Model identification of the Proton Exchange Membrane Fuel Cells by Extreme Learning Machine and a developed version of Arithmetic Optimization Algorithm

Xu, Yi‐Peng; Tan, Jingwen; Zhu, Daqi; Ouyang, Peng; Taheri, Bahman

doi:10.1016/j.egyr.2021.04.042

Cited by 71 publications

(22 citation statements)

References 36 publications

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“…The four arithmetic operators (Multiplication, Division, Subtraction, and Addition) are basically used in various computational sciences [109]. AOA is successfully applied to find a proper model identification of the Proton Exchange Membrane Fuel Cell (PEMFC) [110]. Therefore, these four simple operators can be utilized to find the most optimum solutions while balancing between the exploration and exploitation phases [23], [111].…”

Section: A Inspirationmentioning

confidence: 99%

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

et al. 2022

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Section: A Inspirationmentioning

confidence: 99%

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

et al. 2022

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“…It can be seen that D, M, S and A estimate the position of the near-optimal solution, and other solutions update their positions stochastically around the area of the near-optimal solution [37]. Some recent applications of AOA include workflow scheduling [38], cooling, heating and power systems [39], the forced switching mechanism [38] and the identification of proton exchange membrane fuel cells [40].…”

Section: Arithmetic Optimization Algorithmmentioning

confidence: 99%

Analysis of Nanofluid Particles in a Duct with Thermal Radiation by Using an Efficient Metaheuristic-Driven Approach

et al. 2022

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This study investigated the steady two-phase flow of a nanofluid in a permeable duct with thermal radiation, a magnetic field, and external forces. The basic continuity and momentum equations were considered along with the Buongiorno model to formulate the governing mathematical model of the problem. Furthermore, the intelligent computational strength of artificial neural networks (ANNs) was utilized to construct the approximate solution for the problem. The unsupervised objective functions of the governing equations in terms of mean square error were optimized by hybridizing the global search ability of an arithmetic optimization algorithm (AOA) with the local search capability of an interior point algorithm (IPA). The proposed ANN-AOA-IPA technique was implemented to study the effect of variations in the thermophoretic parameter (Nt), Hartmann number (Ha), Brownian (Nb) and radiation (Rd) motion parameters, Eckert number (Ec), Reynolds number (Re) and Schmidt number (Sc) on the velocity profile, thermal profile, Nusselt number and skin friction coefficient of the nanofluid. The results obtained by the designed metaheuristic algorithm were compared with the numerical solutions obtained by the Runge–Kutta method of order 4 (RK-4) and machine learning algorithms based on a nonlinear autoregressive network with exogenous inputs (NARX) and backpropagated Levenberg–Marquardt algorithm. The mean percentage errors in approximate solutions obtained by ANN-AOA-IPA are around 10−6 to 10−7. The graphical analysis illustrates that the velocity, temperature, and concentration profiles of the nanofluid increase with an increase in the suction parameter, Eckert number and Schmidt number, respectively. Solutions and the results of performance indicators such as mean absolute deviation, Theil’s inequality coefficient and error in Nash–Sutcliffe efficiency further validate the proposed algorithm’s utility and efficiency.

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“…The Gaussian mutation tool is employed to generate a further variant, which retains a better position by comparing it with the target value of the current optimal individual situation. This mechanism makes the algorithm's local results and global results well balanced [ 41 ]. The probability density function (PDF) formula of Gaussian distribution is as tails:

where μ is the anticipation of Gaussian distribution and σ is defined as the standard deviation of Gaussian distribution.…”

Section: An Improved Jellyfish Search Optimizermentioning

confidence: 99%

Fractional-Order Boosted Jellyfish Search Optimizer with Gaussian Mutation for Income Forecast of Rural Resident

Lei

Fan

Yang

et al. 2022

Computational Intelligence and Neuroscience

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The disposable income of residents can reflect the living standard of people in the area. For government departments, it is necessary to master the trend of rural resident income to formulate corresponding policies benefiting farmers. Thus, this paper proposes a grey model with an improved jellyfish search optimizer to predict the rural resident income in Shaanxi Province. Firstly, by applying fractional-order modified strategy and Gaussian mutation mechanism to the original algorithm, the proposed algorithm shows better performance in solving accuracy, stability, and convergence acceleration when compared with different classical methods on cec2017 and cec2019 test functions. Then, based on the fractional time-delayed grey model, a discrete fractional time-delayed grey model with triangular residual correction (TDFTDGM) is proposed by replacing the derivative with a first-order difference and introducing the triangular residual correction functions. Finally, the improved jellyfish search optimizer is used to explore the optimal order of the TDFTDGM model. The all-around performance of the forecast model is incomparable to additional grey models compared on four measure criteria, which means it is a practical approach for long-term prediction with small samples. Moreover, the forecast data of rural resident income in Shaanxi Province from 2021 to 2025 are given for reference.

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Model identification of the Proton Exchange Membrane Fuel Cells by Extreme Learning Machine and a developed version of Arithmetic Optimization Algorithm

Cited by 71 publications

References 36 publications

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

Analysis of Nanofluid Particles in a Duct with Thermal Radiation by Using an Efficient Metaheuristic-Driven Approach

Fractional-Order Boosted Jellyfish Search Optimizer with Gaussian Mutation for Income Forecast of Rural Resident

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