摘要
应用改进粒子群算法,实现单机架可逆冷轧机轧制负荷分配优化。结合某厂单机架可逆冷轧机实际工况,建立了合适的轧制力数学模型并进行参数计算,以压下率为自变量,以轧制力成比例分配为目标,通过改进粒子群算法,得到最佳的轧制负荷分配。工程实践证明:与基本粒子群算法、遗传算法相比,改进粒子群算法具有计算精度更高、收敛速度更快、搜索能力更强等优点,是一种适于单机架可逆冷轧机轧制负荷分配优化的新方法。
The load distribution optimization of single stand reversible cold-strip mill was realized through improved particle swarm optimization （1PSO）. Based on actual working conditions of a single stand reversible cold-strip mill in a factory, adaptive roiling force model was built and parameters were calculated. By taking the reduction rate as independent variable and the rolling pressure pro- portionate distribution as objective function, using IPSO, the best load distribution was obtained. The engineering practice demonstrates that the IPSO at the aspect of load distribution optimization has the advantages of higher calculation accuracy, faster convergence speed, stronger exploration ability, compared with the basic particle swarm algorithm and genetic algorithm. It is a new method suitable for load distribution optimization of single stand reversible cold-strip mill.
出处
《机床与液压》
北大核心
2011年第3期13-16,共4页
Machine Tool & Hydraulics
基金
国家自然科学基金资助项目(50775198)
河北省自然科学基金资助项目(E2008000812)
关键词
单机架可逆轧机
负荷分配优化
改进粒子群算法
Single stand reversible cold-strip mill
Load distribution optimization
Improved particle swarm optimization
