A hierarchical genetic algorithm for reliability based design of geometrically non-linear composite structures

2001 ◽  
Vol 54 (1) ◽  
pp. 37-47 ◽  
Author(s):  
C.A. Conceição António
Keyword(s):  
Genetic Algorithm ◽  
Composite Structures ◽  
Reliability Based Design ◽  
Linear Composite ◽  
Non Linear ◽  
Hierarchical Genetic Algorithm

Behaviour of Axially Loaded Composite Wall Panel by Using Finite Element Analysis

2015 ◽  
Vol 815 ◽  
pp. 49-53
Author(s):  
Nur Fitriah Isa ◽  
Mohd Zulham Affandi Mohd Zahid ◽  
Liyana Ahmad Sofri ◽  
Norrazman Zaiha Zainol ◽  
Muhammad Azizi Azizan ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Composite Structures ◽  
Element Analysis ◽  
Steel Sheets ◽  
Linear Behavior ◽  
Non Linear ◽  
Composite Wall ◽  
Experimental Values ◽  
Civil Engineering Infrastructure

In order to promote the efficient use of composite materials in civil engineering infrastructure, effort is being directed at the development of design criteria for composite structures. Insofar as design with regard to behavior is concerned, it is well known that a key step is to investigate the influence of geometric differences on the non-linear behavior of the panels. One possible approach is to use the validated numerical model based on the non-linear finite element analysis (FEA). The validation of the composite panel’s element using Trim-deck and Span-deck steel sheets under axial load shows that the present results have very good agreement with experimental references. The developed finite element (FE) models are found to reasonably simulate load-displacement response, stress condition, giving percentage of differences below than 15% compared to the experimental values. Trim-deck design provides better axial resistance than Span-deck. More concrete in between due to larger area of contact is the factor that contributes to its resistance.

Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization

2020 ◽  
Vol 54 (2) ◽  
pp. 597-614
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Choquet Integral ◽  
Time Series Forecasting ◽  
Fuzzy Measure ◽  
Model Parameters ◽  
Forecasting Models ◽  
Non Linear

In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.

scholarly journals Hierarchical Genetic Algorithm for B-Spline Surface Approximation of Smooth Explicit Data

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
C. H. Garcia-Capulin ◽  
F. J. Cuevas ◽  
G. Trejo-Caballero ◽  
H. Rostro-Gonzalez
Keyword(s):  
Genetic Algorithm ◽  
Geometric Modeling ◽  
Data Set ◽  
Surface Approximation ◽  
B Spline ◽  
B Splines ◽  
Spline Surface ◽  
Spline Surfaces ◽  
Surface Dimension ◽  
Hierarchical Genetic Algorithm

B-spline surface approximation has been widely used in many applications such as CAD, medical imaging, reverse engineering, and geometric modeling. Given a data set of measures, the surface approximation aims to find a surface that optimally fits the data set. One of the main problems associated with surface approximation by B-splines is the adequate selection of the number and location of the knots, as well as the solution of the system of equations generated by tensor product spline surfaces. In this work, we use a hierarchical genetic algorithm (HGA) to tackle the B-spline surface approximation of smooth explicit data. The proposed approach is based on a novel hierarchical gene structure for the chromosomal representation, which allows us to determine the number and location of the knots for each surface dimension and the B-spline coefficients simultaneously. The method is fully based on genetic algorithms and does not require subjective parameters like smooth factor or knot locations to perform the solution. In order to validate the efficacy of the proposed approach, simulation results from several tests on smooth surfaces and comparison with a successful method have been included.

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