American Institute of Mathematical Sciences

Advanced
  • Previous Article
    A DC programming approach for a class of bilevel programming problems and its application in Portfolio Selection
  • NACO Home
  • This Issue
  • Next Article
    A class of smoothing SAA methods for a stochastic linear complementarity problem
2012, 2(1): 157-165. doi: 10.3934/naco.2012.2.157

Identification of water quality model parameters using artificial bee colony algorithm

Guangzhou Chen 1, , Guijian Liu 2, , Jiaquan Wang 3, and  Ruzhong Li 3,

1. 

Department of Environmental Engineering, Anhui University of Architecture, No. 856, JinZhai South Road, Hefei 230022, China
2. 

School of Earth and Space Sciences, University of Science and Technology of China, No. 96, JinZhai Road, Hefei 230026, China
3. 

School of Resources and Environmental Engineering, Hefei University of Technology, No. 193, TunXi Road, Hefei 230009, China, China

Received  February 2011 Revised  October 2011 Published  March 2012

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.
Keywords: artificial bee colony algorithm, Parameter identification, water quality model, global optimization..
Mathematics Subject Classification: Primary: 65K99; Secondary: 90C3.
Citation: Guangzhou Chen, Guijian Liu, Jiaquan Wang, Ruzhong Li. Identification of water quality model parameters using artificial bee colony algorithm. Numerical Algebra, Control & Optimization, 2012, 2 (1) : 157-165. doi: 10.3934/naco.2012.2.157
References:
[1]

G. Z. Chen, X. C. Xu and J. Q. Wang, Application of a modified artificial fish swarm algorithm to identification of water quality parameters,, Journal of Hydroelectric Engineering, 29 (2010), 108.   Google Scholar

[2]

G. W. Fu, "River Water Quality Model and Simulation Computation,", 1st edition, (1987).   Google Scholar

[3]

J. Q. Guo, Y. Li and H. S. Wang, Chaotic optimization for parameter estimation of water quality model of river,, Journal of Hydroelectric Engineering, 15 (2004), 95.   Google Scholar

[4]

J. Q. Guo, Y. Li and H. S. Wang, Application of particle swarm optimization algorithms to determination of water quality parameters of river streams,, Advances in Science and Technology of Water Resources, 27 (2007), 1.   Google Scholar

[5]

F. Kang, J. J. Li and Q. Xu, Improved artificial bee colony algorithm and its application in back analysis,, Water Resources and Power, 27 (2009), 126.   Google Scholar

[6]

D. Karaboga, An idea based on bee swarm for numerical optimization [R],, Technical Report-TR06, (2005).   Google Scholar

[7]

Y. H. Jia, Determination of water quality parameter based on genetic algorithm,, Haihe Water Resources, 1 (2008), 41.   Google Scholar

[8]

D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) Algorithm,, Journal of Global Optimization, 39 (2007), 459.  doi: 10.1007/s10898-007-9149-x.  Google Scholar

[9]

D. Karaboga and B. Basturk, On the performance of artificial bee colony (ABC) algorithm,, Applied Soft Computing, 8 (2008), 687.  doi: 10.1016/j.asoc.2007.05.007.  Google Scholar

[10]

D. Karaboga, Artificial bee colony algorithm,, Scholarpedia, 5 (2010).  doi: 10.4249/scholarpedia.6915.  Google Scholar

[11]

L. Q. Meng and J. Q. Guo, Application of chaos particle swarm optimization algorithm to determination of water quality parameter of river steam,, Journal of Earth Sciences and Environment, 31 (2009), 169.   Google Scholar

[12]

J. P. Wang and S. T. Cheng, Application of genetic algorithm and simplex method in parameter identification of complicated environmental model,, Journal of Hydraulic Engineering, 36 (2005), 674.   Google Scholar

[13]

W. Wang, G. M. Zeng and L. He, Estimation of water quality model parameters with simulated annealing algorithm,, Shui Li Xue Bao, 6 (2004), 61.   Google Scholar

[14]

X. H. Yang, Z. F. Yang and J. Q. Li, A new method for parameter identification in water environment model,, Advances in Water Science, 14 (2003), 554.   Google Scholar

[15]

Y. J. Zhang, J. Q. Guo and H. S. Wang, Chaotic-Annealing algorithm for parameter identification of river water quality model,, China Rural Water and Hydropower, 1 (2006), 38.   Google Scholar

