求解多目标柔性作业车间的IGWO算法

IGWO algorithm for solving multi-objective flexible job shop

针对多目标柔性作业车间调度问题(multi-objective flexible job shop scheduling problem,MOFJSP),提出一种改进灰狼算法(improved grey wolf algorithm,IGWO)来求解考虑完工时间,总能耗以及机器总负荷的多目标优化。IGWO采用二段式编码和基于权重的种群初始化方法,加入遗传算子对编码进行迭代更新,采用Pareto非支配排序和拥挤度距离来求取迭代过程中的非支配解,将非支配解集保存在外部存档中;引入非线性收敛因子,平衡算法的全局搜索能力和局部搜索能力。通过引入改进鲶鱼效应策略,保证种群活力,提高算法收敛精度,避免算法陷入局部最优解。最后通过机加工车间实例验证和对比实验,验证该算法的可行性和优越性。

For the multi-objective flexible job shop scheduling problem(MOFJSP),an improved grey wolf algorithm(IGWO)was proposed to solve the multi-objective optimization considering the completion time,total energy consumption and total machine load.IGWO uses binary coding and population initialization based on weights,adds genetic operators to update the coding iteratively,uses Pareto nondominated sorting and crowding degree distance to find the non-dominated solution in the iterative process,and saves the non-dominated solution set in the external archive.The nonlinear convergence factor is introduced to balance the global search ability and local search ability of the algorithm.By introducing an improved catfish effect strategy,the population vitality is guaranteed,the convergence accuracy of the algorithm is improved,and the algorithm is avoided to fall into the local optimal solution.Finally,the feasibility and superiority of the algorithm are verified by a machining shop example and a comparative experiment.