Multi Population Hybrid Genetic Algorithms for University Course Timetabling Problem

Multi Population Hybrid Genetic Algorithms for University Course Timetabling Problem

Автор: Meysam Shahvali Kohshori, Dariush Zeynolabedini, Mehrnaz Shirani Liri, Leila Jadidi

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 6 Vol. 4, 2012 года.

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University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP) are proposed: FGARI, FGASA and FGATS. In proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainly and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP.

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University course timetabling problem(UCTP), genetic algorithm, fuzzy logic, local search, heurestic

Короткий адрес: https://sciup.org/15011694

IDR: 15011694

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