A Hybrid Swarm-Based Approach to University Timetabling


This paper is concerned with the application of an automatic hybrid approach in addressing the university timetabling problem. The approach described relies on the character-impressed artificial bee colony (ABC) algorithm. An ABC algorithm is a biologically-impressed optimization approach, that has been widely implemented in solving a range of optimization problems in recent years such as job search scheduling and machine timetabling problems. Although the approach has proven to be robust across a vary of problems, it's acknowledged within the literature that there currently exist a variety of inefficiencies regarding the exploration and exploitation talents. These inefficiencies can typically cause a slow convergence speed inside the search process. Hence, this paper introduces a variant of the algorithm that utilizes a global best model impressed from particle swarm optimization to boost the world exploration ability while hybridizing with the nice deluge (GD) algorithm in order to improve the native exploitation ability. Using this approach, an efficient balance between exploration and exploitation is attained. As well, a ancient local search approach is incorporated at intervals the GD algorithm with the aim of any enhancing the performance of the hybrid methodology. To guage the performance of the proposed approach, two various university timetabling datasets are investigated, i.e., Carter’s examination timetabling and Socha course timetabling datasets. It ought to be noted that each issues have differing complexity and different solution landscapes. Experimental results demonstrate that the proposed methodology is capable of producing top quality solutions across each these benchmark issues, showing a smart degree of generality within the approach. Moreover, the proposed technique produces best results on some instances as compared with different approaches presented within the literature.

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