Optimization of TIG welding parameters using a hybrid Nelder Mead-evolutionary algorithms method

Rohit Kshirsagar*, Steve Jones, Jonathan Lawrence, Jim Tabor*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
4 Downloads (Pure)


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 languageEnglish
Article number10
Number of pages21
JournalJournal of Manufacturing and Materials Processing
Issue number1
Publication statusPublished - 10 Feb 2020
Externally publishedYes


  • bead geometry optimization
  • genetic algorithm
  • Nelder-Mead optimization
  • particle swarm optimization
  • simulated annealing
  • TIG welding


Dive into the research topics of 'Optimization of TIG welding parameters using a hybrid Nelder Mead-evolutionary algorithms method'. Together they form a unique fingerprint.

Cite this