Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

Imam Ahmad Ashari,Much Aziz Muslim,Alamsyah Alamsyah

doi:10.15294/sji.v3i2.7911

Imam Ahmad Ashari, Much Aziz Muslim + Show 1 more

Open Access

https://doi.org/10.15294/sji.v3i2.7911

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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.

Highlights

Scheduling problems can be classified into several types, such as scheduling academic level Higher Education, Primary and Secondary Schools scheduling, exam scheduling, transport scheduling, scheduling the sale or delivery of goods and others [1].Scheduling problems at the university is a complex type of scheduling problems
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 space with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others [3]
Steps undertaken to optimize the scheduling of subjects with a genetic algorithm is as follows: 1) Initialization Parameter The parameters used for accomplishing the initial scheduling of subjects with genetic algorithms are: Length population, amount and probability generation crossover

Summary

Introduction

Scheduling problems can be classified into several types, such as scheduling academic level Higher Education, Primary and Secondary Schools scheduling, exam scheduling, transport scheduling, scheduling the sale or delivery of goods and others [1].Scheduling problems at the university is a complex type of scheduling problems. In a scheduling problem at the university each restriction should not be violated [2]. 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 space with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others [3]. Metaheuristic algorithm is an algorithm that is suitable to solve the problems of scheduling courses. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the boundaries of the optimal solution [4, 5]. Some examples algorithm metaheuristic widely used are: Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolutionary Programming (EP), Particle Swarm Optimization (PSO), Differential Evolution (DE), Tabu Search (TS), Biogeography based Optimization (BBO), Simulated Annealing (SA), etc. [6, 7, 8, 9, 10, 11, 12, 13]

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