Heuristic algorithms can solve the problem of scheduling well, making the study of scheduling problems improve. Heuristic algorithms include graph theory, greedy algorithms, simulated annealing algorithms, backtracking algorithms, etc. With the development of science and technology, it has become possible to apply heuristic algorithms to the scheduling system. Through the analysis of the problem of scheduling, a mathematical model based on the scheduling is established to discuss the solution and the existence of the solution, but the desired result has not been achieved. The essential problem of scheduling is a timetable problem, and the timetable problem is an important branch of operations research. The complexity of arranging classes increases exponentially with the increase in the school’s teaching scale, which is not in direct proportion to the school’s teaching scale. The amount of population size increases as the crossover rate value becomes larger, but it also leads to an increase in the chance that the more outstanding individuals in the population are destroyed the size of the variation rate value directly affects the number of newborn populations in the population, and the larger the value of the variation probability, the larger the value of the size of the newborn population, and the greater the possibility of the algorithm jumping out of local convergence to obtain the optimal solution. The genetic crossover and variation operators themselves are adaptive, and the individuals in the population evolve iteratively through the genetic operations, with changes in their crossover and variation rate values determined by the degree of population dispersion or concentration, as well as the size of the population. In the genetic algorithm, two genetic operations, crossover and variation, directly affect the overall effectiveness of the genetic algorithm.
The goal of the scheduling problem is to first aggregate all the courses offered and then to finally rationalize each teaching task, i.e., course, according to the current semester’s teaching schedule, as well as school resources and teachers, to be able to optimize school resources, teachers to teach rationally, and students to learn efficiently. The use of computers for rational planning and scheduling of different courses enables quickly meeting different constraints and thus obtaining feasible results. With the rapid development of science and technology, computers have become the backbone of various work applications with their excellent performance such as strong processing power and fast computing speed, and they have also become the main force in intelligent education and teaching work, especially in the field of scheduling lessons which has been widely used in recent years. 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. 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. Research shows that the improved genetic algorithm has improved average fitness value and time compared with traditional genetic algorithm.
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. Finally, under the framework of optimal individual retention, the selection operator, crossover operator, and variation operator are improved. Secondly, a problem-specific local search operator is designed to accelerate the convergence speed of the algorithm. 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. In this paper, an improved genetic algorithm is designed to solve the above multiobjective optimization problem for the scheduling problem of college English courses.