利用免疫粒子群算法解决排课问题

Using immune particle swarm optimization to solve timetable problem

为解决排课当中的资源合理分配问题,寻求一种合理的解决方案,提出一种带免疫量子行为的粒子群智能优化算法。将粒子群中的粒子当作抗体,给粒子的生成加入免疫记忆机制,迭代开始后,使用抗体浓度指导粒子种群向更优方向移动。改进后的方法能避免粒子陷入局部最优和早熟收敛,用以解决这种多约束、多目标的组合排课问题。最后进行实验仿真,仿真结果表明了该新算法在解决实际问题中的有效性与优越性。

To resolve the problem of rational allocation of resources,and find a reasonable solution in timetable problem,an improved particle swarm algorithm is advanced.Take the particles of particle swarm as antibodies,and add the immune memory mechanism into it.After the iteration,use antibody concentration to guide particle populations to better move.The improved method can avoid the particles into a local optimum and premature convergence,which can solve this multi-constraint,multi-objective and combined timetable problem.Finally,simulation experiments prove that the new algorithm is effective and predominant in solving practical problems.