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.
| Original language | English |
|---|---|
| Article number | 4010010 |
| Number of pages | 22 |
| Journal | Journal of Manufacturing and Materials Processing |
| Volume | 4 |
| Issue number | 1 |
| Early online date | 10 Feb 2020 |
| DOIs | |
| Publication status | Published - 1 Mar 2020 |
Bibliographical note
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedKeywords
- Bead geometry optimization
- Genetic algorithm
- Nelder-Mead optimization
- Particle swarm optimization
- Simulated annealing
- TIG welding
ASJC Scopus subject areas
- Mechanics of Materials
- Mechanical Engineering
- Industrial and Manufacturing Engineering