Publicação
A hybrid shuffled frog-leaping algorithm for the university examination timetabling problem
| Resumo: | The problem of examination timetabling is studied in this work. We propose a hybrid solution heuristic based on the Shuffled Frog-Leaping Algorithm (SFLA) for minimising the conflicts in the students's exams. The hybrid algorithm, named Hybrid SFLA (HSFLA), improves a population of frogs (solutions) by iteratively optimising each memeplex, and then shuffling the memeplexes in order to distribute the best performing frogs by the memeplexes. In each iteration the frogs are improved based on three operators: crossover and mutation operators, and a local search operator based on the Simulated Annealing metaheuristic. For the mutation and local search, we use two well known neighbourhood structures. The performance of the proposed method is evaluated on the 13 instances of the Toronto datasets from the literature. Computational results show that the HSFLA is competitive with state-of-the-art methods, obtaining the best results on average in seven of the 13 instances. |
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| Autores principais: | Leite, Nuno |
| Outros Autores: | Melicio, Fernando; Rosa, Agostinho C. |
| Assunto: | Examination timetabling Memetic algorithm Shuffled Frog-Leaping Algorithm Simulated annealing Toronto benchmarks |
| Ano: | 2016 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico de Lisboa |
| Resumo: | The problem of examination timetabling is studied in this work. We propose a hybrid solution heuristic based on the Shuffled Frog-Leaping Algorithm (SFLA) for minimising the conflicts in the students's exams. The hybrid algorithm, named Hybrid SFLA (HSFLA), improves a population of frogs (solutions) by iteratively optimising each memeplex, and then shuffling the memeplexes in order to distribute the best performing frogs by the memeplexes. In each iteration the frogs are improved based on three operators: crossover and mutation operators, and a local search operator based on the Simulated Annealing metaheuristic. For the mutation and local search, we use two well known neighbourhood structures. The performance of the proposed method is evaluated on the 13 instances of the Toronto datasets from the literature. Computational results show that the HSFLA is competitive with state-of-the-art methods, obtaining the best results on average in seven of the 13 instances. |
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