改进蚁群算法在热精轧负荷分配优化中的应用  

Application of improved ant colony algorithm in load distribution optimization of hot finishing mills

作　　者：李冬[1] 刘建昌[1] 谭树彬[1] 金阳[1] 张彩金 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110819 [2]中铝瑞闽铝板带有限公司装备能源部,福建福州350015

出　　处：《控制理论与应用》2014年第8期1077-1086,共10页Control Theory & Applications

基　　金：国家自然科学基金资助项目(50974145)

摘　　要：本文尝试用改进的蚁群算法(IACA)求解热精轧机组负荷分配优化问题.首先,建立负荷分配优化的目标函数和约束条件.为了避免蚁群算法(ACS)在加速收敛中出现停滞现象,IACA通过局部和全局信息素浓度更新、引入约束条件的蚂蚁视觉启发函数和基于轧制理论的智力启发函数对状态转移规则进行改进计算;为了保证算法在迭代后期能够收敛,IACA动态更新状态转移规则中的阈值常数和局部信息素浓度挥发系数.基于实际生产数据的仿真结果表明,IACA能够按照目标函数的要求进行合理的负荷分配,且解的性能优于经验值;与其他优化算法比较,IACA具有较快的求解速度和较高的求解精度.We employ the improved ant colony algorithm （IACA） to investigate the load distribution optimization problem of hot finishing mills.Firstly,objective functions and constraints of load distribution are established.In order to avoid the stagnation of ant colony system （ACS） in accelerating convergence process,IACA makes improvement on the calculation of state transition rule by updating the local and the global pheromone concentration and introducing constrained ant-visual heuristic function as well the intelligence heuristic function based on the rolling theory.In addition,to ensure the algorithm for converging in the last stage of iteration,the volatile coefficient of local pheromone concentration and the threshold constant in the state transition rule are all dynamically adjusted by IACA.Experimental results based on practical production data indicate that solutions given by IACA are able to make load distribution reasonable in accordance with requirements of objective functions,and to perform better than the empirical load distribution solution.In addition,IACA provides faster and more accurate solution than the other optimization methods.

关 键 词：热轧机 负荷分配 改进蚁群算法 优化 

分 类 号：TG335[金属学及工艺—金属压力加工] TP18[自动化与计算机技术—控制理论与控制工程]