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Incorporating of constraint-based reasoning into particle swarm optimization for university timetabling problem

Source: OAI

ABSTRACT Timetabling problems are often difficult and time-consuming task. It involves a set of timeslots, classrooms, subjects, students and lecturers. The complexity problem is the constraints that exist within the resources. Thus, a technique that can handle constraints is needed to optimize the problem. Various approaches have been reported in the literature on solving university timetabling problem. This paper focuses on developing a hybrid algorithm consisting of a particle swarm optimization and constraint-based reasoning in solving university timetabling problem in generating a feasible and near-optimal solution. The proposed algorithm is tested using real data from the Faculty of Computer Science and Information System, Universiti Teknologi Malaysia. The result is compared against standard particle swarm optimization and hybrid particle swarm optimization-local search. It shows that the proposed method has outperformed others

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    ABSTRACT: Constraint satisfaction optimization problem is a kind of optimization problem involving cost minimization as well as complex constraints. Local search and constraint programming respectively have been used for solving such problems. In this paper, I propose a method to integrate local search and constraint programming to improve search performance. Basically, local search is used to solve the given problem. However, it is very difficult to find a feasible neighbor satisfying all the constraints when we use only local search. Therefore, I introduced constraint programming as a tool for neighbor generation. Through the experimental results using weighted N-Queens problems, I confirmed that the proposed method can significantly improve search performance.
    05/2010; 15(5). DOI:10.9708/jksci.2010.15.5.039

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