scholarly journals Truss’ topology and shape optimization by genetic algorithms

2021 ◽  
Vol 47 ◽  
Author(s):  
Dmitrij Šešok ◽  
Paulius Ragauskas
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Numerical Solutions ◽  
Search Algorithm ◽  
Truss Topology ◽  
Modified Genetic Algorithm ◽  
Topology And Shape Optimization ◽  
System Characteristics

In the paper the global optimization problem of truss systems is studied.  The genetic algorithms are employed for the optimization. As the objective function the structure mass is treated; the constraints include equilibrium, local stability and other requirements.  All the truss system characteristics needed for genetic algorithm are obtained via finite element solution. Topology optimization of truss system is performed using original modified genetic algorithm, while the shape optimization – using ordinary genetic algorithm. Numerical solutions are presented. The obtained solutions are compared with global extremes obtained using full search algorithm.  All the numerical examples are solved using original software.

scholarly journals Mixed genetic algorithm for optimization of trusses

2008 ◽  
Vol 48 ◽  
Author(s):  
Dmitrij Šešok
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Finite Elements ◽  
Shape Optimization ◽  
Objective Function ◽  
Objective Function Value ◽  
Finite Elements Method ◽  
Topology And Shape Optimization ◽  
Mixed Genetic Algorithm

In this paper two strategies of optimization are compared: sequential and synchronous topology and shape optimization of trusses. Genetic algorithms are used for optimization. A task of optimization of truss withtwelve possible nodes is solved. Finite elements method is used to calculate an objective function value. Software used in calculations was created by the author.

scholarly journals GENETIC ALGORITHMS FOR MULTIDIMENSIONAL SCALING / GENETINIŲ ALGORITMŲ TAIKYMAS DAUGIAMATĖMS SKALĖMS

2015 ◽  
Vol 7 (3) ◽  
pp. 275-279 ◽  
Author(s):  
Agnė Dzidolikaitė
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Multidimensional Scaling ◽  
Optimization Problem ◽  
Dimensional Space ◽  
Local Solution ◽  
Multidimensional Data ◽  
Global Optimization Problem ◽  
Lower Dimensional Space ◽  
Lower Dimensional

The paper analyzes global optimization problem. In order to solve this problem multidimensional scaling algorithm is combined with genetic algorithm. Using multidimensional scaling we search for multidimensional data projections in a lower-dimensional space and try to keep dissimilarities of the set that we analyze. Using genetic algorithms we can get more than one local solution, but the whole population of optimal points. Different optimal points give different images. Looking at several multidimensional data images an expert can notice some qualities of given multidimensional data. In the paper genetic algorithm is applied for multidimensional scaling and glass data is visualized, and certain qualities are noticed. Analizuojamas globaliojo optimizavimo uždavinys. Jis apibrėžiamas kaip netiesinės tolydžiųjų kintamųjų tikslo funkcijos optimizavimas leistinojoje srityje. Optimizuojant taikomi įvairūs algoritmai. Paprastai taikant tikslius algoritmus randamas tikslus sprendinys, tačiau tai gali trukti labai ilgai. Dažnai norima gauti gerą sprendinį per priimtiną laiko tarpą. Tokiu atveju galimi kiti – euristiniai, algoritmai, kitaip dar vadinami euristikomis. Viena iš euristikų yra genetiniai algoritmai, kopijuojantys gyvojoje gamtoje vykstančią evoliuciją. Sudarant algoritmus naudojami evoliuciniai operatoriai: paveldimumas, mutacija, selekcija ir rekombinacija. Taikant genetinius algoritmus galima rasti pakankamai gerus sprendinius tų uždavinių, kuriems nėra tikslių algoritmų. Genetiniai algoritmai taip pat taikytini vizualizuojant duomenis daugiamačių skalių metodu. Taikant daugiamates skales ieškoma daugiamačių duomenų projekcijų mažesnio skaičiaus matmenų erdvėje siekiant išsaugoti analizuojamos aibės panašumus arba skirtingumus. Taikant genetinius algoritmus gaunamas ne vienas lokalusis sprendinys, o visa optimumų populiacija. Skirtingi optimumai atitinka skirtingus vaizdus. Matydamas kelis daugiamačių duomenų variantus, ekspertas gali įžvelgti daugiau daugiamačių duomenų savybių. Straipsnyje genetinis algoritmas pritaikytas daugiamatėms skalėms. Parodoma, kad daugiamačių skalių algoritmą galima kombinuoti su genetiniu algoritmu ir panaudoti daugiamačiams duomenims vizualizuoti.

Data Modeling Using NURBs Curves and Modified Genetic Algorithms

2010 ◽  
Author(s):  
Christopher Hammond ◽  
Cameron J. Turner
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trial Data ◽  
Data Sets ◽  
Control Points ◽  
Nurbs Curve ◽  
B Splines ◽  
Modified Genetic Algorithm ◽  
Nurbs Curves ◽  
2D Data

Non-Uniform Rational B-Splines (NURBS) curves have long been used to model 1D and 2D data because they are efficient to calculate, easy to manipulate, and capable of handling discontinuities and drastic changes in the general topology of the data. However, the user must assist in defining the control points, weights, knots and an order for the curve in order to fit the curve to the data. This paper uses a modified Genetic Algorithm (GA) to choose and manipulate these various parameters to produce a NURBS curve that minimizes the error between the data and the curve and also minimizes the time it takes the algorithm to compute the solution. The algorithm is tested on several 1D trial data sets and the results are explained. Then, several general difficulties for this application of the GA are explained and possible methods for overcoming them are discussed.

