A new improved Alopex-based evolutionary algorithm and its application to parameter estimation  

作　　者：桑志祥 李绍军 董跃华 

机构地区：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education,(East China University of Science and Technology)

出　　处：《Journal of Central South University》2013年第1期123-133,共11页中南大学学报（英文版）

基　　金：Projects(20976048, 21176072) supported by the National Natural Science Foundation of China;Project provided by the Fundamental Research Fund for Central Universities

摘　　要：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.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.

关 键 词：ALOPEX evolutionary algorithm Alopex-based evolutionary algorithm clone selection parameter estimation 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]