自适应粒子群算法求解排课问题

针对传统排课效率低、漏排课、冲突率高等问题，利用自适应粒子群算法（SAPSO）进行排课仿真研究.首先，将粒子群算法中的固定惯性因子改进为随着迭代次数变化而不同的自适应权重，以加快寻优速度；然后，为了防止种群陷入局部最优，定义了种群相似度函数；最后，在种群中加入最差个体位置信息以增加种群混乱度，从而提高算法的全局寻优能力.仿真结果表明，SAPSO在收敛速度较快的情况下，寻优精度优于蒙特洛卡算法和改进遗传算法.

Course Arrangement System Based on Self-Adaptive Particle Swarm Optimization

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.