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    叶贞成, 王鑫, 梅华. 基于改进差分进化算法的裂解反应动力学系数辨识[J]. 华东理工大学学报(自然科学版), 2020, 46(6): 770-779. DOI: 10.14135/j.cnki.1006-3080.20190910002
    引用本文: 叶贞成, 王鑫, 梅华. 基于改进差分进化算法的裂解反应动力学系数辨识[J]. 华东理工大学学报(自然科学版), 2020, 46(6): 770-779. DOI: 10.14135/j.cnki.1006-3080.20190910002
    YE Zhencheng, WANG Xin, MEI Hua. Kinetic Parameter Identification of Cracking Reaction Based on Improved Differential Evolution Algorithm[J]. Journal of East China University of Science and Technology, 2020, 46(6): 770-779. DOI: 10.14135/j.cnki.1006-3080.20190910002
    Citation: YE Zhencheng, WANG Xin, MEI Hua. Kinetic Parameter Identification of Cracking Reaction Based on Improved Differential Evolution Algorithm[J]. Journal of East China University of Science and Technology, 2020, 46(6): 770-779. DOI: 10.14135/j.cnki.1006-3080.20190910002

    基于改进差分进化算法的裂解反应动力学系数辨识

    Kinetic Parameter Identification of Cracking Reaction Based on Improved Differential Evolution Algorithm

    • 摘要: Kumar模型一次反应方程选择性系数辨识问题具有非线性强、维度高、计算时间长、要求精度高等特点,标准差分进化算法(DE)在解决该问题时易早熟、陷入局部最小。针对这一问题,提出了一种新的分类变异参数自适应差分进化(Classification Variation Parameter Adaptive Differential Evolution, CVPADE)算法。结合分类变异策略与改进的控制参数自适应进化策略,使适应度较好的优秀个体着重增加局部搜索能力,适应度较差的个体增强全局搜索能力,同时加快趋向最优解的收敛速度,加快了种群整体收敛速度,并平衡了种群开发能力。与DE算法、控制参数自适应差分进化算法(jDE)进行对比,结果表明本文算法的耗时相对DE算法和jDE算法分别降低了40%与35%,证明了该算法对Kumar模型一次选择性系数的辨识问题具有显著的改进效果。

       

      Abstract: Traditional optimization methods, such as simplex method, steepest descent method, and conjugate gradient method, have the advantages of high computational efficiency, strong reliability, and mature theory. However, it is difficult for them to solve the optimization problem without mathematical model. Moreover, for many high-dimensional optimization problems, they easily fall into the local minimum. Due to the characteristics of strong nonlinearity, high dimension, long computation time, and high precision, the parameters identification problem of the first-order reaction equation in Kumar model cannot be solved effectively by means of traditional optimization algorithm. In recent years, various stochastic optimization algorithms such as genetic algorithm and differential evolution algorithm have rapidly developed. Among them, differential evolution algorithm(DE) shows the strong ability to nonlinear optimization problem, due to its advantages of fast convergence, few control parameters, and simple parameter setting principle. Nevertheless, the standard DE algorithm and its improved algorithm have some shortcomings, e.g., slower convergence speed, easy falling into the local minimum, etc. Aiming at the above problems, this paper proposes a new classification variation parameter adaptive differential evolution algorithm (CVPADE). By combining the classification variation strategy with the control parameter adaptive evolution strategy, CVPADE algorithm can not only increase the convergence speed, but also avoid the shortcoming that the population diversity is not rich enough due to the small population size. Besides, the adaptive evolution strategy of parameters is controlled to make the algorithm more stable and avoid falling into local minimization. It can effectively solve the selectivity coefficient identification problem of Kumar model primary equation. Finally, the comparison with the standard differential evolution algorithm and the control parameter adaptive differential evolution algorithm (jDE) are made, showing that the time consumption of the proposed algorithm in this paper is 40% and 35% lower than that of DE and jDE, respectively. Hence, the proposed algorithm has a significant improvement effect on the parameter identification of the first-order reaction equation in Kumar model.

       

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