A competitive variable-fidelity surrogate-assisted CMA-ES algorithm using data mining techniques

Li, Zengcong; Tian, Kuo; Li, Hongqing; Shi, Yunfeng; Wang, Bo

doi:10.1016/j.ast.2021.107084

Cited by 13 publications

(2 citation statements)

References 55 publications

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“…However, the physical loss part of DR-PINN is not a continuously differentiable function; thus, the typical backpropagation algorithm is not applicable in parameter optimization. Therefore, the covariance matrix adaptation evolution strategy (CMA-ES) was introduced to optimize the inner network weights for the PINN and VAE simultaneously, which are stochastic derivative-free for complex optimization problems …”

Section: Methodsmentioning

confidence: 99%

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Disentangled Representation Aided Physics-Informed Neural Network for Predicting Syngas Compositions of Biomass Gasification

Ren,

Wu,

Weng

et al. 2024

Energy Fuels

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Machine learning (ML) has been extensively studied and applied in the biomass gasification field currently. However, insufficient experimental data tends to cause a mismatch between the ML model and physical mechanism, particularly for the feedstocks that do not appear in the training data set, becoming a significant challenge in creating credible ML models for biomass gasification. Therefore, this study proposes a disentangled representation-aided physics-informed neural network method, briefly called DR-PINN, to predict biomass gasification syngas components. First, the DR-PINN extracts the latent variables to represent the feedstock properties through disentangled representation learning and generates synthetic samples in the gasificationrelated latent variable space to cover a full range of feedstock types. Then, DR-PINN employs inequality constraints to embed a priori monotonic relationships into the model training loss function. Finally, experimental and synthetic samples are simultaneously considered in the model training process to realize the synergy and complementarity of actual data information and existing physical knowledge using an evolutionary algorithm. As a result, DR-PINN shows good prediction performance (the feedstocks within the training data set: R 2 ≈ 0.96, root-mean-square error (RMSE) ≈ 1.7; the feedstocks outside the training data set: R 2 ≈ 0.81, RMSE ≈ 3). Moreover, even with the feedstocks outside the training data set, the DR-PINN model can strictly abide by the prior physical monotonic relationships, with the physical consistency degree equal to 1. Overall, the proposed DR-PINN demonstrates superior generalization and interpretability compared to other methods, such as RF, GBR, SVM, ANN, and PINN.

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Section: Methodsmentioning

confidence: 99%

“…Therefore, the covariance matrix adaptation evolution strategy (CMA-ES) was introduced to optimize the inner network weights for the PINN and VAE simultaneously, which are stochastic derivative-free for complex optimization problems. 62 2.4. Model Performance Evaluation.…”

Section: The Modeling Framework Of Dr-pinnmentioning

confidence: 99%

Disentangled Representation Aided Physics-Informed Neural Network for Predicting Syngas Compositions of Biomass Gasification

Ren,

Wu,

Weng

et al. 2024

Energy Fuels

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A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm

Luo,

Dong,

Chen

et al. 2024

Lecture Notes in Computer Science

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RETRACTED ARTICLE: Application of infrared image detection based on high-resolution image processing in motion recognition

2024

Opt Quant Electron

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A competitive variable-fidelity surrogate-assisted CMA-ES algorithm using data mining techniques

Cited by 13 publications

References 55 publications

Disentangled Representation Aided Physics-Informed Neural Network for Predicting Syngas Compositions of Biomass Gasification

Disentangled Representation Aided Physics-Informed Neural Network for Predicting Syngas Compositions of Biomass Gasification

A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm

RETRACTED ARTICLE: Application of infrared image detection based on high-resolution image processing in motion recognition

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