Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses - Xu, Jing (Complexity (New York, N.Y.), 2021)
In this paper, an improved genetic algorithm is designed to solve the above multiobjective optimization problem for the scheduling problem of college English courses. Firstly, a variable-length decimal coding scheme satisfying the same course that can be scheduled at different times, different classrooms, and different teaching weeks per week is proposed, which fully considers the flexibility of classrooms and time arrangements of the course and makes the scheduling problem more reasonable. Secondly, a problem-specific local search operator is designed to accelerate the convergence speed of the algorithm. Finally, under the framework of optimal individual retention, the selection operator, crossover operator, and variation operator are improved. It is experimentally demonstrated that the designed algorithm not only has a faster convergence speed but also improves the diversity of individuals to a certain extent to enhance the search space and jump out of the local optimum. Research shows that the improved genetic algorithm has improved average fitness value and time compared with traditional genetic algorithm. At the same time, the use of the largest fuzzy pattern algorithm effectively solves the conflict problem of college English lesson scheduling, thereby improving the solution of college English lesson scheduling. Through the research of this article, the management system of college English course scheduling has been made more intelligent, and the rational allocation of teaching resources and the completion of education and teaching plans have been improved.