A new improved Alopex-based evolutionary algorithm and its application to parameter estimation
来源期刊：中南大学学报(英文版)2013年第1期
论文作者：SANG Zhi-xiang(桑志祥) LI Shao-jun(李绍军) DONG Yue-hua(董跃华)
文章页码：123 - 133
Key words：alopex; evolutionary algorithm; alopex-based evolutionary algorithm; clone selection; parameter estimation
Abstract: In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures’ results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.
SANG Zhi-xiang(桑志祥), LI Shao-jun(李绍军), DONG Yue-hua(董跃华)
(Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education,
(East China University of Science and Technology), Shanghai 200237, China)
Abstract:In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures’ results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.
Key words:alopex; evolutionary algorithm; alopex-based evolutionary algorithm; clone selection; parameter estimation
