A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Copyright @ Springer Science + Business Media. All rights reserved.The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02

A guided search non-dominated sorting genetic algorithm for the multi-objective university course timetabling problem

Copyright @ Springer-Verlag Berlin Heidelberg 2011.The university course timetabling problem is a typical combinatorial optimization problem. This paper tackles the multi-objective university course timetabling problem (MOUCTP) and proposes a guided search non-dominated sorting genetic algorithm to solve the MOUCTP. The proposed algorithm integrates a guided search technique, which uses a memory to store useful information extracted from previous good solutions to guide the generation of new solutions, and two local search schemes to enhance its performance for the MOUCTP. The experimental results based on a set of test problems show that the proposed algorithm is efficient for solving the MOUCTP

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

Гибридный алгоритм решения задачи удовлетворения ограничений

Представлен гибридный алгоритм improved Guided Local and Systematic Search для решения распределенной задачи удовлетворения ограничений. Алгоритм объединяет компоненты локального и конструктивного поиска. Доказаны полнота и корректность алгоритма. Приведены результаты его экспериментальной оценки на модельной задаче о ферзях и проведено сравнение его производительности с производительностью алгоритмов Dis-GLS и iGL.The improved Guided Local and Systematic Search hybrid algorithm is presented for solving the Distributed Constraint Satisfaction Problem, which combines two local and one systematic search methods. The completeness and correctness of the algorithm are proved. The results of our experiments with queens' problem, and a comparison of productivity for our hybrid and two other algorithms Dis-GLS and iGL are given.Представлено гібридний алгоритм improved Guided Local and Systematic Search розв’язання розподіленої задачі задоволення обмежень, який поєднує компоненти локального та конструктивного пошуку. Доведено повноту і коректність алгоритму. Описано результати його експериментальної оцінки на модельній задачі про ферзі. Проведено порівняння його продуктивності з продуктивністю алгоритмів класу Dis-GLS та iGL