Optimization Of Bar Linkage By Using Genetic Algorithms

This thesis presents the method of using simple Genetic Algorithms (GAs) in optimizing the size of bar linkage with discrete design variables and continues design variables. Penalty-based transformation method is used in this thesis to change the constrained problems into unconstrained ones. I...

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主要作者: Ramasamy, Mugilan
格式: Monograph
語言:English
出版: Universiti Sains Malaysia 2005
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spelling my.usm.eprints.58164 http://eprints.usm.my/58164/ Optimization Of Bar Linkage By Using Genetic Algorithms Ramasamy, Mugilan T Technology TJ Mechanical engineering and machinery This thesis presents the method of using simple Genetic Algorithms (GAs) in optimizing the size of bar linkage with discrete design variables and continues design variables. Penalty-based transformation method is used in this thesis to change the constrained problems into unconstrained ones. It is well known that GAs is best suited for unconstrained optimization problems. Optimization process will consider, lightest weight of the bar by using GAs without exceeding the allowable stress determined. Two types of problems are chosen to optimize in this thesis. First problem is optimization of four bar linkage under static loading condition and second problem is optimization of four bar linkage under dynamic condition without loading. The result from GA for this problem is compared with some other methods which are presented by V.V Tropov and V.L Markine.It is observed that GA gives better solution compared to other published methods. Universiti Sains Malaysia 2005-03-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/58164/1/Optimization%20Of%20Bar%20Linkage%20By%20Using%20Genetic%20Algorithms_Mugilan%20Ramasamy.pdf Ramasamy, Mugilan (2005) Optimization Of Bar Linkage By Using Genetic Algorithms. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Mekanikal. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TJ Mechanical engineering and machinery
spellingShingle T Technology
TJ Mechanical engineering and machinery
Ramasamy, Mugilan
Optimization Of Bar Linkage By Using Genetic Algorithms
description This thesis presents the method of using simple Genetic Algorithms (GAs) in optimizing the size of bar linkage with discrete design variables and continues design variables. Penalty-based transformation method is used in this thesis to change the constrained problems into unconstrained ones. It is well known that GAs is best suited for unconstrained optimization problems. Optimization process will consider, lightest weight of the bar by using GAs without exceeding the allowable stress determined. Two types of problems are chosen to optimize in this thesis. First problem is optimization of four bar linkage under static loading condition and second problem is optimization of four bar linkage under dynamic condition without loading. The result from GA for this problem is compared with some other methods which are presented by V.V Tropov and V.L Markine.It is observed that GA gives better solution compared to other published methods.
format Monograph
author Ramasamy, Mugilan
author_facet Ramasamy, Mugilan
author_sort Ramasamy, Mugilan
title Optimization Of Bar Linkage By Using Genetic Algorithms
title_short Optimization Of Bar Linkage By Using Genetic Algorithms
title_full Optimization Of Bar Linkage By Using Genetic Algorithms
title_fullStr Optimization Of Bar Linkage By Using Genetic Algorithms
title_full_unstemmed Optimization Of Bar Linkage By Using Genetic Algorithms
title_sort optimization of bar linkage by using genetic algorithms
publisher Universiti Sains Malaysia
publishDate 2005
url http://eprints.usm.my/58164/1/Optimization%20Of%20Bar%20Linkage%20By%20Using%20Genetic%20Algorithms_Mugilan%20Ramasamy.pdf
http://eprints.usm.my/58164/
_version_ 1765297655894769664
score 13.251813