Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm

Sun, Xianke; Wang, Gaoliang; Xu, Linzhi; Yuan, Huang; Yousefi, Nasser

doi:10.1016/j.energy.2021.121532

Cited by 33 publications

(12 citation statements)

References 39 publications

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“…During the PEMFC simulation, an improved version of AOA was applied to DBN to reduce the relative error between network output data and voltage. The proposed DBN-IAOA approach was superior to the original DBN method Sun et al [ 23 ] AOA + Levy Flight LAO Designing Microstrip Patch Antenna The main shortcoming of AOA is it get trapped in local minima and also it has a slow convergence rate. To overcome this issue author proposed a new Levy Flight based Levy Flight Archimedes optimizer (LAO).…”

Section: Enhanced Aoa Variantsmentioning

confidence: 99%

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Dhal

Ray

Rai

et al. 2023

Arch Computat Methods Eng

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The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. Archimedes Optimization Algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, Archimedes Optimization Algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics Inspired Optimization Algorithms (PIOA), the Archimedes Optimization Algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.

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Section: Enhanced Aoa Variantsmentioning

confidence: 99%

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Dhal

Ray

Rai

et al. 2023

Arch Computat Methods Eng

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“…Qiu Y, Zhou E L, Xue H T, et al proposed to detect edges first and then fit with templates to improve the visual effect of interpolated images [11]. Sun X, Wang G, Xu L, et al suggested an edge-guided interpolation algorithm based on the least squares (LS) method, using the edge-guided characteristics of covariance adaptation to adjust the interpolation coefficients and adaptively obtain the interpolation function to improve the interpolation effect [12]. Yang J, Bao W, Liu Y, et al propose an image superresolution reconstruction algorithm based on example learning [13].…”

Section: Related Workmentioning

confidence: 99%

Application of Improved Deep Belief Network Model in 3D Art Design

2022

Mathematical Problems in Engineering

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In recent years, driven by the high-speed computing performance of computers and massive data on the Internet, deep nervine networks with highly abstract feature extraction and classification capabilities have been widely used in 3D art design and other fields, and a large number of breakthrough results have emerged. 3D art design is a research hotspot in the field of computer vision, which has broad application prospects and practical application value. Aiming at the problems of slow convergence and long training time of traditional deep belief network in the process of data feature expression, this paper proposes an unsupervised learning algorithm, namely adaptive deep belief network, and applies it to 3D art design. Its linear correction unit has good sparsity, which can improve the network performance well. The deep belief network DBN is formed by stacking the restricted Boltzmann machine RBM. The recognition research of 3D art design by optimizing the wavelet deep belief network can effectively improve the recognition rate and recognition speed of handwritten character recognition and achieve good results.

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“…Hashim, et al [27] designed this algorithm by replicating the Archimedes' principles. Several engineering fields that have implemented AOA include design [28], forcasting [29], identification of fuel cell parameter [30], optimization prediction [31], and estimation [32]. Unfortunately, this AOA technique has never been used to solve scheduling problems, especially EEHFSP, thereby motivating scholars to investigate this problem.…”

Section: A R T I C L E I N F O Abstractmentioning

confidence: 99%

“…A total of 9 variants of problems that were randomly generated were employed based on the uniform distribution range [1.8, 12.3] for the processing time. Furthermore, the removal time was generated following the random integer principle with the range of [1,3], and the setup time data from the i-th to the i + 1 jobs at the second stage were generated from the range of [0,32]. All experiments were conducted using the MATLAB application.…”

Section: Experimental Settingmentioning

confidence: 99%

A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

Utama

Salima

Widodo

2022

Int. J. Adv. Intell. Informatics

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The manufacturing sector consumes most of the global energy and had been in focus since the outbreak of the energy crisis. One of the proposed strategies to overcome this problem is to implement appropriate scheduling, such as Hybrid Flow Shop Scheduling. Therefore, this study aims to create a Hybrid Archimedes Optimization Algorithm (HAOA) for solving the Energy-Efficient Hybrid Flow Shop Scheduling Problem (EEHFSP). It is hoped that this helps to provide new insights into advanced HAOA methods for resolving the EEHFSP as the algorithm has the potential to be a more efficient alternative. In this study, three stages of EEHFSP were considered in the problem as well as a sequence-dependent setup and removal times in the second stage. Experiments with three population variations and iterations were presented for testing the effect of HAOA parameters on energy consumption. Furthermore, ten job variations are also presented to evaluate the performance of the HAOA algorithm and the results showed that HAOA iteration and the population did not affect the removal and processing of energy consumption, but impacted that of setup and idle. The comparison of these ten cases revealed that the proposed HAOA produced the best total energy consumption (TEC) when compared to the other algorithms.

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Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm

Cited by 33 publications

References 39 publications

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Application of Improved Deep Belief Network Model in 3D Art Design

A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

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