[16]

S. Zhu, G. H. Mao and G. H. Liu, Parameters identification of river water quality model based on finite volume method-hybrid genetic algorithm,, Journal of Hydroelectric Engineering, 26 (2007), 91.   Google Scholar

show all references

References:
[1]

G. Z. Chen, X. C. Xu and J. Q. Wang, Application of a modified artificial fish swarm algorithm to identification of water quality parameters,, Journal of Hydroelectric Engineering, 29 (2010), 108.   Google Scholar

[2]

G. W. Fu, "River Water Quality Model and Simulation Computation,", 1st edition, (1987).   Google Scholar

[3]

J. Q. Guo, Y. Li and H. S. Wang, Chaotic optimization for parameter estimation of water quality model of river,, Journal of Hydroelectric Engineering, 15 (2004), 95.   Google Scholar

[4]

J. Q. Guo, Y. Li and H. S. Wang, Application of particle swarm optimization algorithms to determination of water quality parameters of river streams,, Advances in Science and Technology of Water Resources, 27 (2007), 1.   Google Scholar

[5]

F. Kang, J. J. Li and Q. Xu, Improved artificial bee colony algorithm and its application in back analysis,, Water Resources and Power, 27 (2009), 126.   Google Scholar

[6]

D. Karaboga, An idea based on bee swarm for numerical optimization [R],, Technical Report-TR06, (2005).   Google Scholar

[7]

Y. H. Jia, Determination of water quality parameter based on genetic algorithm,, Haihe Water Resources, 1 (2008), 41.   Google Scholar

[8]

D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) Algorithm,, Journal of Global Optimization, 39 (2007), 459.  doi: 10.1007/s10898-007-9149-x.  Google Scholar

[9]

D. Karaboga and B. Basturk, On the performance of artificial bee colony (ABC) algorithm,, Applied Soft Computing, 8 (2008), 687.  doi: 10.1016/j.asoc.2007.05.007.  Google Scholar

[10]

D. Karaboga, Artificial bee colony algorithm,, Scholarpedia, 5 (2010).  doi: 10.4249/scholarpedia.6915.  Google Scholar

[11]

L. Q. Meng and J. Q. Guo, Application of chaos particle swarm optimization algorithm to determination of water quality parameter of river steam,, Journal of Earth Sciences and Environment, 31 (2009), 169.   Google Scholar

[12]

J. P. Wang and S. T. Cheng, Application of genetic algorithm and simplex method in parameter identification of complicated environmental model,, Journal of Hydraulic Engineering, 36 (2005), 674.   Google Scholar

[13]

W. Wang, G. M. Zeng and L. He, Estimation of water quality model parameters with simulated annealing algorithm,, Shui Li Xue Bao, 6 (2004), 61.   Google Scholar

[14]

X. H. Yang, Z. F. Yang and J. Q. Li, A new method for parameter identification in water environment model,, Advances in Water Science, 14 (2003), 554.   Google Scholar

[15]

Y. J. Zhang, J. Q. Guo and H. S. Wang, Chaotic-Annealing algorithm for parameter identification of river water quality model,, China Rural Water and Hydropower, 1 (2006), 38.   Google Scholar

[16]

S. Zhu, G. H. Mao and G. H. Liu, Parameters identification of river water quality model based on finite volume method-hybrid genetic algorithm,, Journal of Hydroelectric Engineering, 26 (2007), 91.   Google Scholar

[1]

Harish Garg. Solving structural engineering design optimization problems using an artificial bee colony algorithm. Journal of Industrial & Management Optimization, 2014, 10 (3) : 777-794. doi: 10.3934/jimo.2014.10.777
[2]

Miao Yu. A solution of TSP based on the ant colony algorithm improved by particle swarm optimization. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 979-987. doi: 10.3934/dcdss.2019066
[3]

Yuepeng Wang, Yue Cheng, I. Michael Navon, Yuanhong Guan. Parameter identification techniques applied to an environmental pollution model. Journal of Industrial & Management Optimization, 2018, 14 (2) : 817-831. doi: 10.3934/jimo.2017077
[4]

Pankaj Kumar Tiwari, Rajesh Kumar Singh, Subhas Khajanchi, Yun Kang, Arvind Kumar Misra. A mathematical model to restore water quality in urban lakes using Phoslock. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020223
[5]

