Jian-Qiang Li

Chaotic Bayesian Method Based on Multiple Criteria Decision making (MCDM) for Forecasting Nonlinear Hydrological Time Series

To improve the precision and decrease the uncertainty in forecasting nonlinear hydrological time series, a novel chaotic Bayesian method based on multiple criteria decision making (CBMMCDM) is proposed, in \\hich chaotic forecast model of the add-\vcightcd one-rank local-region method (AOLM) is improved by embedding self-learning technique of Bayesian processor of forecast (BPF). In addition, we give the optimal embedding dimension by use of MCDM theory for global parameter decision in CBMMCDM. So as to test the effect of CBMMCDM, the daily runoffs arPanjiakou and Sandaohexi in Luanhc basin are considered. The results of the phase-space reconstruction indicate that both of the above two daily runoffs are chaotic series and their optimal embedding dimensions are both 3 with the four assessment indices of mean relative error (MRE), root mean square error (RMSE), modified coefficient of efficiency (MCE) and Bayesian correlation score (BCS). Compared with the results of AOLM, CBMMCDM can improve the forecast accuracy of daily runoffs. Especially relative errors also decrease in forecasting the maximum daily runoff values in both stations. This new forecast method is an extension to chaos prediction method.

Chaotic Bayesian optimal prediction method and its application in hydrological time series

The embedding dimension and the number of nearest neighbors are very important parameters in the prediction of chaotic time series. To reduce the prediction errors and the uncertainties in the determination of the above parameters, a new chaos Bayesian optimal prediction method (CBOPM) is proposed by choosing optimal parameters in the local linear prediction method (LLPM) and improving the prediction accuracy with Bayesian theory. In the new method, the embedding dimension and the number of nearest neighbors are combined as a parameter set. The optimal parameters are selected by mean relative error (MRE) and correlation coefficient (CC) indices according to optimization criteria. Real hydrological time series are taken to examine the new method. The prediction results indicate that CBOPM can choose the optimal parameters adaptively in the prediction process. Compared with several LLPM models, the CBOPM has higher prediction accuracy in predicting hydrological time series.

DNA accelerating evolutionary algorithm and its application in the parameter optimization of Muskingum routing model

In order to reduce the computational load and improve the computational accuracy for parameter optimization of Muskingum routing model, a new algorithm, DNA accelerating evolutionary algorithm (DNAAEA) is proposed. With the shrinking of searching range, the method gradually directs to optimal result with the excellent individuals obtained by DNA evolutionary algorithm. Its global convergence is analyzed. Its efficiency is verified by application of Muskingum routing model. Compared with standard binary-encoded genetic algorithm (SGA), real-valued accelerating genetic algorithm (RAGA), least residual square algorithm (LRSM) and the test method (TM), DNAAEA has higher precision and rapider convergent speed. It is good for the global optimization in the practical water environmental models.

Refined gray-encoded evolution algorithm for parameter optimization in convection-diffusion equations

Purpose – The purpose of this paper is to reduce the computational burden and improve the precision of the parameter optimization in the convection-diffusion equation, a new algorithm, the refined gray-encoded evolution algorithm (RGEA), is proposed. Design/methodology/approach – In the new algorithm, the differential evolution algorithm (DEA) is introduced to refine the solutions and to improve the search efficiency in the evolution process; the rapid cycle operation is also introduced to accelerate the convergence rate. The authors apply this algorithm to parameter optimization in convection-diffusion equations. Findings – Two cases for parameter optimization in convection-diffusion equations are studied by using the new algorithm. The results indicate that the sum of absolute errors by the RGEA decreases from 74.14 to 99.29 percent and from 99.32 to 99.98 percent, respectively, compared to those by the gray-encoded genetic algorithm (GGA) and the DEA. And the RGEA has a faster convergent speed than doe...

Chaos DNA Hooke-Jeeves evolutionary algorithm and its application in river pollution control

In this paper, chaos DNA Hooke-Jeeves evolutionary algorithm (CDNAHJEA) is proposed to reduce computational amount and to improve the calculation precision for river pollution control programming, in which initial population are generated by chaos mapping, and new DNA mutation operation and Hooke-Jeeves evolution operation are used in the CDNAHJEA. Its convergence theorem is given. Compared with improved real-coded genetic algorithm, Hooke-Jeeves algorithm, Gray-encoded hybrid-accelerated genetic algorithm and LINGO method, CDNAHJEA has rapider convergent speed and higher calculation precision in solving the given practical river pollution control programming problem.