Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm
| Autor: | R. Venkata Rao, Dhiraj P. Rai |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Engineering Optimization problem business.industry Mechanical Engineering Process (computing) Imperialist competitive algorithm Particle swarm optimization 02 engineering and technology Submerged arc welding 020901 industrial engineering & automation Mechanics of Materials Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Multi-swarm optimization business Algorithm Selection (genetic algorithm) |
| Zdroj: | Journal of Mechanical Science and Technology. 31:2513-2522 |
| ISSN: | 1976-3824 1738-494X |
| Popis: | Submerged arc welding (SAW) is characterized as a multi-input process. Selection of optimum combination of process parameters of SAW process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to optimize the SAW process parameters using a simple optimization algorithm, which is fast, robust and convenient. Therefore, in this work a very recently proposed optimization algorithm named Jaya algorithm is applied to solve the optimization problems in SAW process. In addition, a modified version of Jaya algorithm with oppositional based learning, named “Quasi-oppositional based Jaya algorithm” (QO-Jaya) is proposed in order to improve the performance of the Jaya algorithm. Three optimization case studies are considered and the results obtained by Jaya algorithm and QO-Jaya algorithm are compared with the results obtained by well-known optimization algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Imperialist competitive algorithm (ICA) and Teaching learning based optimization (TLBO). |
| Databáze: | OpenAIRE |
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