Jean-Paul Arnaout, Georges Arnaout, John El Khoury. Simulation and optimization of ant colony optimization algorithm for the stochastic uncapacitated location-allocation problem. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1215-1225. doi: 10.3934/jimo.2016.12.1215
[6]

Aurea Martínez, Francisco J. Fernández, Lino J. Alvarez-Vázquez. Water artificial circulation for eutrophication control. Mathematical Control & Related Fields, 2018, 8 (1) : 277-313. doi: 10.3934/mcrf.2018012
[7]

Paul B. Hermanns, Nguyen Van Thoai. Global optimization algorithm for solving bilevel programming problems with quadratic lower levels. Journal of Industrial & Management Optimization, 2010, 6 (1) : 177-196. doi: 10.3934/jimo.2010.6.177
[8]

Chunlin Hao, Xinwei Liu. Global convergence of an SQP algorithm for nonlinear optimization with overdetermined constraints. Numerical Algebra, Control & Optimization, 2012, 2 (1) : 19-29. doi: 10.3934/naco.2012.2.19
[9]

Roya Soltani, Seyed Jafar Sadjadi, Mona Rahnama. Artificial intelligence combined with nonlinear optimization techniques and their application for yield curve optimization. Journal of Industrial & Management Optimization, 2017, 13 (4) : 1701-1721. doi: 10.3934/jimo.2017014
[10]

Pingping Niu, Shuai Lu, Jin Cheng. On periodic parameter identification in stochastic differential equations. Inverse Problems & Imaging, 2019, 13 (3) : 513-543. doi: 10.3934/ipi.2019025
[11]

Daniel Guo, John Drake. A global semi-Lagrangian spectral model for the reformulated shallow water equations. Conference Publications, 2003, 2003 (Special) : 375-385. doi: 10.3934/proc.2003.2003.375
[12]

Xin Zhang, Jie Wen, Qin Ni. Subspace trust-region algorithm with conic model for unconstrained optimization. Numerical Algebra, Control & Optimization, 2013, 3 (2) : 223-234. doi: 10.3934/naco.2013.3.223
[13]

Veysel Fuat Hatipoğlu. A novel model for the contamination of a system of three artificial lakes. Discrete & Continuous Dynamical Systems - S, 2019  doi: 10.3934/dcdss.2020176
[14]

Qinqin Chai, Ryan Loxton, Kok Lay Teo, Chunhua Yang. A unified parameter identification method for nonlinear time-delay systems. Journal of Industrial & Management Optimization, 2013, 9 (2) : 471-486. doi: 10.3934/jimo.2013.9.471
[15]

Qiang Long, Changzhi Wu. A hybrid method combining genetic algorithm and Hooke-Jeeves method for constrained global optimization. Journal of Industrial & Management Optimization, 2014, 10 (4) : 1279-1296. doi: 10.3934/jimo.2014.10.1279
[16]

Simai He, Min Li, Shuzhong Zhang, Zhi-Quan Luo. A nonconvergent example for the iterative water-filling algorithm. Numerical Algebra, Control & Optimization, 2011, 1 (1) : 147-150. doi: 10.3934/naco.2011.1.147
[17]

Ruizhao Zi. Global solution in critical spaces to the compressible Oldroyd-B model with non-small coupling parameter. Discrete & Continuous Dynamical Systems - A, 2017, 37 (12) : 6437-6470. doi: 10.3934/dcds.2017279
[18]

Daniel Guo, John Drake. A global semi-Lagrangian spectral model of shallow water equations with time-dependent variable resolution. Conference Publications, 2005, 2005 (Special) : 355-364. doi: 10.3934/proc.2005.2005.355
[19]

Jiao-Yan Li, Xiao Hu, Zhong Wan. An integrated bi-objective optimization model and improved genetic algorithm for vehicle routing problems with temporal and spatial constraints. Journal of Industrial & Management Optimization, 2020, 16 (3) : 1203-1220. doi: 10.3934/jimo.2018200
[20]

R. S. Johnson. A selection of nonlinear problems in water waves, analysed by perturbation-parameter techniques. Communications on Pure & Applied Analysis, 2012, 11 (4) : 1497-1522. doi: 10.3934/cpaa.2012.11.1497

 Impact Factor: 

Tools

Metrics

  • PDF downloads (42)
  • HTML views (0)
  • Cited by (1)

Other articles
by authors

[Back to Top]

Copyright © 2020 American Institute of Mathematical Sciences