Research on teaching schedule management in universities using an optimized genetic algorithm

Abstract

Problem

Traditional course scheduling management in universities relies on manual experience, leading to subjective and inefficient scheduling.

Objectives

This paper aims to utilize corresponding scheduling algorithms to quickly, accurately, and reasonably arrange courses in universities.

Methods

This paper adopted a genetic algorithm (GA) to schedule courses in universities and used a simulated annealing algorithm to optimize it. Afterward, a comparison was made with the particle swarm optimization (PSO) algorithm and traditional GA through simulation experiments.

Key findings

When the population size was 150, the crossover probability was 0.7, and the mutation probability was 0.05, the performance of the improved GA was the best. The distribution of courses in the optimized curriculum schedule was more uniform after using the improved GA. After the termination of the algorithm, the average fitness value of the optimized GA was the highest, and the PSO algorithm was the lowest. In terms of calculation time, the PSO algorithm consumed the least time, and the traditional GA consumed the most time.