Pareto熵鸡群算法求解多目标柔性作业车间调度问题

发布时间：2018-05-27 08:43

本文选题：多目标柔性作业车间调度 + Pareto熵　；参考：《小型微型计算机系统》2017年12期

【摘要】：针对多目标柔性作业车间调度问题,提出基于Pareto熵的鸡群算法.首先,建立了多目标柔性作业车间调度模型,优化目标为最大完工时间、最大机器负荷和所有机器总负荷.其次,将Pareto熵的概念引入鸡群算法,通过计算Pareto前端的熵值和差熵值判断目前种群的进化状态,动态调节惯性权重,使得调节过程具有针对性和目的性,同时为了避免算法陷入局部最优,加入基于Pareto熵的精英学习策略作为局部扰动策略,精英学习率步长可根据Pareto差熵和进化状态动态调节,从而形成一个闭环调节的进化过程.最后,对多目标柔性作业车间调度的经典算例进行求解,并与相关算法对比,仿真实验证明所提算法在收敛精度和机器分配均匀度方面具有明显优势.
[Abstract]:Aiming at the multi-objective flexible job shop scheduling problem, a chicken swarm algorithm based on Pareto entropy is proposed. Firstly, a multi-objective flexible job shop scheduling model is established. The optimal objectives are maximum completion time, maximum machine load and total machine load. Secondly, the concept of Pareto entropy is introduced into the chicken population algorithm. By calculating the entropy and difference entropy of the Pareto front end, the evolutionary state of the current population is judged, and the inertia weight is dynamically adjusted, which makes the adjustment process have pertinence and purpose. In order to avoid the algorithm falling into local optimum, the elite learning strategy based on Pareto entropy is added as the local disturbance strategy. The step size of elite learning rate can be dynamically adjusted according to Pareto difference entropy and evolutionary state, thus forming a closed-loop evolutionary process. Finally, the classical example of multi-objective flexible job shop scheduling is solved, and compared with the related algorithms, the simulation results show that the proposed algorithm has obvious advantages in convergence accuracy and machine distribution uniformity.
【作者单位】：江南大学轻工过程先进控制教育部重点实验室;
【基金】：国家自然科学基金项目(61572237,61573167)资助
【分类号】：TB497;TP18

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