scholarly journals CONSTRUCTION PROCESS OPTIMIZATION FOR TRUSS STRUCTURES BY GENETIC ALGORITHMS

1998 ◽  
Vol 63 (508) ◽  
pp. 87-92 ◽  
Author(s):  
Yoshinobu KANEKO ◽  
Tomomi KANEMITSU ◽  
Kazuo MITSUI ◽  
Nobuyoshi TOSAKA ◽  
Yasuhiko HANGAI
Keyword(s):  
Genetic Algorithms ◽  
Process Optimization ◽  
Truss Structures ◽  
Construction Process

Practical Design Optimization of Real Life Truss Structures Constructed From Basic Modules Using the Genetic Algorithms

Volume 3 ◽  
2004 ◽  
Author(s):  
Aurellio Dominguez-Gonzalez ◽  
Ramin Sedaghati ◽  
Ion Stiharu
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Penalty Function ◽  
Slenderness Ratio ◽  
Real Life ◽  
Truss Structures ◽  
Cross Sectional ◽  
Space Truss ◽  
Stress Maximum ◽  
The Cost

Truss structures are widely employed in the industrialized world. They appear as bridges, towers, roof supports, building exoskeletons or high technology light space structures. This paper investigates the simultaneous size, geometry and topology optimization of real life large truss structures using Genetic Algorithms (GAs) as optimizer and Finite Element Method as analyzer. In general the large truss structures are constructed from the duplication of some basic modules called bays. Thus, the final optimum design may be reached by optimizing the characteristics of the basic bays instead of optimizing the whole structure. Both single and multi-objective functions based on the mass of the structure and the maximum nodal displacement, have been considered as the cost functions. In order to have realistic optimal designs, the cross-sectional areas have been extracted from the standard profiles according to AISC codes and practical conditions are imposed to the bays. The design optimization problem is also constrained by the maximum stress, maximum slenderness ratio and the maximum and minimum cross-sectional area of the truss members. To accommodate all these constraints, two different penalty functions are proposed. The first penalty function considers the normalization of violated constraints with respect to the allowable stress or slenderness ratio. The second penalty function is a constant function, which is used to penalize the violations of the slenderness ratio. Two illustrative examples of realistic planar and space truss structures have been optimized to demonstrate the effectiveness of the proposed methodology.

OPTIMAL PLACEMENT OF TUNING MASSES ON TRUSS STRUCTURES BY GENETIC ALGORITHMS

1993 ◽  
Author(s):  
ERIC PONSLET ◽  
RAPHAEL HAFTKA ◽  
HARLEY CUDNEY
Keyword(s):  
Genetic Algorithms ◽  
Optimal Placement ◽  
Truss Structures

Applying Soft Computing to Clinical Decision Support

2016 ◽  
pp. 256-271
Author(s):  
José Machado ◽  
Lucas Oliveira ◽  
Luís Barreiro ◽  
Serafim Pinto ◽  
Ana Coimbra
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Programming Languages ◽  
Soft Computing ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Construction Process ◽  
Python Programming

This article aims to explain the construction process of the learing systems based on Artificial Neural Networks and Genetic Algorithms. These systems were implemented using R and Python programming languages, in order to compare results and achieve the best solution and it was used Diabetes and Parkinson datasets with the purpose of identifying the carriers of these diseases.

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