计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 668-672.doi: 10.11896/jsjkx.210300118

• 交叉&应用 • 上一篇    

基于莱维萤火虫算法的智能生产线调度问题研究

章菊, 李学鋆   

  1. 湖北汽车工业学院汽车工程学院 湖北 十堰442002
    汽车动力传动与电子控制湖北省重点实验室 湖北 十堰442002
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 章菊 (fionanzhang@163.com)
  • 基金资助:
    湖北省教育厅科学研究计划项目 (B2019083);湖北省重点实验室开放基金项目(ZDK1201905)

Research on Intelligent Production Line Scheduling Problem Based on LGSO Algorithm

ZHANG Ju, LI Xue-yun   

  1. School of Vehicle Engineering,Hubei University of Automotive Technology,Shiyan,Hubei 442002,China
    Key Laboratory of Automotive Power Train and Electronics,Hubei University of Automotive Technology,Shiyan,Hubei 442002,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:ZHANG Ju,born in 1988,master,lecturer.Her main research interests include mechanical dynamics and intelligent control.
  • Supported by:
    Scientific Research Project of Hubei Provincial Department of Education(B2019083) and Hubei Key Laboratory Open Fund Project(ZDK1201905).

摘要: 针对智能生产线调度过程中易出现饥饿和堵塞等问题,通过分析调度过程,建立调度问题的目标函数和约束条件;然后提出一种基于莱维飞行的新型萤火虫算法,利用莱维分布提高种群的搜索范围和有效性,以最大和最小荧光素作为边界约束优化荧光素迭代公式,提高个体所携带荧光素的合理性;引入立方映射实现对种群的优化,提高种群的综合搜索能力。算法测试结果表明,莱维萤火虫算法(Levy Glowworm Swarm Optimization,LGSO)相比GSO(Glowworm Swarm Optimization)、SGSO(Glowworm Swarm Optimization of Scene Understanding )和CGSO(Chaos Glowworm Swarm Optimization)具有更好的求解精度、收敛性和稳定性。利用LGSO算法对典型的4种智能生产线的调度问题进行优化求解,并与GSO算法和SGSO算法进行对比,结果表明:LGSO算法计算结果的最差值、最优值、结果平均值以及标准偏差基本均优于其他两种算法,特别在复杂路径下,LGSO算法具有更好的求解精度、收敛速度及稳定性,验证了数学模型的准确性和LGSO算法解决调度问题的可行性。

关键词: 调度问题, 莱维飞行, 萤火虫优化算法, 智能生产线

Abstract: Aiming at the problems of starvation and congestion in the scheduling process of intelligent production line,the objective function and constraint conditions of scheduling problem are established by analyzing the scheduling process.Then a new glowworm swarm optimization algorithm based on Levy flight is proposed.Levy distribution is used to improve the search range and effectiveness of the population.The maximum and minimum fluorescein are taken as boundary constraints to optimize the ite-rative formula of fluorescein,and to improve the rationality of the fluorescein carried by individuals.And the cubic mapping is introduced to optimize the population,so as to improve the comprehensive search ability of the population.The algorithm test results show that LGSO is provided with better solution accuracy,convergence and stability than GSO,SGSO and CGSO.LGSO algorithm is used to solve the scheduling problems of four typical intelligent production lines,and compared with GSO and SGSO.The results show that LGSO is basically better than the other two algorithms in the worst value,the optimal value,the average value and the standard deviation.In the complex path,LGSO has better solution accuracy,convergence speed and stability.Further,the accuracy of the mathematical model and the feasibility of LGSO to solve the scheduling problem are verified by the test.

Key words: Glowworm swarm optimization algorithm, Intelligent production line, Levy flight, Scheduling problem

中图分类号: 

  • O221
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