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Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems 被引量:1

Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems
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摘要 Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the con- vergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model oarameters for a comolex mathematical model. Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.
出处 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2013年第2期155-162,共8页 中国化学工程学报(英文版)
基金 Supported by the National Natural Science Foundation of China (60804027, 61064003) and Fuzhou University Research Foundation (FZU-02335, 600338 and 600567).
关键词 parameter estimation kinetic model hybrid differential evolution Gauss-Newton feed batch fermentor 差分进化算法 反应动力学参数 估计问题 混合方法 生化系统 动力学模型参数 全局搜索能力 迭代过程
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