Estimating the mechanical properties of Heat-Treated woods using Optimization Algorithms-Based ANN

Nikoo, Mehdi; Malekabadi, Reza Abbasi; Hafeez, Ghazanfarah

doi:10.1016/j.measurement.2022.112354

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…Numerous researchers have made improvements to DBO parameters and applied them in practical scenarios. Zhang et al [59] used fragmental linear chaotic mapping to generate the initial dung beetle population. They employed an adaptive nonlinear producer rate decay model to control the number of producers and applied dimension learning-enhanced foraging search strategies.…”

Section: B Federated Optimization For Communication Cost and Data Het...mentioning

confidence: 99%

FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer

Wang,

Chen,

et al. 2024

IEEE Access

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As an emerging distributed machine learning technology, federated learning has gained widespread attention due to its critical privacy protection mechanism. However, it also faces challenges such as high communication costs and heterogeneous client data.In order to address the above issues. This paper proposes a federated learning approach based on the dung beetle optimizer, named FedDBO. In this method, the model parameters uploaded from clients to the server are transformed into model scores. In each round of training, only some of the clients with high model scores need to be selected to upload their parameters to the server, thus reducing communication costs; Simultaneously, a model retraining strategy is introduced. After aggregating the model parameters sent by clients, the server performs a second iterative training on the aggregated model using its own metadata, thereby reducing data heterogeneity and improving model performance. In addition, a proof of convergence is provided, demonstrating that the model aggregated by FedDBO converges to the aggregated model of FedAvg after each training round. Finally, experiments indicate that when simulating various data heterogeneous environments on datasets, FedDBO exhibits higher accuracy and better stability compared to three other algorithms: FedAvg, FedShare, and FedPSO.

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Section: B Federated Optimization For Communication Cost and Data Het...mentioning

confidence: 99%

FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer

Wang,

Chen,

et al. 2024

IEEE Access

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“…The GWO optimizes the weights of the networks to reduce the error. The GWO-ANN model is developed to improve the accuracy, which compares PSO, multiple linear regression (MLR), and nonlinear regression (NLR) models [65]. Two machine learning techniques have been adopted, i.e., ANN and GWO, to predict the level of road crash severity [66].…”

Section: • Optimization Of the Artificial Neural Network (Ann)mentioning

confidence: 99%

Optimizing a Multi-Layer Perceptron Based on an Improved Gray Wolf Algorithm to Identify Plant Diseases

Tian

Chen

et al. 2023

Mathematics

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Metaheuristic optimization algorithms play a crucial role in optimization problems. However, the traditional identification methods have the following problems: (1) difficulties in nonlinear data processing; (2) high error rates caused by local stagnation; and (3) low classification rates resulting from premature convergence. This paper proposed a variant based on the gray wolf optimization algorithm (GWO) with chaotic disturbance, candidate migration, and attacking mechanisms, naming it the enhanced gray wolf optimizer (EGWO), to solve the problem of premature convergence and local stagnation. The performance of the EGWO was tested on IEEE CEC 2014 benchmark functions, and the results of the EGWO were compared with the performance of three GWO variants, five traditional and popular algorithms, and six recent algorithms. In addition, EGWO optimized the weights and biases of a multi-layer perceptron (MLP) and proposed an EGWO-MLP disease identification model; the model was tested on IEEE CEC 2014 benchmark functions, and EGWO-MLP was verified by UCI dataset including Tic-Tac-Toe, Heart, XOR, and Balloon datasets. The experimental results demonstrate that the proposed EGWO-MLP model can effectively avoid local optimization problems and premature convergence and provide a quasi-optimal solution for the optimization problem.

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“…Firstly, an improved IDBO algorithm is used to optimize LSTM parameters, then the predicted value of the subsequence is recombined to get the final result, indicating that IDBO-LSTM improves the accuracy of prediction. Zhang and Zhu [31] improved the DBO algorithm with three strategies, and then optimized a back-propagation (BP) neural network with IDBO algorithm, proving that IDBO-BP has a superior performance in predicting the parameters of heat treated larch sawed timber. By using DBO algorithm to automatically iteratively search the optimal LSTM parameters, Zhang et al [32] proposed a short-term power load combination prediction model of DBO-LSTM, the average prediction error was reduced by 25.13%.…”

Section: B Parameter Optimizationmentioning

confidence: 99%

NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

Kong

2023

IEEE Access

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Ultra-wideband (UWB) is regarded as the technology with the most potential for precise indoor location due to its centimeter-level ranging capabilities, good time resolution, and low power consumption. However, Because of the presence of non-line-of-sight (NLOS) error, the accuracy of UWB localization deteriorates significantly in harsh and volatile indoor conditions. Therefore, identifying NLOS conditions is crucial to enhancing the accuracy of UWB location. This paper proposes a convolutional neural network (CNN) classification method based on an improved Dung Beetle Optimizer (DBO). Firstly, based on the standard DBO, the Circle chaotic mapping, non-uniform Gaussian variational strategy, and multi-stage perturbation strategy are used to optimize the exploration capability and enhance the performance of original DBO method, the superiority-seeking ability of IDBO is demonstrated by testing 23 benchmark functions. In addition, based on the IDBO algorithm, we propose an IDBO-CNN classification model, with the help of IDBO, the accuracy of NLOS identification is improved by adjusting the hyperparameters of CNN to be closer to the optimal solution. Experiments conducted on the open-source dataset demonstrate that IDBO-CNN is capable of achieve the desired effect. In comparison to the conventional CNN approach, the F1-score achieved by IDBO-CNN is enhanced by 3.31%, which demonstrates that IDBO-CNN has superior identification accuracy.

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Estimating the mechanical properties of Heat-Treated woods using Optimization Algorithms-Based ANN

Cited by 9 publications

References 19 publications

FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer

FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer

Optimizing a Multi-Layer Perceptron Based on an Improved Gray Wolf Algorithm to Identify Plant Diseases

NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

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