改进的粒子群算法在轧制负荷分配中的优化

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

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。

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 uses improved 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.