基于交叉算子改进遗传算法在柔性作业车间的研究  

Research on an Enhanced Genetic Algorithm with Cross Operator for Flexible Manufacturing Workshop

作　　者：郜振华[1] 乔恒赟 张洪亮[1] GAO Zhenhua;QIAO Hengyun;ZHANG Hongliang(Anhui University of Technology,Ma'anshan 243000,China)

机构地区：[1]安徽工业大学管理科学与工程学院,安徽马鞍山243000

出　　处：《通化师范学院学报》2024年第12期1-9,共9页Journal of Tonghua Normal University

基　　金：安徽省哲学社会科学规划项目(AHSKY2022D117)。

摘　　要：针对柔性作业车间调度问题(Flexible Job-shop Scheduling Problem,FJSP),该文提出了一种改进的自适应三体交叉算子遗传算法模型.该模型以最小化工件最大完工时间为目标,通过引入三体交叉算子,有效促进了最优解的产生.同时,结合自适应的交叉和变异概率,提高了算法搜索最优解的能力,加速了种群收敛.实验结果表明:与传统的灰狼优化算法和混合量子粒子群优化启发式算法相比,该改进算法在柔性作业车间调度问题中具有更好的性能,显著提升了搜索最优解的能力.For the Flexible Job-shop Scheduling Problem(FJSP),this paper presents an improved adaptive three-body crossover operator genetic algorithm model.The model aims to minimize the maximum completion time of the workpiece and effectively promotes the generation of the optimal solution by introducing the three-body crossover operator.At the same time,combined with the adaptive crossover and mutation probability,the algorithm can improve the ability of searching the optimal solution and accelerate the population convergence.The experimental results show that the improved algorithm has better performance in flexible job-shop scheduling problems than traditional Gray Wolf optimization algorithm and hybrid quantum particle swarm optimization heuristic algorithm,and significantly improves the ability to search for optimal solutions.

关 键 词：柔性作业车间调度 三体交叉算子 自适应概率 遗传算法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]