Cooperative Co-evolution with Principal Component Analysis for Large Scale Optimization

2018 ◽  
pp. 426-434
Author(s):  
Guangzhi Xu ◽  
Xinchao Zhao ◽  
Rui Li
Keyword(s):  
Principal Component Analysis ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Large Scale Optimization ◽  
Scale Optimization

scholarly journals Regionalization of Daily Soil Moisture Dynamics Using Wavelet-Based Multiscale Entropy and Principal Component Analysis

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 548 ◽  
Author(s):  
Yuqing Sun ◽  
Jun Niu
Keyword(s):  
Principal Component Analysis ◽  
Soil Moisture ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Simulated Data ◽  
Grid Cell ◽  
Principal Component ◽  
Component Analysis ◽  
Multiscale Entropy ◽  
Homogeneous Groups

Hydrological regionalization is a useful step in hydrological modeling and prediction. The regionalization is not always straightforward, however, due to the lack of long-term hydrological data and the complex multi-scale variability features embedded in the data. This study examines the multiscale soil moisture variability for the simulated data on a grid cell base obtained from a large-scale hydrological model, and clusters the grid-cell based soil moisture data using wavelet-based multiscale entropy and principal component analysis, over the Xijiang River basin in South China, for the period of 2002–2010. The effective regionalization, for 169 grid cells with the special resolution of 0.5° × 0.5°, produced homogeneous groups based on the pattern of wavelet-based entropy information. Four distinct modes explain 80.14% of the total embedded variability of the transformed wavelet power across different timescales. Moreover, the possible implications of the regionalization results for local hydrological applications, such as parameter estimation for an ungagged catchment and designing a uniform prediction strategy for a sub-area in a large-scale basin, are discussed.

Assisted History Matching of Channelized Models by Use of Pluri-Principal-Component Analysis

SPE Journal ◽  
2016 ◽  
Vol 21 (05) ◽  
pp. 1793-1812 ◽  
Author(s):  
C.. Chen ◽  
G.. Gao ◽  
B. A. Ramirez ◽  
J. C. Vink ◽  
A. M. Girardi
Keyword(s):  
Principal Component Analysis ◽  
History Matching ◽  
Large Scale ◽  
Gaussian Model ◽  
Principal Component ◽  
Component Analysis ◽  
Work Flow ◽  
Real Field ◽  
Assisted History Matching ◽  
Facies Types

Summary Assisted history matching (AHM) of a channelized reservoir is still a very-challenging task because it is very difficult to gradually deform the discrete facies in an automated fashion, while preserving geological realism. In this paper, a pluri-principal-component-analysis (PCA) method, which supports PCA with a pluri-Gaussian model, is proposed to reconstruct geological and reservoir models with multiple facies. PCA extracts the major geological features from a large collection of training channelized models and generates gridblock-based properties and real-valued (i.e., noninteger-valued) facies. The real-valued facies are mapped to discrete facies indicators according to rock-type rules (RTRs) that determine the fraction of each facies and neighboring connections between different facies. Pluri-PCA preserves the main (or principal) features of both geological and geostatistical characteristics of the prior models. A new method is also proposed to automatically build the RTRs with an ensemble of training realizations. An AHM work flow is developed by integrating pluri-PCA with a derivative-free optimization algorithm. This work flow is validated on a synthetic model with four facies types and a real-field channelized model with three facies types, and it is applied to update both the facies model and the reservoir model by conditioning to production data and/or hard data. The models generated by pluri-PCA preserve the major geological/geostatistical descriptions of the original training models. This has great potential for practical applications in large-scale history matching and uncertainty quantification.

scholarly journals GO-PCA: An Unsupervised Method to Explore Biological Heterogeneity Based on Gene Expression and Prior Knowledge

2015 ◽  
Author(s):  
Florian Wagner
Keyword(s):  
Principal Component Analysis ◽  
Prior Knowledge ◽  
Large Scale ◽  
Cpg Island ◽  
Expression Profiles ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Data ◽  
Unsupervised Method ◽  
Biological Heterogeneity

Genome-wide expression profiling is a cost-efficient and widely used method to characterize heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such datasets typically relies on generic unsupervised methods, e.g. principal component analysis or hierarchical clustering. However, generic methods fail to exploit the significant amount of knowledge that exists about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that incorporates prior knowledge about gene functions in the form of gene ontology (GO) annotations. GO-PCA aims to discover and represent biological heterogeneity along all major axes of variation in a given dataset, while suppressing heterogeneity due to technical biases. To this end, GO-PCA combines principal component analysis (PCA) with nonparametric GO enrichment analysis, and uses the results to generate expression signatures based on small sets of functionally related genes. I first applied GO-PCA to expression data from diverse lineages of the human hematopoietic system, and obtained a small set of signatures that captured known cell characteristics for most lineages. I then applied the method to expression profiles of glioblastoma (GBM) tumor biopsies, and obtained signatures that were strongly associated with multiple previously described GBM subtypes. Surprisingly, GO-PCA discovered a cell cycle-related signature that exhibited significant differences between the Proneural and the prognostically favorable GBM CpG Island Methylator (G-CIMP) subtypes, suggesting that the G-CIMP subtype is characterized in part by lower mitotic activity. Previous expression-based classifications have failed to separate these subtypes, demonstrating that GO-PCA can detect heterogeneity that is missed by other methods. My results show that GO-PCA is a powerful and versatile expression-based method that facilitates exploration of large-scale expression data, without requiring additional types of experimental data. The low-dimensional representation generated by GO-PCA lends itself to interpretation, hypothesis generation, and further analysis.

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