Calibrating RZWQM2 model using quantum-behaved particle swarm optimization algorithm

Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochastic time series. Roller bearings are working at high speed in a heavy load environment so that the combination of bearing faults gradually degraded during the rotation might lead to unpredicted catastrophic accidents. The degradation process has the property of long-range dependence (LRD), so that the fractional Brownian motion (fBm) is taken into account for a prediction model. Because of the dramatic changes in the bearing degradation process, the Hurst exponent that describes the fBm will change during the degradation process. A priori Hurst value of the conventional fBm in the prediction is fixed, thus inducing a minor accuracy of the prediction. To avoid this problem, we propose an improved prediction method. Based on the following steps, at the initial data processing, a skip-over factor is selected as the characteristics parameter of the bearing degradation process. A multifractional Brownian motion (mfBm) replaces the fBm for the degradation modeling. We will show that also our mfBm has the same property of long-range dependence as the fBm. Moreover, a time-varying Hurst exponent H(t) is taken to replace the constant H in fBm. Finally, we apply the quantum-behaved partial swarm optimization (QPSO) to optimize H(t) for a finite interval. Some tests and corresponding experimental results will show that our model QPSO + mfBm have a much better performance on the prediction effect than fBm.

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“…where L i,j (t) is the standard deviation of the distribution. e position of each particle can be obtained by [38]…”

Section: Quantum-behaved Particle Swarm For the Hurst Exponent Optimimentioning

confidence: 99%

Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

et al. 2020

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“…A longer iteration is likely to produce a better optimization result. The QPSO algorithm, due to its strong global convergence, has been applied to many studies, e.g., Davoodi et al (2014), Xi et al (2015), and Hassani and Lee (2016). A review on its application was summarized in Fang et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization

Ricciuto

Stoyanov

et al. 2018

J Adv Model Earth Syst

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Calibration of the Energy Exascale Earth System Model (E3SM), land model (ELMv0) is challenging because of its model complexity, strong model nonlinearity, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near‐optimal solution within an affordable time. The goal of this study is to calibrate some of the ELMv0 parameters to improve model projection of carbon fluxes. We propose a computationally efficient global optimization procedure using sparse‐grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the ELMv0, and then calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated a sufficiently large number of times in the optimization, which facilitates the global search. We calibrate eight parameters against five years of net ecosystem exchange, total leaf area index, and latent heat flux data from the U.S. Missouri Ozark flux tower. The calibrated model is then used for predicting the three variables in the following 4 years. The results indicate that an accurate surrogate model can be created for the ELMv0 with a relatively small number of SG points, i.e., a few ELMv0 simulations that can be fully parallel. And, the application of the optimized parameters leads to a better model performance and a higher predictive capability than the default parameter values in the ELMv0.

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“…In fact, for most gradient-based optimization methods, becoming trapped in a local objective function minimum is a common problem. To address this issue, Xi et al (2015) applied a global search method, termed quantum-behaved particle swarm optimization (QPSO), to calibrate the RZWQM2 and obtained promising results.…”

Section: Introductionmentioning

confidence: 99%

“…Proposed by Sun et al (2004), QPSO is a population-based swarm intelligence algorithm theoretically guaranteed to find optimal solutions in search space. Given its strong global convergence, the QPSO algorithm has been applied to many studies in recent years (Davoodi et al, 2014;Xi et al, 2015;Hassani and Lee, 2016). A review on its application can be found in Fang et al (2010).…”

Section: Introductionmentioning

confidence: 99%

“…The key idea is to use SG interpolation to construct a surrogate of the RZWQM2 in a polynomial form, and then evaluate the cheap-to-run surrogate model in the global optimization process so as to improve overall computational efficiency. The main contribution of our work lies in exploring the combination of a state-of-the-art SG surrogate method with a global optimization technique, and successfully applying this efficient method to find better solutions of the RZWQM2 than previous study (Qi et al 2011, Xi et al, 2015, which greatly improves model calibration performance and helps make rational and effective agricultural-hydrological decisions.…”

Section: Introductionmentioning

confidence: 99%

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Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

GUI

et al. 2017

Journal of Hydrology

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Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and-loss data from a subsurface-drained cornsoybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

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Calibrating RZWQM2 model using quantum-behaved particle swarm optimization algorithm

Cited by 14 publications

References 30 publications

Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

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