改进近邻人工蜂群算法求解柔性作业车间调度问题  

Improved algorithm of near-neighbor artificial bee colony for flexible Job-Shop scheduling

作　　者：李瑞 徐华 杨金峰 顾一帆 Li Rui;Xu Hua;Yang Jinfeng;Gu Yifan(School of Artificial Intelligence&Computer Science,Jiangnan University,Wuxi Jiangsu 214122,China)

机构地区：[1]江南大学人工智能与计算机学院,江苏无锡214122

出　　处：《计算机应用研究》2024年第2期438-443,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(62106088)。

摘　　要：为了更好地解决以最小化最大完工时间为目标的柔性作业车间调度问题,提出了一种改进的人工蜂群算法。首先,采用随机选择和反向学习策略来提高初始蜜源的质量。同时,设计了一种新颖的特征表示方式,用于计算蜜源之间的距离。在引领蜂阶段,通过引入交叉和变异策略来优化种群中的近距离蜜源。在探索蜂阶段,引入了六种变邻域方法,以扩大解空间的搜索范围。而在侦查蜂阶段,则根据蜜源的潜力值剔除局部最优个体。在15个数据集上进行了广泛实验,实验结果表明,该改进算法性能明显优于其他四种著名的群智能优化算法。该研究为解决柔性作业车间调度问题提供了一种新的有效方法,对于实际生产调度具有重要的实用价值。This paper proposed an improved artificial bee colony algorithm to better address the flexible Job-Shop scheduling problem with the objective of minimizing the makespan.Firstly,it employed random selection and reverse learning strategies to enhance the quality of initial food sources.Simultaneously,it designed a novel feature representation method to calculate the distance between food sources.During the leading bee phase,it introduced cross-over and mutation strategies to optimize nearby food sources in the population.Moreover,in the exploring bee phase,it incorporated six variable neighborhood me-thods to expand the search space of solutions.Subsequently,in the scout bee phase,it eliminated local optima individuals based on the potential value of their food sources.This paper conducted extensive experiments on 15 datasets,and the results demonstrate that the performance of the proposed improved algorithm significantly outperforms that of four other well-known swarm intelligence optimization algorithms.This study provides a novel and effective approach for solving the flexible Job-Shop scheduling problem with the goal of minimizing the makespan,holding important practical value for real-world production scheduling.

关 键 词：人工蜂群算法 柔性作业车间调度 特征表示 邻居 变邻域搜索 潜在价值 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]