Application of stochastic particle swarm optimization algorithm to determine the graded refractive index distribution in participating media

Wei, Linyang; Qi, Hong; Ren, Yukun; Ruan, Li-Ming

doi:10.1016/j.infrared.2016.07.024

Cited by 17 publications

(2 citation statements)

References 31 publications

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“…It means that the particle just could fly in one direction till finding a better location resulting in premature convergence. The SPSO algorithm was developed to overcome this issue and it showed higher retrieval accuracy during solution of ASD prediction and other problems of inverse radiation (Qi et al 2007;Yuan et al 2011;Wei et al 2016). In SPSO algorithm, the formulae for velocity update and position update are described as (Qi, Ruan et al 2007):…”

Section: Spsomentioning

confidence: 99%

Application of Hybridized ANN-GARCH, ANN-SETAR, MARS-SPSO and CANFIS-SPSO Meta-Models for Improving Accuracy of Monthly Streamflow Prediction

Mikaeili

Shourian

2023

Preprint

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Among the components of the hydrological cycle, stream flow has a major role in integrated water resources management. Establishing an accurate and reliable forecasting method for prediction of stream flow is very useful. Nowadays, data-driven methods are variously applied for river flow prediction. By hybridizing, one can take advantage of the cons of different methods for the proposed purpose. In the present research, we have combined SETAR and GARCH methods with ANN and also coupled MARS and CANFIS with SPSO to predict the monthly flow of the Maroon River in south west of Iran. Thus, four hybridized data-driven models of ANN-GARCH, ANN-SETAR, MARS-SPSO and CANFIS-SPSO are developed and compared to see which method has the best performance. Although all the models yielded good results but it was seen that the ANN-SETAR model found more accurate answers in prediction of the stream flow with an average 5% higher accuracy in the results. The IQR of ANN-SETAR model is similar to observed value that this showed the efficiency of the ANN-SETAR for dependable simulation of extreme values of river flow compared to other models. So, it was concluded that the ANN-SETAR model is better than the other methods for forecasting the monthly streamflow.

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Section: Spsomentioning

confidence: 99%

Application of Hybridized ANN-GARCH, ANN-SETAR, MARS-SPSO and CANFIS-SPSO Meta-Models for Improving Accuracy of Monthly Streamflow Prediction

Mikaeili

Shourian

2023

Preprint

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“…However, in the standard PSO algorithm, there is only one term which regulates historic velocity during the evolution process, and therefore the particle can only fly along one direction until it finds a better position, which will result in premature convergence. To overcome the difficulty, the SPSO algorithm was introduced, and showed better retrieval accuracy in solving the problem of estimating the ASD and other inverse radiation problems [18][19][20].…”

Section: The Principle Of the Spsomentioning

confidence: 99%

Determination of Aerosol Size Distribution from Angular Light-Scattering Signals by Using a SPSO-DE Hybrid Algorithm

Mao

Han

2018

Computation

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The comparison of the angular light-scattering method (ALSM) and the spectral extinction method (SEM) in solving the inverse problem of aerosol size distribution (ASD) are studied. The inverse problem is solved by a SPSO-DE hybrid algorithm, which is based on the stochastic particle swarm optimization (SPSO) algorithm and differential evolution (DE) algorithm. To improve the retrieval accuracy, the sensitivity analysis of measurement signals to characteristic parameters in ASDs is studied; and the corresponding optimal measurement angle selection region for ALSM and optimal measurement wavelength selection region for SEM are proposed, respectively. Results show that more satisfactory convergence properties can be obtained by using the SPSO-DE hybrid algorithm. Moreover, short measurement wavelengths and forward measurement angles are beneficial to obtaining more accurate results. Then, common monomodal and bimodal ASDs are estimated under different random measurement errors by using ALSM and SEM, respectively. Numerical tests show that retrieval results by using ALSM show better convergence accuracy and robustness than those by using SEM, which is attributed to the distribution of the objective function value. As a whole, considering the convergence properties and the independence on prior optical information, the ALSM combined with SPSO-DE hybrid algorithm provides a more effective and reliable technique to obtain the ASDs.

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Determination of gradient index based on laser beam deflection by stochastic particle swarm optimization

Wei

Song

et al. 2021

Appl. Phys. B

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Application of stochastic particle swarm optimization algorithm to determine the graded refractive index distribution in participating media

Cited by 17 publications

References 31 publications

Application of Hybridized ANN-GARCH, ANN-SETAR, MARS-SPSO and CANFIS-SPSO Meta-Models for Improving Accuracy of Monthly Streamflow Prediction

Application of Hybridized ANN-GARCH, ANN-SETAR, MARS-SPSO and CANFIS-SPSO Meta-Models for Improving Accuracy of Monthly Streamflow Prediction

Determination of Aerosol Size Distribution from Angular Light-Scattering Signals by Using a SPSO-DE Hybrid Algorithm

Determination of gradient index based on laser beam deflection by stochastic particle swarm optimization

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