Abstract
The university course timetabling is an NP-hard (non-deterministic polynomial-time hard) optimization problem to create a course timetable without conflict. It must assign a set of subject classes to a fixed number of timeslots with physical resources, including rooms and teachers. Avoiding hard c
Highlights
The timetable is an essential and crucial document in any educational institute to manage classes of various subjects by allocating resources at the maximum level with minimum conflicts among them
The university course timetabling problem is considered as an NP-hard optimization problem that does not solve in polynomial time [2]
A famous local search metaheuristic technique, starts with an initial solution and tries to spot out an optimal solution by exploring the candidate solutions iteratively [5]. It is effective in removing hard constraints in less time and tries to exploit all the candidates to get the optimal solution in a vertical direction
Summary
These algorithms depend upon the defined heuristics and always return the same result and performance, as they find out every solution with those defined heuristics [2] Optimization approaches such as graph theory[6], integer programming/linear programming [7], and constraint satisfaction programming [8], solve the university course timetabling problem by applying various heuristics. The proposed algorithm in this paper, the multi-objective fuzzy-based adaptive memetic algorithm (MO-FAMA), is a population-based genetic algorithm with local searches that is an effective global optimization solution compared with simple optimization It tries to find an optimal solution by using the fuzzy logic rule base to create adaptive operations with different hyper-heuristics.
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