摘要
针对现有遗传算法在解决柔性作业车间调度问题中易产生非法解且运行时间长等缺陷,提出了一种求解该问题的改进遗传算法。本算法优化了初始解的产生机制,使用了随机联赛选择和精英选择相结合的选择策略,加快了算法的收敛速度,同时设计了一种新的基于机器的两点交叉和基于工序的单点变异操作,完全避免了算法运行过程中由于生成非法解而引起的染色体的修复和重建过程,减少了算法的运行时间。仿真结果表明,改进后的算法具有寻优能力强,运行时间短等优点。
An improved genetic algorithm is proposed to overcome the shortcomings of traditional genetic algorithm, such as easy to produce illegal solutions and long running time when solving FJSP. The algorithm optimizes the generation mechanism of the initial population, and uses the combined strategies of random league selection and elite selection to accelerate the convergence speed of the algorithm. In addition, a new machine-based two-point crossover and process-based single-point mutation operation is designed to avoid the occurrence of illegal solutions in the operation sequence, thus reducing the running time of the algorithm. The simulation results show that the proposed algorithm has the advantages of stronger searching ability and shorter running time.
作者
罗雄
钱谦
伏云发
Luo Xiong;Qian Qian;Fu Yunfa(School of information Engineering and Automation,Yunnan Key Laboratory of Computer Technology Applications,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电子测量技术》
2020年第7期36-40,共5页
Electronic Measurement Technology
关键词
遗传算法
柔性作业车间调度
两点交叉
单点变异
genetic algorithm
flexible job shop scheduling
two-point crossover
single-point mutation
作者简介
罗雄,硕士研究生,主研研究方向为计算智能。E-mail:386896124@qq.com;通信作者:钱谦,副教授/博士,主研研究方向为视觉认知和计算智能。E-mail:qianqian_yn@126.com;伏云发,教授/博士,主研研究方向为模式识别与智能控制,脑信息处理与脑控机器人接口方向研究工作。