Optimization of Radial Cams with Translating Roller Follower through Genetic Algorithms

2015 ◽  
Vol 783 ◽  
pp. 83-94
Author(s):  
Alberto Borboni
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Contact Pressure ◽  
Optimization Problem ◽  
Optimization Method ◽  
Optimization Process ◽  
Radius Of Curvature ◽  
Design Problems ◽  
Computational Optimization ◽  
Different Types

In this work, the optimization problem is studied for a planar cam which rotates around its axis and moves a centered translating roller follower. The proposed optimization method is a genetic algorithm. The paper deals with different design problems: the minimization of the pressure angle, the maximization of the radius of curvature and the minimization of the contact pressure. Different types of motion laws are tested to found the most suitable for the computational optimization process.

Optimization of Multi-Modal Discrete Functions Using Genetic Algorithms

1993 ◽  
Vol 207 (1) ◽  
pp. 53-59 ◽  
Author(s):  
D T Pham ◽  
Y Yang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Approximate Solutions ◽  
Solution Set ◽  
Preliminary Design ◽  
Potential Solution ◽  
Reproduction Cycle ◽  
Fitness Sharing ◽  
Discrete Functions

Four techniques are described which can help a genetic algorithm to locate multiple approximate solutions to a multi-modal optimization problem. These techniques are: fitness sharing, ‘eliminating’ identical solutions, ‘removing’ acceptable solutions from the reproduction cycle and applying heuristics to improve sub-standard solutions. Essentially, all of these techniques operate by encouraging genetic variety in the potential solution set. The preliminary design of a gearbox is presented as an example to illustrate the effectiveness of the proposed techniques.

Optimization of Contact Problem by Genetic Algorithm Method

2011 ◽  
Vol 105-107 ◽  
pp. 386-391 ◽  
Author(s):  
Jan Szweda ◽  
Zdenek Poruba
Keyword(s):  
Genetic Algorithm ◽  
Contact Problem ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Optimization Technique ◽  
Contact Problems ◽  
Global Optimum ◽  
Genetic Algorithm Method ◽  
Characteristic Features ◽  
Contact Shape

In this paper is discussed the way of suitable numerical solution of contact shape optimization problem. The first part of the paper is focused on method of global optimization field among which the genetic algorithm is chosen for computer processing and for application on contact problem optimization. The brief description of this method is done with emphasis of its characteristic features. The experiment performed on plane structural problem validates the ability of genetic algorithm in search the area of the global optimum. On the base of the research described in this work, it is possible to recommend optimization technique of genetic algorithm to use for shape optimization of engineering contact problems in which it is possible for any shape to achieve successful convergence of contact task solution.

scholarly journals Reliable Shape Optimization of Structures Subjected to Transient Dynamic Loading Using Genetic Algorithms

2005 ◽  
Vol 12 (6) ◽  
pp. 407-424 ◽  
Author(s):  
Sabyasachi Chand ◽  
Anjan Dutta
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Genetic Algorithms ◽  
Shape Optimization ◽  
Analysis Tool ◽  
Time Step ◽  
Transient Dynamic ◽  
Boundary Definition ◽  
Automatic Mesh ◽  
Shape Changes

This paper presents a reliable method of solution of two dimensional shape optimization problems subjected to transient dynamic loads using Genetic Algorithms. Boundary curves undergoing shape changes have been represented by B-splines. Automatic mesh generation and adaptive finite element analysis modules are integrated with Genetic algorithm code to carry out the shape optimization. Both space and time discretization errors are evaluated and appropriate finite element mesh and time step values as obtained iteratively are adopted for accurate dynamic response. Two demonstration problems have been solved, which show convergence to the optimal solution with number of generations. The boundary curve undergoing shape optimization shows smooth shape changes. The combinations of automatic mesh generator with proper boundary definition capabilities, analysis tool with error estimation and Genetic algorithm as optimization engine have been observed to behave as a satisfactory shape optimization environment to deal with real engineering problems.

scholarly journals Framework for the Shape Optimization of Aerodynamic Profiles Using Genetic Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
D. López ◽  
C. Angulo ◽  
I. Fernández de Bustos ◽  
V. García
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Genetic Algorithms ◽  
Shape Optimization ◽  
Computational Fluid Dynamics Cfd

This study developed a framework for the shape optimization of aerodynamics profiles using computational fluid dynamics (CFD) and genetic algorithms. A genetic algorithm code and a commercial CFD code were integrated to develop a CFD shape optimization tool. The results obtained demonstrated the effectiveness of the developed tool. The shape optimization of airfoils was studied using different strategies to demonstrate the capacity of this tool with different GA parameter combinations.

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