Two-stage based ensemble optimization framework for large-scale global optimization

2013 ◽  
Vol 228 (2) ◽  
pp. 308-320 ◽  
Author(s):  
Yu Wang ◽  
Jin Huang ◽  
Wei Shan Dong ◽  
Jun Chi Yan ◽  
Chun Hua Tian ◽  
...  
Keyword(s):  
Global Optimization ◽  
Large Scale ◽  
Two Stage ◽  
Optimization Framework ◽  
Ensemble Optimization

scholarly journals Automatic Construction and Global Optimization of a Multisentiment Lexicon

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoping Yang ◽  
Zhongxia Zhang ◽  
Zhongqiu Zhang ◽  
Yuting Mo ◽  
Lianbei Li ◽  
...  
Keyword(s):  
Global Optimization ◽  
Large Scale ◽  
Language Model ◽  
Superior Performance ◽  
Optimization Framework ◽  
Test Intensity ◽  
Sentiment Lexicon ◽  
Classification Tasks ◽  
Network Language ◽  
Accurate Quantification

Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010). This paper implements statistical training for large-scale Chinese corpus through neural network language model and proposes an automatic method of constructing a multidimensional sentiment lexicon based on constraints of coordinate offset. In order to distinguish the sentiment polarities of those words which may express either positive or negative meanings in different contexts, we further present a sentiment disambiguation algorithm to increase the flexibility of our lexicon. Lastly, we present a global optimization framework that provides a unified way to combine several human-annotated resources for learning our 10-dimensional sentiment lexicon SentiRuc. Experiments show the superior performance of SentiRuc lexicon in category labeling test, intensity labeling test, and sentiment classification tasks. It is worth mentioning that, in intensity label test, SentiRuc outperforms the second place by 21 percent.

scholarly journals Cooperative Coevolution with Two-Stage Decomposition for Large-Scale Global Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
H. D. Yue ◽  
Y. Sun
Keyword(s):  
Global Optimization ◽  
Decomposition Method ◽  
Large Scale ◽  
Optimization Problems ◽  
Decomposition Methods ◽  
Unit Vector ◽  
Cooperative Coevolution ◽  
Two Stage ◽  
Computational Resources ◽  
Dynamic Decomposition

Cooperative coevolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems. However, CC with static decomposition method is ineffective for fully nonseparable problems, and CC with dynamic decomposition method to decompose problems is computationally costly. Therefore, a two-stage decomposition (TSD) method is proposed in this paper to decompose LSGO problems using as few computational resources as possible. In the first stage, to decompose problems using low computational resources, a hybrid-pool differential grouping (HPDG) method is proposed, which contains a hybrid-pool-based detection structure (HPDS) and a unit vector-based perturbation (UVP) strategy. In the second stage, to decompose the fully nonseparable problems, a known information-based dynamic decomposition (KIDD) method is proposed. Analytical methods are used to demonstrate that HPDG has lower decomposition complexity compared to state-of-the-art static decomposition methods. Experiments show that CC with TSD is a competitive algorithm for solving LSGO problems.

scholarly journals Model reduction based global optimization for large-scale steady state nonlinear systems

2021 ◽  
Author(s):  
Min Tao ◽  
Panagiotis Petsagkourakis ◽  
Jie Li ◽  
Constantinos Theodoropoulos
Keyword(s):  
Neural Networks ◽  
Global Optimization ◽  
Model Reduction ◽  
Large Scale ◽  
Principal Component ◽  
Activation Function ◽  
Illustrative Case ◽  
Piecewise Affine ◽  
Optimization Framework ◽  
Global Optimal

Many engineering processes can be accurately modeled using partial differential equations (PDEs), but high dimensionality and non-convexity of the resulting systems pose limitations on their efficient optimization. In this work, a model reduction methodology combining principal component analysis (PCA) and artificial neural networks (ANNs) is employed to construct a reduced surrogate model, which is then utilized by advanced deterministic global optimization algorithms to compute global optimal solutions with theoretical guarantees. However, the optimization framework is still time-consuming due to the high non-convexity of the activation functions inside the reduced ANN structure. To further enhance the capability of our optimization framework, two alternative strategies have been proposed. The first one is a piecewise-affine reformulation while the second one is based on deep rectifier neural networks with ReLU activation function. The performances of the two improved frameworks is demonstrated through two illustrative case studies.

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