二冷配水优化建模与混合自适应粒子群算法求解  

Continuous Cast Secondary Cooling Zone Optimization and Hybrid Adaptive Dynamic Particle Swarm Optimization

作　　者：曾燕[1] 王晓[1] 成新文[1] 

机构地区：[1]四川理工学院计算机学院,四川自贡643000

出　　处：《计算机测量与控制》2015年第3期847-851,共5页Computer Measurement &Control

基　　金：四川理工学院科研项目资助(2013KY04);酿酒生物技术及应用四川省重点实验室开放基金项目(NJ2011-09);企业信息化与物联网测控技术四川省高校重点实验室开放基金项目(2014WYJ08)

摘　　要：针对连铸二冷区生产环境复杂且存在着大量水雾干扰的情况,建立了连铸水量优化模型并提出了一种混合的自适应粒子群算法来求解连铸二冷水优化问题;依据冶金过程中的工艺要求建立了二冷水量优化模型,并在经典的PSO算法基础上提出了适合该问题求解了混合自适应PSO算法;由于连铸过程存在着偏微分方程约束,传统的优化方法容易陷入局部最优解,不能达到很好的动态优化效果;研究了粒子群算法,基于种群的多样性,不断的自适应的更新粒子群算法中参数,将禁忌搜索的方法和传统的粒子群算法结合,增强了算法的局部搜索能力和全局寻找全局最优的能力;将该算法应用到连铸二冷水动态优化中,实验结果表面该算法能够快速有效的求解该优化问题;该方法用于连铸二冷水优化是可行的、有效的。For casting secondary cooling zone production environment is complex and there are a lot of mist interference, the establishment of a continuous casting water optimization model and proposed an adaptive hybrid particle swarm algorithm to solve the optimization problem casting secondary cooling water. Based on the metallurgical process technology requirements established two cold water volume optimization model and on the basis of the classical PSO algorithm is proposed for solving the problem of the hybrid adaptive PSO algorithm. Due to the existence of partial differential equations casting process constraints, traditional optimization method is easy to fall into local optimal solution, dynamic optimization can not achieve good results. PSO study, based on population diversity, constantly updated adaptive particle swarm algorithm parameters, tabu search methods and traditional PSO algorithm combined to enhance the local search algorithm to find the global optimum capacity and global capabilities. The algorithm is applied to dynamic optimization of casting two of cold water, the surface of the experimental results that the algorithm can quickly and efficiently solve the optimization problem. This method is used to optimize casting two cold water is feasible and effective.

关 键 词：粒子群算法 自适应 禁忌搜索 连铸 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]