Research Article
Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment
Table 2
Tuning of metaheuristics.
| Metaheuristic | Parameters | Levels | Tuned value | ā1 | 0 | +1 |
| GA | Population size | 100 | 150 | 200 | 200 | Maximum number of iterations | 300 | 500 | 700 | 500 | Rate of mutation | 0.05 | 0.15 | 0.25 | 0.15 | Rate of crossover | 0.6 | 0.7 | 0.8 | 0.8 |
| KA | Population size | 100 | 150 | 200 | 100 | Maximum number of iterations | 300 | 500 | 700 | 300 | Percentage of N1 | 0.1 | 0.2 | 0.3 | 0.1 | Percentage of N2 | 0.4 | 0.5 | 0.6 | 0.6 | Maximum number of swirlings | 5 | 10 | 15 | 10 |
| RDA | Population size | 100 | 150 | 200 | 150 | Maximum number of iterations | 300 | 500 | 700 | 700 | Number of males | 15 | 25 | 30 | 25 | Alpha | 0.5 | 0.6 | 0.7 | 0.6 | Beta | 0.7 | 0.8 | 0.9 | 0.7 | Gamma | 0.8 | 0.9 | 1 | 0.8 |
| H-RDKGA | Population size | 100 | 150 | 200 | 150 | Maximum number of iterations | 300 | 500 | 700 | 500 | Number of males | 15 | 25 | 30 | 30 | Maximum number of swirlings | 5 | 10 | 15 | 15 |
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