A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering

Yu, Qiyuan; Lin, Qiuzhen; Zhu, Zexuan; Wong, Ka-Chun; Coello, Carlos A. Coello

doi:10.1016/j.swevo.2022.101075

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…The computations used in finding the regression coefficients, sum of squares for residuals, regression sums, etc. are rather complex (Yu et. al., 2022).…”

Section: Many Linear Regressionsmentioning

confidence: 99%

Computational Analysis of Predicting Stress Intensity Factor Using Hdmr Technique

L. Ramaprasad Reddy

2024

jes

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The present research focused to develop meta-model (MM) as a low-cost substitute for the Finite Element Method (FEM), to forecast the Stress Intensity Factor (SIF) of a semi-elliptic crack propagating. Multi-linear regression (MLR) based MM is developed for predicting the crack propagation. Three parameters—Root Mean Square Error (RMSE), Maximum Absolute Error (AAE), and Average Absolute Error—are examined to compare the MM's precision (AAE).

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“…The computations used in finding the regression coefficients, sum of squares for residuals, regression sums, etc. are rather complex (Yu et. al., 2022).…”

Section: Many Linear Regressionsmentioning

confidence: 99%

Computational Analysis of Predicting Stress Intensity Factor Using Hdmr Technique

L. Ramaprasad Reddy

2024

jes

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“…Because polynomial functions can better approximate and fit most nonlinear functions, the parameter solving problem of polynomial functions can usually obtain the global optimal solution through optimization algorithms [15,16]. Approximating the nonlinear relationship of speed prediction using polynomial functions can improve the accuracy of speed prediction to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

A Novel Spatiotemporal Periodic Polynomial Model for Predicting Road Traffic Speed

Jiang,

Feng,

Liao

et al. 2024

Symmetry

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Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spatial feature extraction methods, we introduce a maximum mutual information coefficient spatial feature extraction method. In terms of periodic feature extraction methods, we introduce a periodic trend modeling method into the prediction of speed time series, and effective fusion is carried out. Four strategies are evaluated based on the Guangzhou road speed dataset: a univariate polynomial model, a spatiotemporal polynomial model, a periodic polynomial model, and a spatiotemporal periodic polynomial model. The test results show that the three methods proposed in this article can effectively improve prediction accuracy. Comparing the spatiotemporal periodic polynomial model with multiple machine learning models and deep learning models, the prediction accuracy is improved by 5.94% compared to the best feedforward neural network. The research in this article can effectively deal with the temporal, spatial, periodic, and nonlinear characteristics of speed prediction, and to a certain extent, improve the accuracy of speed prediction.

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Machine learning based prediction of Young's modulus of stainless steel coated with high entropy alloys

Radhika,

Sabarinathan,

Ragunath

et al. 2024

Results in Materials

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A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering

Cited by 6 publications

References 42 publications

Computational Analysis of Predicting Stress Intensity Factor Using Hdmr Technique

Computational Analysis of Predicting Stress Intensity Factor Using Hdmr Technique

A Novel Spatiotemporal Periodic Polynomial Model for Predicting Road Traffic Speed

Machine learning based prediction of Young's modulus of stainless steel coated with high entropy alloys

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