Computer Science ›› 2018, Vol. 45 ›› Issue (7): 214-218.doi: 10.11896/j.issn.1002-137X.2018.07.037

• Artificial Intelligence • Previous Articles     Next Articles

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

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

CLC Number: 

  • TP18
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