摘要
针对混合流水车间调度问题(Hybrid Flow Shop Scheduling Problem,HFSP),以总工位切换时间为目标函数建立仿真优化模型,并提出一种基于种群并行融合机制的改进遗传算法(PIGA)进行求解。通过仿真模型计算目标函数适应度值并在遗传算法的迭代进化中引入并行融合拆分机制;在传统遗传算法的基础上将精英保留策略引入个体选择机制,将自适应遗传因子引入交叉变异概率,建立改进自适应遗传算法;运用该算法进行仿真优化实验,实验结果证明了该算法的有效性。
For the hybrid flow shop scheduling problem(HFSP),this paper establishes a simulation optimization model with the total station switching time as the objective function,and proposes an improved genetic algorithm(PIGA)based on the population parallel fusion mechanism.The fitness value of objective function was calculated through simulation model,and the parallel fusion and resolution mechanism was introduced in the iterative evolution of genetic algorithm.Based on the traditional genetic algorithm,we introduced elite retention strategy into individual selection mechanism,and introduced adaptive genetic factors into crossover mutation probability,so as to establish an improved adaptive genetic algorithm.The simulation optimization experiment was carried out by the algorithm.The experimental result proves the effectiveness of the algorithm.
作者
张源
陶翼飞
王加冕
Zhang Yuan;Tao Yifei;Wang Jiamian(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,Yunnan,China)
出处
《计算机应用与软件》
北大核心
2022年第6期252-257,共6页
Computer Applications and Software
基金
国家自然科学基金项目(51165014)。
关键词
混合流水车间
自适应遗传算法
总工位切换时间
并行融合拆分机制
精英保留策略
Hybrid flow shop
Adaptive genetic algorithm
Total station switching time
Parallel fusion split mechanism
Elite retention strategy
