Optimization of stress distribution in 2D structures using evolutionary optimization techniques
One of the most popular optimization algorithms used in engineering applications are based on gradient methods that where its effectiveness depends heavily on the starting point selected as this method utilizes gradient information to obtain an improved solution in the surrounding of the selected st...
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sg-ntu-dr.10356-456232023-03-04T19:44:03Z Optimization of stress distribution in 2D structures using evolutionary optimization techniques Pauh, Seng Wah. Ng Teng Yong School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering::Mechanics and dynamics One of the most popular optimization algorithms used in engineering applications are based on gradient methods that where its effectiveness depends heavily on the starting point selected as this method utilizes gradient information to obtain an improved solution in the surrounding of the selected starting point. Although some of these approaches have proven to be quite efficient, their application can be quite limited if the problem involved has more than one local optimum as the optimal solution obtained might not be the global optimum depending on the initial point selected. In this project, the genetic algorithm (GA) approach will be explored. This approach which holds a strong resemblance to the principles involved in the biological evolution process has been gaining substantial popularity due to its ability to identify the global optimum regardless of the initial point selected. By integrating the finite element analysis with the GA, the ability of GA in solving problems that require finite element analysis is investigated. Bachelor of Engineering (Aerospace Engineering) 2011-06-15T08:02:59Z 2011-06-15T08:02:59Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/45623 en Nanyang Technological University 102 p. application/pdf |
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DRNTU::Engineering::Mechanical engineering::Mechanics and dynamics Pauh, Seng Wah. Optimization of stress distribution in 2D structures using evolutionary optimization techniques |
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One of the most popular optimization algorithms used in engineering applications are based on gradient methods that where its effectiveness depends heavily on the starting point selected as this method utilizes gradient information to obtain an improved solution in the surrounding of the selected starting point. Although some of these approaches have proven to be quite efficient, their application can be quite limited if the problem involved has more than one local optimum as the optimal solution obtained might not be the global optimum depending on the initial point selected. In this project, the genetic algorithm (GA) approach will be explored. This approach which holds a strong resemblance to the principles involved in the biological evolution process has been gaining substantial popularity due to its ability to identify the global optimum regardless of the initial point selected. By integrating the finite element analysis with the GA, the ability of GA in solving problems that require finite element analysis is investigated. |
author2 |
Ng Teng Yong |
author_facet |
Ng Teng Yong Pauh, Seng Wah. |
format |
Final Year Project |
author |
Pauh, Seng Wah. |
author_sort |
Pauh, Seng Wah. |
title |
Optimization of stress distribution in 2D structures using evolutionary optimization techniques |
title_short |
Optimization of stress distribution in 2D structures using evolutionary optimization techniques |
title_full |
Optimization of stress distribution in 2D structures using evolutionary optimization techniques |
title_fullStr |
Optimization of stress distribution in 2D structures using evolutionary optimization techniques |
title_full_unstemmed |
Optimization of stress distribution in 2D structures using evolutionary optimization techniques |
title_sort |
optimization of stress distribution in 2d structures using evolutionary optimization techniques |
publishDate |
2011 |
url |
http://hdl.handle.net/10356/45623 |
_version_ |
1759856618000875520 |