A Hybrid Multiscale Finite Cloud Method and Finite Volume Method in Solving High Gradient Problem

Oh, Miewleng; Hoe, Yeak Su

doi:10.1142/s0219876222500025

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…Feature selection [5] is a challenging NP-hard combinatorial problem, which can be effectively addressed using meta-heuristic algorithms. These algorithms use defined rules and randomness to simulate natural phenomena, thus avoiding the gradient problem with the optimization process [6], which is different from traditional mathematical programming methods [7]. So, some evolutionary algorithms [8] are generally applied to feature selection.…”

Section: Related Workmentioning

confidence: 99%

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A Robust Adaptive Hierarchical Learning Crow Search Algorithm for Feature Selection

et al. 2023

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Feature selection is a multi-objective problem, which can eliminate irrelevant and redundant features and improve the accuracy of classification at the same time. Feature selection is a great challenge to balance the conflict between the two goals of selection accuracy and feature selection ratio. The evolutionary algorithm has been proved to be suitable for feature selection. Recently, a new meta-heuristic algorithm named the crow search algorithm has been applied to the problem of feature selection. This algorithm has the advantages of few parameters and achieved good results. However, due to the lack of diversity in late iterations, the algorithm falls into local optimal problems. To solve this problem, we propose the adaptive hierarchical learning crow search algorithm (AHL-CSA). Firstly, an adaptive hierarchical learning technique was used to adaptive divide the crow population into several layers, with each layer learning from the top layer particles and the topmost layer particles learning from each other. This strategy encourages more exploration by lower individuals and more exploitation by higher individuals, thus improving the diversity of the population. In addition, in order to make full use of the search information of each level in the population and reduce the impact of local optimization on the overall search performance of the algorithm, we introduce an information sharing mechanism to help adjust the search direction of the population and improve the convergence accuracy of the algorithm. Finally, different difference operators are used to update the positions of particles at different levels. The diversity of the population is further improved by using different difference operators. The performance of the method was tested on 18 standard UCI datasets and compared with eight other representative algorithms. The comparison of experimental results shows that the proposed algorithm is superior to other competitive algorithms. Furthermore, the Wilcoxon rank-sum test was used to verify the validity of the results.

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Section: Related Workmentioning

confidence: 99%

“…The average Hamming value of the population is calculated according to Formulas (3) and ( 4), and then the number of layers is calculated according to the average Hamming value of the population, using Formulas ( 5) and (6). Equations ( 7)-( 10) can be used for migration learning in the formula.…”

Section: Adaptive Hierarchical Learning Crow Search Algorithmmentioning

confidence: 99%

A Robust Adaptive Hierarchical Learning Crow Search Algorithm for Feature Selection

et al. 2023

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3D airborne electromagnetic forward modeling based on the multiscale hexahedral finite-element method

Qu,

Zhang,

Yin

et al. 2024

GEOPHYSICS

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Slow forward modeling is the main factor that restricts the practical use of three-dimensional (3D) inversion and interpretation of airborne electromagnetic (AEM) data. To improve the modeling efficiency in 3D AEM, we propose a new multiscale finite-element (MsFE) method based on unstructured hexahedral meshes. Compared with the traditional 3D AEM forward modeling, the main advantage of our newly developed method is that it can simulate complex underground structures in the earth quickly. Since we can fit the earth's topography or the anomalous bodies underground using a small number of hexahedral grids, we can quickly model them using MsFE. The main idea of the MsFE forward modeling method is to construct an interpolation operator between a coarse and a dense mesh and use the interpolation operator to map the conventional FE coefficient matrix to the MsFE coefficient matrix and thus reduce the number of unknowns in the modeling process. This will vastly reduce the scale of the linear equations system. We validate our method by simulating a typical mountain peak model and demonstrate its effectiveness by simulating numerous synthetic models and a model from Voisey Bay's Ovoid sulfide deposit, Canada.

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A Hybrid Multiscale Finite Cloud Method and Finite Volume Method in Solving High Gradient Problem

Cited by 2 publications

References 36 publications

A Robust Adaptive Hierarchical Learning Crow Search Algorithm for Feature Selection

A Robust Adaptive Hierarchical Learning Crow Search Algorithm for Feature Selection

3D airborne electromagnetic forward modeling based on the multiscale hexahedral finite-element method

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