Prey-predator algorithm for discrete problems: a case for examinationtimetabling problem

Abstract

The prey-predator algorithm is a metaheuristic algorithm inspired by the interaction between a predator and its prey. Initial solutions are put into three categories: the better performing solution as the best prey, the worst performing solution as a predator, and the rest as ordinary prey. The best prey totally focuses on exploiting its neighborhood while the predator explores the search space searching for a promising region in the search space. The ordinary prey will be affected by these two extreme search behaviors of exploration and exploitation. The algorithm has been tested and found to be effective in solving different problems arising from different disciplines including engineering, tourism, and management. Originally, the algorithm was designed to deal with continuous problems. However, many problems arising from real aspects are not continuous. Hence, in this paper the prey-predator algorithm will be extended to suit discrete problems. Examination timetabling is used to test the approach. The simulation results with appropriate statistical analysis show that the approach is as good as the cumulative best performance of results recorded in the literature for the selected benchmark problems.

An Evolutionary Algorithm for Solving Academic Courses Timetable Scheduling Problem

“…Many modern works were produced utilized different optimization algorithms and heuristics techniques for solving timetabling schedule problems like particle swarm optimization and local search algorithm 21 , Prey-predator algorithm 22 , adaptive evolutionary memetic algorithm 23 , cellular memetic algorithm 24 and constructive ordering heuristics 25 . All works proved that the scheduling timetable for education purposes still a complex problem and needs more and more studies.…”

A spreadsheet-based decision support system for examination timetabling

“…Later Qu et al [13] used IP models to solve the hard subproblems of the ETP and introduced new cutting planes to have better solutions. In addition to these studies, Tilahun [14] created a heuristic approach to solve ETPs. He used a discrete version of the prey-predator algorithm and set up a simulation for tests.…”

“…In contrast to constraint (12), some examinations should not be held simultaneously. This is ensured with constraint ( 13) and (14). Constraint ( 15) is used to avoid conflicts with service courses for which examination periods are predetermined.…”

Multistage Newton’s Approach for Training Radial Basis Function Neural Networks

“…Metaheuristic algorithms such as firefly [10] algorithm and prey predator [11,12] algorithms are used to find optimal hyperparameters for RBF networks. These networks involve using open form solutions for hyperparameter tuning or grid search for continuous and non continuous optimization problems using the search space [10,11,13].…”