Comparison of genetic algorithms and Particle Swarm Optimization (PSO) algorithms in course scheduling

Abstract

Making lecture schedules is a complicated matter because it involves many parties and resources. In order to arrange the schedule optimally, a method is needed that can produce the best optimization value. In this paper, we will discuss the comparison of Genetic Algorithms and Particle Swarm Optimization to design lecture schedules. Once implemented, then comparative analysis of the results of the course scheduling process is carried out by comparing the fitness value and execution speed of the two algorithms. The results showed that in the 25th iteration the Genetic algorithm succeeded in compiling a lecture schedule with the best fitness value of 0.021 and 9.36 second execution time compared to the PSO algorithm which produced a fitness value of 0.099 and execution time of 61.95 seconds in the same iteration. This proves that the PSO fitness value outperforms the Genetic Algorithm, but the Genetic Algorithm execution time is faster than the PSO algorithm.