with those obtained by 6 comparative methods that used Carter dataset demonstrating that the proposed method is able to obtain viable results with some best solutions for two testing datasets.