改进的量子遗传算法在冷连轧机负荷分配中的应用研究

Application research on improved quantum genetic algorithm for load distribution optimization of tandem cold rolling mill

量子遗传算法可以克服常规遗传算法迭代次数多、易陷入局部极值的缺点,本文研究并改进了量子遗传算法的量子门旋转角度更新策略,提高了量子遗传算法的性能。应用标准函数测试表明,改进后的量子遗传算法收敛速度快,全局寻优能力更强。将改进的量子遗传算法应用于冷连轧机的负荷分配优化过程,根据Bland-Ford轧制理论以功率和轧制力均衡分配为目标建立综合目标函数,试验数据对比证明,量子遗传算法优化所得的轧制参数比经验分配和常规遗传算法优化所得结果更为合理,符合轧制工艺要求。因此,利用量子遗传算法对冷连轧机进行负荷分配优化是一种有效可行的新方法。

A quantum genetic algorithm is introduced for load distribution of tandem cold rolling mill in order to avoid the shortcomings of genetic algorithm,such as more iterations,slow convergence and easily trapped into local minima.A new adjusting strategy of quantum rotational gate is proposed for improving the performance of quantum genetic algorithm.It is shown by standard function tests and data comparison that the improved quantum genetic algorithm can avoid the premature convergence,has stronger ability of global optimization,and is an effective method for the optimization of the load distribution of tandem cold rolling mill.