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ITS » Scientific Articles » LPPM
Posted by harun@ce.its.ac.id at 22/06/2016 16:21:41  •  521 Views


MULTI OBJECTIVE TWO DIMENSIONAL TRUSS OPTIMIZATION BY USING GENETIC ALGORITHM

Author :
ALRASYID, HARUN ( 1983080820081210005 )
AJI, PUJO




ABSTRAK

During last three decade many mathematical programming methods have been develop for solving optimization problems. However no single method has been found to be entirely efficient and robust for the wide range of engineering optimization problems. Most design application in civil engineering involve selecting values for a set of design variables that best describe the behavior and performance of the particular problem while satisfying the requirements and specifications imposed by codes of practice. The introduction of Genetic Algorithm GA into the field of structural optimization has opened new avenues for research because they have been successful applied while traditional methods have failed. GAs is efficient and broadly applicable global search procedure based on stochastic approach which relies on survival of the fittest strategy. GAs are search algorithms that are based on the concepts of natural selection and natural genetics. On this research Multi-objective sizing and configuration optimization of the two-dimensional truss has been conducted using a genetic algorithm. Some preliminary runs of the GA were conducted to determine the best combinations of GA parameters such as population size and probability of mutation so as to get better scaling for rest of the runs. Comparing the results from sizing and sizing configuration optimization can obtained a significant reduction in the weight and deflection. Sizingconfiguration optimization produces lighter weight and small displacement than sizing optimization. The results were obtained by using a GA with relative ease computationally and these results are very competitive compared to those obtained from other methods of truss optimization.



Keywordstruss optimization; genetic algorithm; multi-objective optimization.
 
Subject:  algoritma genetika
Date Create: 01/05/2011
Type: Text
Format: PDF
Language: English
Identifier: ITS-Article-10005160010102
Collection ID: 10005160010102
Call Number: 005.1 Alr m


Source
article of IPTEK, The Journal for Technology and Science, Vol. 22, No. 2, May 2011

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ITS Community

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IPTEK, The Journal for Technology and Science, Vol. 22, No. 2, May 2011




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ITS-Article-10005160010102-41688.pdf




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