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Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 2132963, 11 pages
Research Article

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

1School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China
2Department of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

Correspondence should be addressed to He Wang

Received 31 July 2017; Revised 9 November 2017; Accepted 4 January 2018; Published 31 January 2018

Academic Editor: Stefan Balint

Copyright © 2018 He Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.