AEA combined with differential evolution and its application on parameter estimation

被引：0

作者：

He, Pengfei ^{[1
]}

Li, Shaojun ^{[1
]}

机构：

[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai

来源：

Li, Shaojun | 1600年 / Materials China卷 / 65期

基金：

中国国家自然科学基金;

关键词：

AEA; Alopex; Differential evolution; Optimization; Parameter estimation;

D O I：

10.3969/j.issn.0438-1157.2014.12.029

中图分类号：

学科分类号：

摘要：

Focusing on the demerits of AEA (Alopex-based evolutionary algorithm), this paper proposed a modified AEA algorithm (MAEA), which was fused AEA with differential evolution (DE). In order to enhance the performance of the algorithm, the generation method of population in AEA was improved by applying an improved DE operation to AEA. The modified algorithm not only takes advantage of heuristic search and deterministic search of AEA, but also increases the population diversity and is adapted to global search and local search. Then the MAEA algorithm was tested by 21 benchmark functions,the results show that MAEA outperforms DE, MDE and AEA. Further comparison results between MAEA algorithm and a representative state-of-the-art algorithm (ISDEMS) indicate that, the performance of the modified algorithm is significantly improved, in both accuracy and stability. Furthermore, the algorithm was applied to the parameter estimation of the models of fermentation dynamics, and the satisfactory results were obtained. ©, 2014, Chemical Industry Press. All right reserved.

引用

页码：4857 / 4865

页数：8

共 21 条

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