摘要
为了解决作业车间调度问题,以最小化最大完工时间为目标,结合遗传算法(GA)与麻雀搜索算法(SSA),提出了一种混合麻雀搜索算法。首先采取基于工序的编码方式构建了一种转换机制,将SSA中的麻雀个体位置与工序编码相对应。然后针对SSA在求解过程中容易陷入局部最优的问题,采用侦察者数量递减策略,结合GA中的变异操作来提高SSA跳出局部最优的能力。在发现者探索阶段加入GA中的交叉操作,以提高算法的收敛速度。最后以FT06、FT10等测试问题以及2个应用实例为例,证明混合麻雀搜索算法在求解作业车间调度问题时,与其他算法相比有更快的收敛速度、更高的寻优成功率和更强的寻优能力,证明了所提算法的有效性。
In order to solve the job-shop scheduling problem and minimize the maximum completion time, a hybrid sparrow search algorithm is proposed combining genetic algorithm(GA) and sparrow search algorithm(SSA).Firstly, the coding method based on the procedure is adopted, and a transformation mechanism is constructed to match the sparrow individual position in the sparrow search algorithm(SSA) with the procedure code. Then, to solve the problem that SSA is prone to fall into local optimum in the process of solving, the decreasing number of scouts strategy is adopted to improve the ability of SSA to jump out of local optimum combined with mutation operation in GA. In the discoverer exploration stage, the crossover operation in GA is added to improve the convergence speed of the algorithm. FT06, FT10 and other test problems as well as two application examples are compared with other algorithms. The results show that the hybrid sparrow search algorithm has faster convergence speed, higher success rate and higher searching ability when solving job-shop scheduling problems, which shows the effectiveness of the proposed algorithm.
作者
李保伟
喻明让
陈云
Li Baowei;Yu Mingrang;Chen Yun(College of Mechanical and Electrical Engineering,North University of China,Shanxi Taiyuan,030051,China;North Institute of Automatic Control Technology,Shanxi Taiyuan,030006,China)
出处
《机械设计与制造工程》
2022年第12期93-97,共5页
Machine Design and Manufacturing Engineering
基金
山西省基础研究计划项目(20210302123050)。
关键词
作业车间调度
遗传算法
麻雀搜索算法
编码转换
job shop scheduling
genetic algorithms
sparrow search algorithm
encoding conversion
