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| 基于HPSO的钢坯加热过程炉温优化设定 | |
| 引用本文： | 廖迎新,吴敏,佘锦华,曹卫华.基于HPSO的钢坯加热过程炉温优化设定[J].控制理论与应用,2007,24(6):1010-1014. |
| 作者姓名： | 廖迎新 吴敏 佘锦华 曹卫华 |
| 作者单位： | 1. 中南大学,信息科学与工程学院,湖南,长沙,410083;中南林业科技大学,电子与信息工程学院,湖南,长沙,410004;东京工科大学,仿生学学部,日本,东京,192-0982 2. 中南大学,信息科学与工程学院,湖南,长沙,410083 3. 东京工科大学,仿生学学部,日本,东京,192-0982 |
| 基金项目： | 国家杰出青年科学基金资助项目(60025310)，国家863计划项目(2006AA04Z172)，湖南省科技计划项目(04FJ3029)． |
| 摘 要： | 针对一种蓄热推钢式加热炉三个加热区的炉温稳态优化问题,本文提出了一种混合粒子群优化(HPSO)方法.首先,基于钢坯导热偏微分方程和边界条件,建立钢坯温度预报模型.然后,采用HPSO算法确定最佳稳态炉温,即炉温控制的参考输入.该方法利用混沌机制产生初始种群,通过免疫和克隆来提高粒子群优化(PSO)算法的全局搜索能力和搜索精度. |
| 关 键 词： | 加热炉 稳态优化 混合粒子群优化 免疫 克隆 |
| 文章编号： | 1000-8152（2007）06-1010-05 |
| 收稿时间： | 2006-11-09 |
| 修稿时间： | 2007-04-29 |
Hybrid particle swarm optimization of temperature settings of billet-reheating process |
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| LIAO Ying-xin,WU Min,SHE Jin-hu,CAOWei-hua.Hybrid particle swarm optimization of temperature settings of billet-reheating process[J].Control Theory & Applications,2007,24(6):1010-1014. | |
| Authors: | LIAO Ying-xin WU Min SHE Jin-hu CAOWei-hua |
| Affiliation: | School of Information Science and Engineering, Central South University, Changsha Hunan 410083, China;School of Electron and Information Engineering, Central South University of Forestry and Technology,Changsha Hunan 410004, China; School of Bionics, Tokyo University of Technology, Tokyo 192-0982, Japan |
| Abstract: | A hybrid particle swarm optimization (HPSO) approach is presented to solve the problem of optimizing the steady-state temperatures of the three zones of a billet reheating furnace. Firstly, a model for predicting billet temperature is constructed based on a heat transfer equation and its boundary conditions. Then, HPSO is used to determine the optimal steady-state temperatures, which are reference inputs for the control of furnace temperature. This approach uses a chaos mechanism to create the initial population, and adopts immunity and cloning to improve the global search capability and search precision of particle swarm optimization. |
| Keywords: | reheating furnace steady-state optimization hybrid particle swarm optimization immunity cloning |
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