计算机科学 ›› 2018, Vol. 45 ›› Issue (7): 214-218.doi: 10.11896/j.issn.1002-137X.2018.07.037

• 人工智能 • 上一篇    下一篇

改进的粒子群算法在轧制负荷分配中的优化

李荣雨,张卫杰,周志勇   

  1. 南京工业大学计算机科学与技术学院 南京211816
  • 收稿日期:2017-02-28 出版日期:2018-07-30 发布日期:2018-07-30
  • 作者简介:李荣雨(1977-),男,博士,副教授,主要研究方向为工业过程的优化与监控,E-mail:alleric@yeah.net(通信作者);张卫杰(1990-),男,硕士生,主要研究方向为机器学习方法在工业过程中的应用,E-mail:alleric@yeah.net。
  • 基金资助:
    本文受江苏省高校自然科学基金(12KJB510007)资助。

Improved PSO Algorithm and Its Load Distribution Optimization of Hot Strip Mills

LI Rong-yu ,ZHANG Wei-jie ,ZHOU Zhi-yong   

  1. College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China
  • Received:2017-02-28 Online:2018-07-30 Published:2018-07-30

摘要: 针对带钢热连轧精轧机组中负荷分配的优化问题,提出一种基于经验的自适应双层粒子群优化算法(ADLPSO-EM)。每次种群迭代后,对记忆群体通过改进的更新公式进行更新。利用改进的自适应调整惯性权重的策略充分增强种群的多样性,提高全局搜索能力。最后,在将其应用于热连轧负荷分配问题时,通过以经验法得到的值产生一个搜索邻域,并通过变邻域求出最后的负荷分配。仿真结果表明,改进的算法对负荷分配优化具有明显的效果。

关键词: 变邻域, 负荷分配, 记忆群体, 经验法, 粒子群优化, 自适应调整

Abstract: Aiming at the load distribution problem of hot strip rolling,an adaptive double layer particle swarm optimization algorithm based on empirical method (ADLPSO-EM) was proposed.After each population iteration,the algorithm usesimproved speed update formula to update memory swarm.At the same time,in order to improve the diversity of the population,it uses an improved adaptive adjustment strategy to update inertia weight.Finally,The initialization section of the algorithm is a changeable neighborhood based on the value obtained by the empirical method in load distribution problem.The experimental results show that the improved algorithm has a significant effect on the load distribution optimization.

Key words: Adaptive adjustment, Changeable neighborhood, Empirical method, Load distribution, Memory swarm, Particle swarm optimization

中图分类号: 

  • TP18
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