scholarly journals Particle Swarm Optimization and Shuffle Complex Evolution for Calibrating Xinanjiang Model Parameters

2013 ◽  
Vol 10 (10) ◽  
pp. 2036-2048
Author(s):  
OKelvin Kuok ◽  
Po Chan Chiu
Keyword(s):  
Particle Swarm Optimization ◽  
Model Calibration ◽  
Hydrological Model ◽  
Particle Swarm ◽  
Daily Rainfall ◽  
Model Parameters ◽  
Hydrological Models ◽  
Swarm Optimization ◽  
Xinanjiang Model ◽  
Conceptual Hydrological Model

Xinanjiang model, a conceptual hydrological model, is well known and widely used in China since 1970s. Xinanjiang model consists of large number of parameters that cannot be directly obtained from measurable quantities of catchment characteristics, but only through model calibration. Parameter optimization is a significant but time-consuming process that is inherent in conceptual hydrological models representing rainfall–runoff processes. This study presents newly developed Particle Swarm Optimization (PSO) and compared with famous Shuffle Complex Evolution (SCE) to auto-calibrate Xinanjiang model parameters. The selected study area is Bedup Basin, located at Samarahan Division, Sarawak, Malaysia. Input data used for model calibration are daily rainfall data Year 2001, and validated with data Year 1990, 1992, 2000, 2002 and 2003. Simulation results are measured with Coefficient of Correlation (R) and Nash-Sutcliffe coefficient (E2). Results show that the performance of PSO is comparable with the famous SCE algorithm. For model calibration, the best R andE2 obtained are 0.775 and 0.715 respectively, compared to R=0.664 and E2=0.677 for SCE. For model validation, the average R=0.859 and average E2=0.892 are obtained for PSO, compared to average R=0.572 and average E2 =0.631 obtained for SCE. 

scholarly journals Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Michala Jakubcová ◽  
Petr Máca ◽  
Pavel Pech
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Problems ◽  
Hydrological Model ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Model Parameters ◽  
Estimation Of Parameters ◽  
Swarm Optimization

The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO) algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

scholarly journals Short-term power load forecasting based on support vector machine and particle swarm optimization

2018 ◽  
Vol 13 ◽  
pp. 174830181879706 ◽  
Author(s):  
Song Qiang ◽  
Yang Pu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Influencing Factors ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Model Parameters ◽  
Short Term ◽  
Swarm Optimization

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the “abnormal data” in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.

scholarly journals Simultaneous Identification of Model Structure and the Associated Parameters for Linear Systems Based on Particle Swarm Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Semin Chun ◽  
Tae-Hyoung Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Linear Systems ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Model Parameters ◽  
Model Structure ◽  
Optimal Model ◽  
Swarm Optimization ◽  
Optimal Dimension ◽  
Simultaneous Identification

In this study, a novel easy-to-use meta-heuristic method for simultaneous identification of model structure and the associated parameters for linear systems is developed. This is achieved via a constrained multidimensional particle swarm optimization (PSO) mechanism developed by hybridizing two main methodologies: one for negating the limit for fixing the particle’s dimensions within the PSO process and another for enhancing the exploration ability of the particles by adopting a cyclic neighborhood topology of the swarm. This optimizer consecutively searches the dimensional optimum of particles and then the positional optimum in the search space, whose dimension is specified by the explored optimal dimension. The dimensional optimum provides the optimal model structure, while the positional optimum provides the optimal model parameters. Typical numerical examples are considered for evaluation purposes, which clearly demonstrate that the proposed PSO scheme provides novel and powerful impetus with remarkable reliability toward simultaneous identification of model structure and unknown model parameters. Furthermore, identification experiments are conducted on a magnetic levitation system and a robotic manipulator with joint flexibility to demonstrate the effectiveness of the proposed strategy in practical applications.

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