Identification of water quality model parameters using artificial bee colony algorithm

Guangzhou Chen; Guijian Liu; Jiaquan Wang; Ruzhong Li

doi:10.3934/naco.2012.2.157

Article Contents

2012, Volume 2, Issue 1: 157-165. Doi: 10.3934/naco.2012.2.157

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Identification of water quality model parameters using artificial bee colony algorithm

1.
Department of Environmental Engineering, Anhui University of Architecture, No. 856, JinZhai South Road, Hefei 230022
2.
School of Earth and Space Sciences, University of Science and Technology of China, No. 96, JinZhai Road, Hefei 230026
3.
School of Resources and Environmental Engineering, Hefei University of Technology, No. 193, TunXi Road, Hefei 230009

Received: February 2011

Revised: October 2011

Published: March 2012

Abstract / Introduction Related Papers Cited by

Abstract

As prime data of water quality model, water quality parameters are of importance to forecast the situation of water quality correctly. Therefore, it is a key to identify them correctly. Aimed at the parameter identification problem, it can be transformed into an optimization problem by constructing objective function that minimizes simulation errors. In this study, a novel swarm intelligence optimization algorithm-artificial bee colony algorithm was used. In the experiment, many tests were done under the various ranges of parameters, and each variable was optimized according to its own reasonable scope. In addition, the optimization effect was compared based on the two methods producing new solutions in the neighborhood. As a key parameter of the algorithm, the impact of limit value on the algorithm performance was analyzed in detail under various values. Finally, two examples were analyzed and their computation results were compared with that of artificial fish swarm algorithm, simulated annealing and genetic algorithm. The results show that artificial bee colony algorithm has good adaptability to various ranges of parameters and better optimization precision. Moreover, it needs few control parameters of algorithm. So it is an effective parameter identification method.

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Mathematics Subject Classification: Primary: 65K99; Secondary: 90C31.

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