针对混合流水车间调度问题，提出了分阶段加工优先级的调度原则，并将迭代局部搜索算法应用于求解此问题。
Multistage processing priority was developed for the hybrid flow shop problem and an iterated local search algorithm was used for solving this kind of problem.
在每次迭代时，算法可根据粒子的适应度变化动态改变惯性权重，从而使算法具有动态自适应性全局搜索与局部搜索能力。
According to the changes of the fitness, updated the inertia weight of each particle after each iteration to achieved a self-adaptive adjustment of global search ability and local search capabilities.
理论分析与仿真结果均表明，该算法能加快种群迭代速度，提高粒子搜索精度，防止粒子陷入局部最优。
Theoretical analysis and simulation results prove that the algorithm can increase iteration speed, enhance search accuracy, prevent the situation that particles fall into local best.
但是，这两种方法都存在局部搜索能力差的问题，在算法运行的中后期会出现大量的冗余迭代。鉴于此，提出一种信息素指导下的自适应变异方法求解专家分配问题。
Though it has been proven they are effective ways for EAP, they have disadvantages of massive redundancy iteration in later period and inferior local search ability.
但是，这两种方法都存在局部搜索能力差的问题，在算法运行的中后期会出现大量的冗余迭代。鉴于此，提出一种信息素指导下的自适应变异方法求解专家分配问题。
Though it has been proven they are effective ways for EAP, they have disadvantages of massive redundancy iteration in later period and inferior local search ability.
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