Simulated Annealing Algorithm for Solving the Optimal Configuration of Graduate Curriculum System and Competency Development Objectives

Abstract

The arrangement and scheduling of courses is one of the basic tasks of academic management in higher education institutions, and the quality of graduate school class schedules is related to whether the teaching task can be executed normally. The article models the college scheduling problem and the grouping problem, and proposes a college solution algorithm based on the combination of genetic algorithm and simulated annealing-branching limit method. The algorithm promotes the continuous genetic evolution of the population in a better direction, and at the same time uses the idea of combining the simulated annealing algorithm with the branching limit method to carry out individual optimization, so as to jointly solve the problem of the postgraduate curriculum system and ability cultivation objectives. Finally, the experimental results show that from the average value of the results of 8 runs of the three algorithms, the average number of iterations of this paper's algorithm is 34, the running time is 7.47 seconds, and the optimal solution value is 0.8088. It has a certain degree of superiority compared with the basic simulated annealing algorithm and the original adaptive simulated annealing algorithm, and verifies the effectiveness of this paper's algorithm in solving the postgraduate curriculum system and ability cultivation objectives.