Aiming at the inefficiency, scheduling missing and conflict of traditional course arrangement system, an adaptive particle swarm optimization (APSO) course scheduling system is proposed. Firstly, the fixed inertia weight in particle swarm optimization is improved as an adaptive weight which changes with the number of iterations, so as to speed up the search speed. In order to prevent the population from falling into local optimum, the population similarity function is defined, and the worst individual location information is added to the population to increase the population chaos, thus increasing the global search ability of the population. The simulation results show that the optimization accuracy of the proposed algorithm is better than the current MCGA algorithm and IGA algorithm under the condition of fast convergence speed.

%U https://zkxb.jsu.edu.cn/EN/10.13438/j.cnki.jdzk.2019.06.004