Using Dynamic Search Mandatory Genetic Algorithm to Solve the University Course Timetabling Problem Considering Walking Distance

The University Course Timetabling Problem (UCTP) is a highly constrained real-world combinatorial optimization task. The purpose of this study is to improve the scientific nature of constraints and the solving performance of algorithms to supplement the previous studies. First of all, through crawling and analyzing the university post bar data, the characteristics of teachers’ and students’ preferences different from those of previous studies are obtained, which are taken as the main constraints, and the improvement of class efficiency is also taken into account to establish a mathematical model. Secondly, according to the characteristics of the problem, an improved genetic algorithm with dynamic search and mandatory directional iteration is designed to solve such problems, which can significantly reduce the number of optimization iterations and obtain better solutions. Then, this paper applies the real data to conduct a case study. The genetic algorithm and particle swarm optimization are compared with the improved genetic algorithm in this paper. The multi-dimensional analysis proves the superiority of the improved algorithm. The results of this paper can not only effectively solve the UCTP, but also provide model and algorithm support for scheduling problems considering the distance factor.