Abstract: According to the characteristics of parallel flow-shop scheduling problem，a new quantum particle swarm optimizer，called the cooperative evolutionary QPSO with multi-populations（MC-PSO），is presented based on the analysis of the standard QPSO.The whole quantum particle swarm group is divided into several sub-groups.Every subgroup evolves independently and updates sharing information periodically.This paper uses a practical analysis to confirm the performance of the method.The results show that MC-QPSO is effective in solving the problem.The results of simulation indicate that MC-QPSO performs better than the genetic algorithm.

Key words: Quantum Particle Swarm Optimization（QPSO）, parallel flow-shop scheduling problem, cooperative evolutionary

摘要： 针对并行流水车间调度问题的特点，提出了一种基于多种群协同进化的改进量子粒子群算法（MC-QPSO）进行求解。首先将整个量子粒子种群分解为多个子种群，然后各个子种群独立地演化，并通过周期性共享搜索信息，以获得对自身信息的更新。最后，通过具体仿真实例进行了求解验证，结果表明，在求解并行流水车间调度问题时，基于多种群协同的量子粒子群算法，在收敛速度、寻优性能等方面，都要优于遗传算法。

关键词: 量子粒子群算法, 并行流水车间调度, 协同进化