Automatic Calibration of Conceptual Rainfall-Runoff Models

2013 ◽  
Vol 405-408 ◽  
pp. 2185-2189 ◽  
Author(s):  
Chao Zhang ◽  
Ying Ying Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Historical Data ◽  
Flood Forecasting ◽  
Parameters Optimization ◽  
Forecasting Model ◽  
Automatic Calibration ◽  
Rainfall Runoff ◽  
Objective Functions ◽  
Swarm Optimization ◽  
River Watershed

The Particle Swarm Optimization (PSO) method was used to calibrate the Xinanjiang (XAJ) conceptual rainfall-runoff flood forecasting model, using a 7-year record of historical data of Yandu river watershed. Based on results of calibration runs using different objective functions, it is concluded that parameters optimization has a certain relationship with the choice of objective functions and the results vary with different functions. The simulation and prediction results were reasonable, as PSO method was used in XAJ model with observed data of Yandu river, combined with layer-debugging theory of Zhao Ren-jun.

Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R767-R781 ◽  
Author(s):  
Mattia Aleardi ◽  
Silvio Pierini ◽  
Angelo Sajeva
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Model Space ◽  
Global Search ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Fireworks Algorithm ◽  
Highly Nonlinear ◽  
Whale Optimization

We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular, we are interested in assessing the exploration and exploitation capabilities of each method as the dimension of the model space increases. First, we test the different algorithms on two multiminima and two convex analytic objective functions. Then, we compare them using the residual statics corrections and 1D elastic full-waveform inversion, which are highly nonlinear geophysical optimization problems. Our results demonstrate that FA, FWA, and WOA are characterized by optimal exploration capabilities because they outperform the other approaches in the case of optimization problems with multiminima objective functions. Differently, QPSO and PSO have good exploitation capabilities because they easily solve ill-conditioned optimizations characterized by a nearly flat valley in the objective function. QPSO, PSO, and WCA offer a good compromise between exploitation and exploration.

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

On Global Convergence in Design Optimization Using the Particle Swarm Optimization Technique

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Forrest W. Flocker ◽  
Ramiro H. Bravo
Keyword(s):  
Particle Swarm Optimization ◽  
Global Minimum ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Objective Functions ◽  
Convergence Criteria ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique

The particle swarm optimization (PSO) method is becoming a popular optimizer within the mechanical design community because of its simplicity and ability to handle a wide variety of objective functions that characterize a proposed design. Typical examples arising in mechanical design are nonlinear objective functions with many constraints, which typically arise from the various design specifications. The method is particularly attractive to mechanical design because it can handle discontinuous functions that occur when the designer must choose from a discrete set of standard sizes. However, as in other optimizers, the method is susceptible to converging to a local rather than global minimum. In this paper, convergence criteria for the PSO method are investigated and an algorithm is proposed that gives the user a high degree of confidence in finding the global minimum. The proposed algorithm is tested against five benchmark optimization problems, and the results are used to develop specific guidelines for implementation.

A Hybrid Forecasting Model for Non-Stationary Time Series

2012 ◽  
Vol 3 (2) ◽  
pp. 67-82 ◽  
Author(s):  
Yi Xiao ◽  
Jin Xiao ◽  
Shouyang Wang
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Stationary Time Series ◽  
Adaptive Mutation ◽  
Forecasting Model ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Individual Fitness ◽  
Hybrid Forecasting

In time series analysis, an important problem is how to extract the information hidden in the non-stationary and noise data and combine it into a model for forecasting. In this paper, the authors propose a TEI@I based hybrid forecasting model. A novel feed forward neural network is developed based on the improved particle swarm optimization with adaptive genetic operator (IPSO-FNN) for forecasting. In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Subsequently, a crossover rate which only depends on generation and an adaptive mutation rate based on individual fitness are designed. The parameters of FNN are optimized by binary and decimal particle swarm optimization. Further, the forecast results of IPSO-FNN are adjusted with the knowledge from text mining and an expert system. The empirical results on the container throughput forecast of Tianjin Port show that forecasts with the proposed method are much better than some other methods.

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