基于改进量子猫群算法的流水车间调度研究

猫群算法（Cat Swarm Optimization，CSO）是近年来提出的一种新型群体智能算法，针对猫群算法在求解大规模调度问题中出现的不足，如易早熟、搜索效率低下等，提出了一种改进的量子猫群算法。将猫群算法的跟踪模式和搜寻模式中猫群位置的更新，通过基于量子旋转门的量子位概率幅更新的方式来实现，并提出了随时间可变的猫群模式选择配比MR。在求解流水线调度问题的仿真实验结果中表明，改进量子猫群算法的性能远远优于基本猫群算法。

Research of flow-shop scheduling problem based on quantum cat swarm optimization

Cat Swarm Optimization （CSO） is a kind of swarm intelligence algorithm proposed in recent years, but there are a few of shortcomings when CSO solves large-scale scheduling problems, such as prematurity and low search efficiency. In order to improve this situation, the Quantum Cat Swarm Optimization （QCSO） is proposed. The algorithm uses revolving door to update cat swarm location, moreover, the variable MR is proposed. The simulation results of solving the problem of pipeline scheduling show that the performance of the improved algorithm is superior to the basic algorithm.