A New Strategy for Parameter Estimation of Dynamic Differential Equations Based on NSGA II

  • Yingzi Shi
  • Jiangang Lu
  • Qiang Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


A new strategy for parameter estimation of dynamic differential equations based on nondominated sorting genetic algorithm II (NSGA II) and one-step-integral Treanor algorithm is presented. It is adopted to determine the exact model of catalytic cracking of gas oil. Compared with those conventional methods, for example, quadratic programming, the method proposed in this paper is more effective and feasible. With the parameters selected from the NSGA II pareto-optimal solutions, more accurate results can be obtained.


Sequential Quadratic Programming Nondominated Sorting Nondominated Sorting Genetic Algorithm Nondominated Front Dynamic Differential Equation 
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  1. 1.
    Jiang, A.P., Shao, Z.J., Qian, J.X.: Optimization of Reaction Parameters Based on rSQP and Hybrid Automatic Differentiation Algorithm. Journal of Zhejiang University (Engineering Science) 38, 1606–1610 (2004)Google Scholar
  2. 2.
    Tjoa, I.-B., Biegler, L.T.: Simultaneous Solution and Optimization Strategies for Parameter Estimation of Differential-algebraic Equations Systems. Ind. Eng. Chem. Res. 30, 376–385 (1991)CrossRefGoogle Scholar
  3. 3.
    Srinivas, N., Deb, K.: Multiobjective Function Optimization Using Nondominated Sorting Genetic Algorithms [J]. Evolutionary Computation 2(3), 221–248 (1995)CrossRefGoogle Scholar
  4. 4.
    Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning [M]. Addison-Wesley, Reading (1989)Google Scholar
  5. 5.
    Deb, K., Agrawal, S., Pratap, A., et al.: A Fast Elitist Nondominated Sorting Genetic Algorithm For Multi-objective Optimization: NSGA II [A]. In: Proc of the Parallel Problem Solving from Nature VI Conf. [C], Paris, pp. 849–858 (2000)Google Scholar
  6. 6.
    Xu, S.L.: Common Algorithm Set by FORTRAN. Tsinghua University Press (1995)Google Scholar
  7. 7.
    Froment, G.F., Bischoff, K.B.: Chemical Reactor Analysis and Design. Wiley, New York (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yingzi Shi
    • 1
  • Jiangang Lu
    • 2
  • Qiang Zheng
    • 2
  1. 1.School of Education ScienceHangzhou Teachers CollegeHangzhouChina
  2. 2.National Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouChina

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