Handling of Constraints in Efficient Global Optimization

2020 ◽  
pp. 2050033
Author(s):  
Hu Wang ◽  
Wei Hu ◽  
Enying Li
Keyword(s):  
Global Optimization ◽  
Variable Stiffness ◽  
Kriging Model ◽  
Superior Performance ◽  
Efficient Global Optimization ◽  
Feasible Region ◽  
Support Vector ◽  
Multiple Constraint ◽  
Fiber Path ◽  
Feasible Points

Although the Efficient Global Optimization (EGO) algorithm has been widely used in multi-disciplinary optimization, it is still difficult to handle multiple constraint problems. In this study, to increase the accuracy of approximation, the Least Squares Support Vector Regression (LSSVR) is suggested to replace the kriging model for approximating both objective and constrained functions while the variances of these surrogate models are still obtained by kriging. To enhance the ability to search the feasible region, two criteria are suggested. First, a Maximize Probability of Feasibility (MPF) strategy to handle the infeasible initial sample points is suggested to generate feasible points. Second, a Multi-Constraint Parallel (MCP) criterion is suggested for multiple constraints handling, parallel computation and validation, respectively. To illustrate the efficiency of the suggested EGO-based method, several deterministic benchmarks are tested and the suggested methods demonstrate a superior performance compared with two other constrained algorithms. Finally, the suggested algorithm is successfully utilized to optimize the fiber path of variable-stiffness beam and lightweight B-pillar to demonstrate the performance for engineering applications.

AN ASSESSMENT OF THE PERFORMANCE OF GLOBAL OPTIMIZATION METHODS FOR GEO-ACOUSTIC INVERSION

2008 ◽  
Vol 16 (02) ◽  
pp. 199-223 ◽  
Author(s):  
MIRJAM SNELLEN ◽  
DICK G. SIMONS
Keyword(s):  
Global Optimization ◽  
Optimization Methods ◽  
Simplex Algorithm ◽  
Optimization Techniques ◽  
Acoustic Properties ◽  
Superior Performance ◽  
Efficient Global Optimization ◽  
Acoustic Inversion ◽  
Global Optimization Methods ◽  
Downhill Simplex

Having available efficient global optimization methods is of high importance when going to a practical application of geo-acoustic inversion, where fast processing of the data is an essential requirement. A series of global optimization techniques are available and have been described in literature. In this paper three optimization techniques are considered, being a genetic algorithm (GA), differential evolution (DE), and the downhill simplex algorithm (DHS). The performance of these three methods is assessed using a test function, demonstrating superior performance of DE. Additionally, the DE optimal setting is determined. As a next step DE is applied for determining the geo-acoustic properties of the upper seabed sediments from simulated seabed reflection loss, indicating good DE performance also for real geo-acoustic inversion problems.

Constrained efficient global optimization with support vector machines

2012 ◽  
Vol 46 (2) ◽  
pp. 201-221 ◽  
Author(s):  
Anirban Basudhar ◽  
Christoph Dribusch ◽  
Sylvain Lacaze ◽  
Samy Missoum
Keyword(s):  
Global Optimization ◽  
Support Vector Machines ◽  
Efficient Global Optimization ◽  
Support Vector ◽  
Vector Machines

Protein Structural Class Prediction Based on Distance-related Statistical Features from Graphical Representation of Predicted Secondary Structure

2019 ◽  
Vol 16 (4) ◽  
pp. 317-324
Author(s):  
Liang Kong ◽  
Lichao Zhang ◽  
Xiaodong Han ◽  
Jinfeng Lv
Keyword(s):  
Feature Extraction ◽  
Secondary Structure ◽  
Protein Sequence ◽  
Function Analysis ◽  
Superior Performance ◽  
Support Vector ◽  
Chaos Game Representation ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class

Protein structural class prediction is beneficial to protein structure and function analysis. Exploring good feature representation is a key step for this prediction task. Prior works have demonstrated the effectiveness of the secondary structure based feature extraction methods especially for lowsimilarity protein sequences. However, the prediction accuracies still remain limited. To explore the potential of secondary structure information, a novel feature extraction method based on a generalized chaos game representation of predicted secondary structure is proposed. Each protein sequence is converted into a 20-dimensional distance-related statistical feature vector to characterize the distribution of secondary structure elements and segments. The feature vectors are then fed into a support vector machine classifier to predict the protein structural class. Our experiments on three widely used lowsimilarity benchmark datasets (25PDB, 1189 and 640) show that the proposed method achieves superior performance to the state-of-the-art methods. It is anticipated that our method could be extended to other graphical representations of protein sequence and be helpful in future protein research.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

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