Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements

He, Zhenzong; Qi, Hong; Chen, Qin; Ruan, Li-Ming

doi:10.1016/j.partic.2014.12.016

Cited by 20 publications

(9 citation statements)

References 24 publications

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“…The coefficient of the function Γ(x) can be determined when the value of RE is the minimum value. The improved quantum-behaved particle swarm optimization (IQPSO) algorithm presented in [25] is used to optimize the undetermined function RE in our study. The detail of the IQPSO available in reference [25] will not be repeated here.…”

Section: mentioning

confidence: 99%

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Quantitative Research of Photobioreactor Performance Based on an Improved Surface Fitting Method

Jin

2019

Energies

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The relationship between performance and working conditions in photobioreactor hydrogen production systems illuminated by a variable intensity light source has been described quantitatively using relational expressions. First, based on the finite volume method and the Michaelis-Menten model, the hydrogen production process of a photobioreactor (PBR) system was simulated numerically. Then, the performance of the PBR system was evaluated considering the hydrogen production rate, dimensionless hydrogen production rate, hydrogen production thrust coefficient and conversion efficiency of light energy to hydrogen energy rate as performance parameters, and the relationship between these parameters and working conditions was studied. Finally, the improved quantum-behaved particle swarm optimization (IQPSO) and surface fitting technique based on the curve fitting method were used to obtain relational expressions about the performance and working conditions of the PBR. All of the results show that the method can obtain accurately relational expressions for the performance optimization and forecasts of the PBR system.

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Section: mentioning

confidence: 99%

“…The improved quantum-behaved particle swarm optimization (IQPSO) algorithm presented in [25] is used to optimize the undetermined function RE in our study. The detail of the IQPSO available in reference [25] will not be repeated here. The flow chart of curve fittings based on the IQPSO is shown in Figure 4.…”

Section: mentioning

confidence: 99%

Quantitative Research of Photobioreactor Performance Based on an Improved Surface Fitting Method

Jin

2019

Energies

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“…Recently, quantum-behaved particle swarm optimization (QPSO) proposed by Sun et al 30,31 has been widely used. As a promising global optimization algorithm, QPSO has been applied to many fields such as spectral extinction measurements, 32 economic dispatch, 33 solving nonlinear equations, 34 and processing medical image. 35,36 For the application of medical image, Li et al 35 proposed a dynamic-context cooperative QPSO with enhanced search ability for processing medical images and Li et al 36 proposed a SCQPSO algorithm to optimize the parameters for image segmentation of stomach CT images.…”

Section: Introductionmentioning

confidence: 99%

“…30,31 has been widely used. As a promising global optimization algorithm, QPSO has been applied to many fields such as spectral extinction measurements, 32 economic dispatch, 33 solving nonlinear equations, 34 and processing medical image. 35,36 For the application of medical image, Li et al.…”

Section: Introductionmentioning

confidence: 99%

Quantum-behaved particle swarm optimization-based active noise control system with timing varying path

Yue

et al. 2020

Journal of Low Frequency Noise, Vibration and Active Control

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Active noise control systems can effectively suppress the impact of low-frequency noise and they have been applied in many fields. Recently, the evolutionary computation algorithm-based active noise control system has attracted considerable attention. To improve the noise reduction performance of the evolutionary computation algorithm-based active noise control system and solve the problem that the system cannot converge again when the path abruptly changes in steady state, we propose the path abruptly change-quantum-behaved particle swarm optimization algorithm. We apply quantum-behaved particle swarm optimization, a global optimization algorithm, to the active noise control system to improve noise reduction performance. In addition, the scheme of detecting the abrupt path change in steady state and performing re-convergence processing is designed to effectively address the problem that the system cannot regain convergence after a path change in steady state. The simulation study demonstrates that the proposed algorithm can efficiently improve noise reduction performance, accurately detect the path change, and re-converge to new global optimization.Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https:// creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).versions 7,8 have been used in many ANC systems, but FxLMS has some limitations. First, the estimated model of the secondary pathŜ z ð Þ is required; the model estimation error will reduce noise reduction performance and even cause instability. [9][10][11][12] In addition, FxLMS is a gradient-based optimization method, which can easily fall into local minima. 13 The adaptability of the most commonly used off-line estimation S z ð Þ method is poor. One solution is the online estimation of the secondary path. [14][15][16][17] However, most of the on-line estimation methods require additional noise, which increases the residual noise level and computational complexity. 18 Another method is evolutionary computation algorithm-based ANC system, which does not require secondary path estimation and is able to avoid the problem that the traditional gradient-based methods easily fall into local minima. Next, we introduce the evolutionary computation algorithm-based ANC systems in detail.Genetic algorithm (GA) has been applied to ANC systems. Yim et al. 13 proposed an ANC system, which uses the IIR filter as the controller and the GA to adjust the weights of the adaptive IIR filter. Russo and Sicuranza 19,20 used the GA to adjust the weights of a nonlinear Volterra filter in an ANC system and demonstrates better noise reduction performance than an FxLMS-based ANC system. Chang and Chen 21 proposed an adaptive GA-based linear ANC system using FIR filter and nonlinear ANC system using Volt...

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“…The research is organized as follows. First, the principles of ALSM and SPSO-DE hybrid algorithm are introduced, and that of SEM, available in Reference [14], is not repeated here. Then, the sensitivity analysis of optical 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.…”

Section: Introductionmentioning

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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Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements

Cited by 20 publications

References 24 publications

Quantitative Research of Photobioreactor Performance Based on an Improved Surface Fitting Method

Quantitative Research of Photobioreactor Performance Based on an Improved Surface Fitting Method

Quantum-behaved particle swarm optimization-based active noise control system with timing varying path

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

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