一种求解车间调度的混合免疫遗传算法  

Hybrid Immune Genetic Algorithm for Solving Job Shop Scheduling Problem

作　　者：徐雨 黄海松[1] 胡涞 XU Yu;HUANG Hai-song;HU Lai(Key Laboratory of Advanced Manufacturing Technology,Ministry of Education,Guizhou University,Guizhou Guiyang 550025,China)

机构地区：[1]贵州大学现代制造技术教育部重点实验室,贵州贵阳550025

出　　处：《机械设计与制造》2020年第9期287-291,共5页Machinery Design & Manufacture

基　　金：贵州省科技重大专项计划(黔科合重大专项[2017]3004);贵州工业攻关重点项目(黔科合GZ字[2015]3009);贵州工业攻关重点项目(黔科合GZ字[2015]3034)。

摘　　要：为了克服传统免疫遗传算法(IGA)在车间调度问题上易陷入局部最优的缺点,将免疫遗传算法(IGA)与模拟退火算法(SA)进行了结合,提出一种应用于车间作业调度的混合免疫遗传算法。为了有效的提高免疫遗传算法收敛速度和避免算法陷入局部最优解,此算法设计了一种基于适应度和浓度的自适应精英保留策略且重新设置了变异算子,即将变尺度变异和自适应变异算子进行了融合。最后利用“Muth and Thompson”基准问题进行仿真实验,验证了该算法在JSP问题中的高效性和可行性。In order to overcome the shortcomings of traditional immune genetic algorithm(IGA)which can easily fall into local optimum on the shop floor scheduling problem,an immune genetic algorithm(IGA)is combined with the simulated annealing algorithm(SA)to propose a hybrid immune genetic algorithmapplication for job shop scheduling.In order to effectively improve the convergence speed of the immune genetic algorithm and avoid the algorithm falling into the local optimal solution,this algorithm designs an adaptive elitism retention strategy based on fitness and concentration,thenresetting the mutation operator,that is,fusion of variable scale mutation and adaptive mutation operator.Finally,a simulation experiment was conducted using the“Muth and Thompson”benchmark problem,which verified the efficiency and feasibility of the algorithm in the JSP problem.

关 键 词：免疫遗传算法 模拟退火算法 JSP调度 

分 类 号：TH16[机械工程—机械制造及自动化]