Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing
Abstract: Scheduling problems
at the university is a complex type of scheduling problems. The scheduling
process should be carried out at every turn of the semester's. The core of the
problem of scheduling courses at the university is that the number of
components that need to be considered in making the schedule, some of the
components was made up of students, lecturers, time and a room with due regard
to the limits and certain conditions so that no collision in the schedule such
as mashed room, mashed lecturer and others. To resolve a scheduling problem
most appropriate technique used is the technique of optimization. Optimization
techniques can give the best results desired. Metaheuristic algorithm is an
algorithm that has a lot of ways to solve the problems to the very limit the
optimal solution. In this paper, we use a genetic algorithm and ant colony
optimization algorithm is an algorithm metaheuristic to solve the problem of
course scheduling. The two algorithm will be tested and compared to get
performance is the best. The algorithm was tested using data schedule courses
of the university in Semarang. From the experimental results we conclude that
the genetic algorithm has better performance than the ant colony
optimization algorithm in solving the
case of course scheduling.
Keywords: Course scheduling,
Genetic algorithm, Ant colony optimization algorithm, Metaheuristic algorithm,
Performance
Author: Imam Ahmad Ashari,
Much Aziz Muslim, Alamsyah
Journal Code: jptinformatikagg160030
