Using an Improved Artificial Bee Colony Algorithm for Parameter Estimation of a Dynamic Grain Flow Model

被引：3
|
作者：
Wang, He [1 ]
Liang, Hongbin [1 ]
Gao, Lei [2 ]
机构：
[1] Univ Sci & Technol Liaoning, Sch Mech Engn & Automat, Anshan 114051, Peoples R China
[2] Chinese Acad Sci, Shenyang Inst Automat, Dept Informat Serv & Intelligent Control, Shenyang 110016, Liaoning, Peoples R China
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2018年 / 2018卷
关键词：
DESIGN; SENSOR; PERFORMANCE;
D O I：
10.1155/2018/2132963
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
An effective method is proposed to estimate the parameters of a dynamic grain flow model (DGFM). To this end, an improved artificial bee colony (IABC) algorithm is used to estimate unknown parameters of DGFM with minimizing a given objective function. A comparative study of the performance of the IABC algorithm and the other ABC variants on several benchmark functions is carried out, and the results present a significant improvement in performance over the other ABC variants. The practical application performance of the IABC is compared to that of the nonlinear least squares (NLS), particle swarm optimization (PSO), and genetic algorithm (GA). The compared results demonstrate that IABC algorithm is more accurate and effective for the parameter estimation of DGFM than the other algorithms.
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收藏
页数：11
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