A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
A MAX-MIN Ant System for the University Course Timetabling Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Advances in evolutionary computing
An effective hybrid algorithm for university course timetabling
Journal of Scheduling
Context-Independent Scatter and Tabu Search for Permutation Problems
INFORMS Journal on Computing
An improved ant colony optimisation heuristic for graph colouring
Discrete Applied Mathematics
Generating University Course Timetable Using Genetic Algorithms and Local Search
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
Scatter Search and Path Relinking
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
The influence of run-time limits on choosing ant system parameters
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Ant algorithms for the university course timetabling problem with regard to the state-of-the-art
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Fish swarm intelligent algorithm for the course timetabling problem
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Scatter search technique for exam timetabling
Applied Intelligence
Computers and Operations Research
A hybrid metaheuristic approach to the university course timetabling problem
Journal of Heuristics
Dual sequence simulated annealing with round-robin approach for university course timetabling
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
The university course timetabling problem with a three-phase approach
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
New computational results for the nurse scheduling problem: a scatter search algorithm
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Genetic Algorithms With Guided and Local Search Strategies for University Course Timetabling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This study presents an investigation of enhancing the capability of the Scatter Search (SS) metaheuristic in guiding the search effectively toward elite solutions. Generally, SS generates a population of random initial solutions and systematically selects a set of diverse and elite solutions as a reference set for guiding the search. The work focuses on three strategies that may have an impact on the performance of SS. These are: explicit solutions combination, dynamic memory update, and systematic search re-initialization. First, the original SS is applied. Second, we propose two versions of the SS (V1 and V2) with different strategies. In contrast to the original SS, SSV1 and SSV2 use the quality and diversity of solutions to create and update the memory, to perform solutions combinations, and to update the search. The differences between SSV1 and SSV2 is that SSV1 employs the hill climbing routine twice whilst SSV2 employs hill climbing and iterated local search method. In addition, SSV1 combines all pairs (of quality and diverse solutions) from the RefSet whilst SSV2 combines only one pair. Both SSV1 and SSV2 update the RefSet dynamically rather than static (as in the original SS), where, whenever a better quality or more diverse solution is found, the worst solution in RefSet is replaced by the new solution. SSV1 and SSV2 employ diversification generation method twice to re-initialize the search. The performance of the SS is tested on three benchmark post-enrolment course timetabling problems. The results had shown that SSV2 performs better than the original SS and SSV1 (in terms of solution's quality and computational time). It clearly demonstrates the effectiveness of using dynamic memory update, systematic search re-initialization, and combining only one pair of elite solutions. Apart from that, SSV1 and SSV2 can produce good quality solutions (comparable with other approaches), and outperforms some approaches reported in the literature (on some instances with regards to the tested datasets). Moreover, the study shows that by combining (simple crossover) only one pair of elite solutions in each RefSet update, and updating the memory dynamically, the computational time is reduced.