Kshirsagar, R., Jones, S. orcid.org/0000-0003-0957-6375, Lawrence, J. et al. (1 more author) (2020) Optimization of TIG welding parameters using a hybrid nelder mead-evolutionary algorithms method. Journal of Manufacturing and Materials Processing, 4 (1). 10.
Abstract
A number of evolutionary algorithms such as genetic algorithms, simulated annealing, particle swarm optimization, etc., have been used by researchers in order to optimize different manufacturing processes. In many cases these algorithms are either incapable of reaching global minimum or the time and computational effort (function evaluations) required makes the application of these algorithms impractical. However, if the Nelder Mead optimization method is applied to approximate solutions cheaply obtained from these algorithms, the solution can be further refined to obtain near global minimum of a given error function within only a few additional function evaluations. The initial solutions (vertices) required for the application of Nelder-Mead optimization can be obtained through multiple evolutionary algorithms. The results obtained using this hybrid method are better than that obtained from individual algorithms and also show a significant reduction in the computation effort.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | genetic algorithm; simulated annealing; particle swarm optimization; Nelder-Mead optimization; TIG welding; bead geometry optimization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > Nuclear Advanced Manufacturing Research Centre |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 May 2020 12:38 |
Last Modified: | 04 May 2020 12:38 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/jmmp4010010 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160155 |