An improved Wolf pack algorithm for optimization problems: Design and evaluation

Chen, Xuan; Cheng, Feng; Liu, Cong; Cheng, Long; Mao, Ying

doi:10.1371/journal.pone.0254239

Cited by 21 publications

(7 citation statements)

References 40 publications

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“…This may involve combining regularization techniques with swarm intelligence optimization algorithms and ensemble methods to develop more reliable and generalizable models for genomic analysis. For example, the initial parameters of the model can be further optimized through the swarm intelligence optimization algorithm, such as the whale optimization algorithm (Brodzicki et al, 2021), Harris Hawks optimization algorithm (Qu et al, 2021), and wolf pack Frontiers in Genetics frontiersin.org optimization algorithm (Chen et al, 2021), to obtain more accurate results. Additionally, efforts to improve data quality and increase sample sizes can help reduce the risk of overfitting and enhance the robustness of artificial neural network-based predictive models in clinical applications.…”

Section: Discussionmentioning

confidence: 99%

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

Zheng,

Li,

Xiong

2024

Front. Genet.

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Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited.Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed.Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset.Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.

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Section: Discussionmentioning

confidence: 99%

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

Zheng,

Li,

Xiong

2024

Front. Genet.

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“…In addition, the local carbon emissions trading policy (CETP) also has an impact on it. Chen [12] analyzed panel data for 282 prefecture-level and above cities in China from 2007 to 2017, and found that CETP can significantly improve the efficiency of carbon emissions in pilot cities.…”

Section: Related Workmentioning

confidence: 99%

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Chen,

et al. 2023

Processes

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Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.

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“…The act of rounding up is the last step of the wolf pack's hunting behavior, in which the wolves summoned by the head wolf attack the prey. When the hunting is completed, the prey obtained will be distributed to the wolf that has contributed the most to the hunting action in priority, in order of the degree of contribution in the hunting, and finally to the wolf with the least merit [20].…”

Section: The Basic Principle Of the Wolf Pack Optimization Algorithmmentioning

confidence: 99%

The application of new media communication technology in vocational education teaching under the background of big data

Wang

2023

Applied Mathematics and Nonlinear Sciences

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Exploring the application of new media communication technology in vocational education teaching is better for improving the skill level of vocational school students. In this paper, starting from the KNN algorithm in the context of big data, the basic principle and process of the algorithm are explained, and the principle and model of the wolf pack optimization algorithm are introduced. The parameters of the KNN algorithm are optimized using the wolf pack search algorithm, and the definition of the objective function, initialization and solution of the optimal K value are given, which leads to the WPOA-KNN analysis model. The new media communication technology is analyzed, including teaching media, new media teaching technology and the advantages of new media communication technology applied to vocational education teaching. The WPOA-KNN analysis model was used to analyze and demonstrate the teaching of new media technology for vocational education in a higher education institution. From the satisfaction evaluation, the percentage of those who said they liked it was 76.06%, and the percentage of those who disliked it was only 9.09%. In terms of students’ learning behavior, the overall percentage of those who rated above C grade was 92.37%. This shows that using new media communication technology for vocational education teaching can enhance students’ learning enthusiasm, make them have a more exquisite technical level, and meet the diversified talent needs of employing enterprises.

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An improved Wolf pack algorithm for optimization problems: Design and evaluation

Cited by 21 publications

References 40 publications

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

The application of new media communication technology in vocational education teaching under the background of big data

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