Optimization of TIG Welding Parameters Using a Hybrid Nelder Mead-Evolutionary Algorithms Method

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...

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Vydáno v:Journal of Manufacturing and Materials Processing Ročník 4; číslo 1; s. 10
Hlavní autoři: Kshirsagar, Rohit, Jones, Steve, Lawrence, Jonathan, Tabor, Jim
Médium: Journal Article
Jazyk:angličtina
Vydáno: MDPI AG 01.03.2020
Témata:
Algorithms
bead geometry optimization
Gas welding
genetic algorithm
Mathematical models
nelder-mead optimization
Optimization theory
particle swarm optimization
simulated annealing
tig welding
Iran
ISSN:2504-4494, 2504-4494
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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.
AbstractList 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. Keywords: genetic algorithm; simulated annealing; particle swarm optimization; Nelder-Mead optimization; TIG welding; bead geometry optimization
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.
Audience Academic
Author Tabor, Jim
Kshirsagar, Rohit
Lawrence, Jonathan
Jones, Steve
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SubjectTerms Algorithms
bead geometry optimization
Gas welding
genetic algorithm
Mathematical models
nelder-mead optimization
Optimization theory
particle swarm optimization
simulated annealing
tig welding
Title Optimization of TIG Welding Parameters Using a Hybrid Nelder Mead-Evolutionary Algorithms Method
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