基于混合演化的自适应建模及其应用

doi:10.3785/j.issn.1008-973X.2010.08.010

2010, Vol. 44

Issue (8): 1490-1495 DOI: 10.3785/j.issn.1008-973X.2010.08.010

自动化技术、计算机技术

基于混合演化的自适应建模及其应用

倪何1,2 , 程刚2, 孙丰瑞1

1. 海军工程大学动力工程系，湖北武汉 430033; 2. 海军工程大学装备仿真研究所，湖北武汉 430033

Adaptive hybrid evolutionary modeling method and its application

NI He1,2, CHEN Gang2, SUN Feng-rui1

1. Department of Power Engineering, Naval University of Engineering, Wuhan 430033, China;
2. Research Certain of Naval Power Plant Simulation, Naval University of Engineering, Wuhan 430033,China

全文: PDF HTML

摘要：

针对传统的遗传规划方法收敛效率低、模型精度不高等缺点,将进化策略应用于模型参数的全局最优搜索,在遗传规划建模的基础上使用进化策略修正模型参数,以实现对模型结构和参数的同时优化,通过2种演化算法的结合提高建模精度，同时采用最优保持策略加速优化过程,改善算法的收敛效率.将该方法应用于某型船用汽轮机组的仿真建模中,通过和传统演化建模的对比,证明采用混合演化策略的建模方法具有更快的求解速度和更好的全局搜索能力.

Abstract:

An improved genetic programming modeling method has been proposed to improve the traditional genetic programming method's shortcomings: low constringent efficiency and nonhigh model precision. This method used evolution strategy to modify the parameters of models which established by means of the genetic programming. By the combining of GP and ES, the global optimal searches of model parameters and structures were carried out at the some time, which heightened the modeling precision. By simultaneously adopted optimumkeeping strategy, we also ensured algorithm's constringent efficiency. Used this method to modeling and simulated a certain type marine steam turbine and compared the simulative results with general GP method, we find the hybrid evolutionary modeling method has faster modeling speed and better global search abilities.

出版日期: 2010-09-21

TP 301.6

作者简介: 倪何(1982-)，男，上海人，博士生，主要从事热力系统的设计、优化和仿真研究.E-mail: elegance2006@sina.com

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章

引用本文:

倪何, 程刚, 孙丰瑞. 基于混合演化的自适应建模及其应用[J]. J4, 2010, 44(8): 1490-1495.

NI He, Cheng-Gang, SUN Feng-Rui. Adaptive hybrid evolutionary modeling method and its application. J4, 2010, 44(8): 1490-1495.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2010.08.010 或 http://www.zjujournals.com/eng/CN/Y2010/V44/I8/1490

［1］ PAN Zhenjun, KANG Lishan, LI Yunxun. An adaptive modeling algorithm based on evolutionary ［C］∥ The 2nd Global Chinese Congress an Intelligence Control and Intelligence Automatization (CWCICIA’97). Xian:Xt’an Jiaotong Unicrersity press, 1997: 886889．
［2］曹宏庆. 基于遗传程序设计的复杂系统演化建模［D］. 武汉: 武汉大学, 1999．
CAO Hongqing. Evolutionary modeling for complex system by means of genetic programming ［D］. Wuhan: Wuhan University, 1999．
［3］ KOZA J R. Automatic creation of humancompetitive programs and controllers by means of genetic programming ［J］. Genetic Programming and Evolvable Machines, 2000, 1(2): 121164．
［4］ LANGDON W B. Genetic programming and data structure ［D］: University London,1996.
［5］谢大同, 康立山. 函数优化的一种高效演化算法［J］. 计算机工程与应用, 2007, 43(4): 4446.
XIE Datong, KANG Lishan. High efficient evolutionary algorithm for function optimization ［J］. Computer Engineering and Applications, 2007, 43(4): 4446．
［6］熊盛武, 卢孝强. 偏微分方程参数识别问题的遗传程序设计方法［J］. 武汉理工大学学报：信息与管理工程版, 2003, 25(3): 1115．
XIONG Shenwu, LU Xiaoqiang. A Genetic programming approach to partial differential equation inverse problems ［J］. Journal of WUT :Information and management edition, 2003, 25(3): 1115．
［7］周爱民, 曹宏庆, 康立山, 等. 用遗传程序设计实现复杂函数的自动建模［J］. 系统仿真学报, 2003, 15(6) : 797799．
ZHOU Aiming, CAO Hongqing, KANG Lishan, et al. The automatic modeling of complex functions based on genetic programming ［J］. Journal of System Simulation, 2003, 15(6): 797799．
［8］ CAO Hongqing, KANG Lishan, CHEN Yuping. The modeling of system of ordinary differential equations for dynamic systems:the experimental study based on different search techniques ［J］. Journal of Computer Research and Development, 2001, 38(6): 746753．
［9］康立山, 刘溥, 陈毓屏. 高阶非线性微分方程的并行演化建模算法［J］. 微电子学与计算机, 2000(6): 2328．
KANG Lishan, LIU Fu, CHEN Yuping. A parallel evolutionary algorithm on modeling high order differential equation ［J］. Microelectronics and Computer, 2000(6): 2328．
［10］ KOZA J R. Genetic programming: on the programming of computers by means of natural selection ［M］. Cambridge: MIT press, 1994．
［11］ KOZA J R. Genetic programming Ⅱ: automatic discovery of reusable programs ［M］. Cambridge: MIT press, 1992．

