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This article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than exiting ABC techniques and competes well with other techniques from the literature.

The selection process is the most attractive operator in the optimization algorithms. It normally mimics the natural selection of survival of the fittest principle. When the selection is too greedy, the selection pressure will be high and therefore the search becomes biased toward exploitation. In contrast, when the selection has a tendency to be random, the selection pressure will be low and thus the exploration is observed more. The selection process in Grey Wolf Optimizer (GWO) tends to be too greedy since the search is driven by the three best solutions. In this paper, different selection methods extracted from other evolutionary algorithms (EAs) are investigated for GWO. Along with the original selection method of GWO called greedy-based selection method, five others selection methods which are proportional, tournament, universal sampling, linear rank, and random selection methods are investigated. Accordingly, six versions of GWO are proposed which are Greedy-based GWO (GGWO), Proportional-based GWO (PGWO), Tournament-based GWO (TGWO), Universal sampling-based GWO (UGWO), Linear rank-based GWO (LGWO), Random-based GWO (RGWO). The six versions are evaluated using 23 test functions circulated in the literature with different characteristics and complexity. The sensitivity analysis of the control parameters of some new versions are discussed. Interestingly, TGWO achieved the best results for almost all benchmark functions. This proves that the selection methods have a high impact on the performance of GWO. [ABSTRACT FROM AUTHOR]