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Experimental studies show the superiority of JGDE over other competitive algorithms in both convergence and diversity.
The observer is validated on industrial data and shows good performance in both convergence rate and accuracy.
Simulated on three examples, the NrABC algorithm presents good performance in both convergence process and solution quality.
The results show that the GB-RRT∗ performs better than RRT∗ in both convergence and reliability toward achieving the minimum dose path.
The study on the performance for typical benchmark functions shows that the DNA-GA outperforms the other two methods in both convergence speed and accuracy.
The results demonstrate that the HABC outperforms other methods (BIPOA, PSOPS and two improved GAs) in both convergence speed and accuracy.
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ANOVA applied on the group mean peak velocity of vergence shows statistically significant effect of the group for both cases with (F1,32 = 41.82, p<0.001) and without (F1,32 = 59.41, p<0.001) coinciding saccades, i.e. the peak velocity of vergence is lower in tinnitus than in controls for both convergence and divergence.
However, within the MCMC framework several problems in terms of both convergence and computational time occur in practical applications.
IGD is a popular metric for measuring the quality of a solution set in terms of both convergence and distribution.
Some promising results are reported in this study, indicating that the proposed SOFNN-AGA performs better favorably in terms of both convergence speed and modeling accuracy.
The results show that the proposed MOPSO (MOPSO+EPD) significantly outperforms MOPSO on the majority of test functions in terms of both convergence and coverage.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com