［12］ CAO Hongqing, KANG Lishan. A hybride evolutionary modeling algorithm for system of ordinary equation ［J］. Neutral, Parallel & Sisntific Computations, 1998, 6(2): 171188．
［13］董文永, 李元香. 二次演化建模在实时仿真中的应用［J］. 计算机研究和发展. 2002, 39(10): 12611268．
DONG Wunyong, LI Yuanxiang. A new method of evolutionary modeling used in realtime simulation ［J］. Journal of Computer Research and Development, 2002, 39(10): 12611268．
［14］ BACK T, SCHWEFUL H P. Evolution strategies Ⅰ: Variants and their computational implementation ［C］∥ Genetic Algorithms in Engineering and Computer Science.New York: Wiley, 1995: 111126．
［15］ BACK T, SCHWEFUL H P. Evolution strategies Ⅱ: theoretical aspects ［C］∥ Genetic Algorithms in Engineering and Computer Science, New York: Wiley, 1995: 127140．
［16］王小平, 曹立明. 遗传算法:理论、应用与软件实现［M］.西安：西安交通大学出版社, 2002: 8991．
［17］倪何. 新型蒸汽动力装置热力系统模块化建模与仿真［D］. 武汉：海军工程大学, 2006．
NI He. The module modeling and simulation for the thermal system of a new type steam power plant ［D］. Wuhan: Naval University of Engineering, 2006．
［18］张明, 周永权. 遗传规划和进化策略混合算法及应用［J］. 计算机工程和应用, 2007, 43(1): 7982．
ZHANG Ming, ZHOU Yongquan. Application of genetic programming and evolution strategy in fitting function ［J］. Computer Engineering and Applications, 2007, 43(1): 7982．

[1]	刘加海,杨茂林,雷航,廖勇. 共享资源约束下多核实时任务分配算法[J]. J4, 2014, 48(1): 113-117.
[2]	赵诗奎, 方水良, 顾新建. 柔性车间调度的新型初始机制遗传算法[J]. J4, 2013, 47(6): 1022-1030.
[3]	张俊超, 岳茂雄, 刘华锋. 结构先验约束的动态PET图像重建[J]. J4, 2012, 46(6): 961-966.
[4]	刘毅,李平,高增梁. 用于高炉铁水质量预报的改进支持向量回归[J]. J4, 2012, 46(5): 830-836.
[5]	方水良, 姚嫣菲, 赵诗奎. 柔性车间调度的改进遗传算法[J]. J4, 2012, 46(4): 629-635.
[6]	喻海清, 刘毅, 陈坤, 纪俊, 李平. 鲁棒的递推核学习建模方法在高炉过程的应用[J]. J4, 2012, 46(4): 705-711.
[7]	刘加海,杨茂林. 基于多核处理器平台的公平调度算法[J]. J4, 2011, 45(9): 1566-1570.

Viewed

Full text

Abstract

Cited

Shared

Discussed