作　　者： (牛琳）;

机构地区： 海南医学院医学信息学院,海口571100

出　　处： 《组合机床与自动化加工技术》 2017年第8期157-160,共4页

摘　　要： 针对柔性作业车间调度中单纯的遗传算法容易陷入局部陷阱问题,结合柔性作业车间调度的特点,采用模拟退火算法融合遗传算法对调度领域进行了研究。应用模拟退火算法能跳出局部陷阱的能力及克服了遗传算法过早熟的现象,很大程度上降低算法的收敛速度,同时提高了全局的收敛性。基于Matlab2012b软件编程实现混合调度算法,文中仿真实例用混合调度算法,将结果与单纯的遗传算法得到的结果进行比较,证明了混合算法的优势。 The quality of the initial population of genetic algorithm have a decisive influence on the quality and speed. When traditional genetic algorithm is applied in solving flexible job shop scheduling problems, the initial population is randomly generated, which may result in forming many infeasible solutions at the be- ginning of the iteration. Only through a complex operation will form optimum solutions, it may greatly re- duces convergence speed of the algorithm. After study the characteristics of flexible job shop scheduling ,Ini- tial population give rules of base on the entire Search to code and generate initial population＇s strategy, has been put forward. When the quality of initial population be improved, its diversity also won＇t lose. At the same time, its global convergence can be improved. The instance in this article using the improved genetic algorithm ,the results are compared with the traditional genetic algorithm＇s results, It proved that the advanta- ges of improved algorithm.

关 键 词： 遗传算法 柔性作业车间调度 初始种群