[1]丁 凯,赵欣悦,吕景祥,等.改进天牛群算法在柔性作业车间调度中的应用[J].郑州大学学报(工学版),2024,45(03):111-118.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 012]
　DING Kai,ZHAO Xinyue,LYU Jingxiang,et al.Improved Beetle Swarm Optimization Algorithm for Flexible Job-shop Scheduling[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):111-118.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 012]
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改进天牛群算法在柔性作业车间调度中的应用()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45卷
期数:
2024年03期
页码:
111-118
栏目:
出版日期:
2024-04-20

文章信息/Info

Title:
Improved Beetle Swarm Optimization Algorithm for Flexible Job-shop Scheduling
文章编号:
1671-6833( 2024) 03-0111-08
作者:
丁 凯;  赵欣悦;  吕景祥;  朱 斌
长安大学 智能制造系统研究所,陕西 西安 710064
Author(s):
DING Kai;  ZHAO Xinyue;  LYU Jingxiang;  ZHU Bin
Institute of Smart Manufacturing Systems, Chang􀆳an University, Xi􀆳an 710064, China
关键词:
柔性作业车间调度;  天牛群算法;  莱维飞行策略;  反向搜索策略;  自适应参数调整
Keywords:
flexible job-shop scheduling;  beetle swarm optimization;  Levy flight;  reverse search;  adaptive parameter adjustment
分类号:
TH165TH18
DOI:
10. 13705/ j. issn. 1671-6833. 2024. 03. 012
文献标志码:
A
摘要:
为解决柔性作业车间调度问题,在模拟自然界中天牛觅食行为的天牛须算法基础上,结合群智能优化理论,提出了一种基于莱维飞行、反向搜索和自适应参数调整混合策略的改进天牛群算法( LRA-BSO) 。首先,建立柔性作业车间调度模型;其次,提出了基于Tent 混沌映射生成初始种群的方法,以提高初始种群质量;再次,应用莱维飞行策略和反向搜索策略,并通过适应度反馈自适应调整天牛群的搜索步长以及搜索距离,以改善算法全局搜索能力,避免陷入局部极值;最后,为验证改进的天牛群算法的性能,通过6 个多维度标准测试函数验证了LRA-BSO算法的寻优能力。通过FJSP 的10 个标准算例和1 个实际案例验证了LRA-BSO 算法在FJSP 中的适用性。测试结果表明:改进的天牛群算法在8 个标准算例中的表现均优于或持平于其他智能优化算法,表现出了较好的寻优能力;在实际案例验证中,改进后的算法相对于原始的天牛群算法,在收敛速度上提升了48%。
Abstract:
To solve the flexible job shop scheduling problem(FJSP), a hybrid Levy flight, reverse search, and parameter adaptive adjustment strategy improved beetle swarm optimization (LRA-BSO) was proposed based on the beetle antennae search algorithm which could simulate the foraging behavior of beetles in nature and the swarm intelligence optimization theory. Firstly, a FJSP model was established. Secondly, the initial population was generated based on the Tent chaotic mapping, which would improve the quality of the initial population. Then, the Levy flight strategy and reverse search strategy were used to improve the global search ability of the LRA-BSO algorithm, and the search step size and the search distance of the beetle swarm were adjusted through fitness feedback to avoid falling into local optimum. Finally, the optimization ability of the algorithm was validated through 6 multi-dimensional standard test functions. In addition, the applicability of the LRA-BSO algorithm in FJSP was verified by 10 standard test cases and 1 practical case. The test results showed that the algorithm performed better or equal to other intelligent optimization algorithms in eight standard test cases and demonstrated good optimization ability. In the practical cases, the improved algorithm had a 48% improvement in convergence speed compared to the original beetle swarm optimization algorithm.

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相似文献/References:

[1]吴秀丽,张志强.求解柔性作业车间调度问题的细菌算法对比及改进[J].郑州大学学报(工学版),2018,39(03):34.[doi:10.13705/j.issn.1671-6833.2017.06.018]
　Wu Xiuli,Zhang Zhiqiang.The Comparison and Improvement of Bacterial Algorithms for Flexible Job Scheduling Problem[J].Journal of Zhengzhou University (Engineering Science),2018,39(03):34.[doi:10.13705/j.issn.1671-6833.2017.06.018]

备注/Memo

备注/Memo:
收稿日期:2023-10-23;修订日期:2023-11-20
基金项目:国家自然科学基金资助项目(51705030);中国博士后科学基金特别资助项目(2022T150073)
作者简介:丁凯(1989— ),男,江苏淮安人,长安大学教授,博士,主要从事制造系统智能化研究,E-mail:kding@ chd.edu. cn。
更新日期/Last Update: 2024-04-29