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Thresholds for accepting PSMs generated by each algorithm were set consistent with an FDR of 1%.
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The numbers of clusters found by each algorithm are placed between parentheses.
The optimal objective function values attained by each algorithm is shown in Table 2.
Thus, a number of the final non-dominated solutions found by each algorithm are counted.
The total power allocated by each algorithm is shown in Fig. 4. It is observed from the figure, the curves of power consumed by each algorithm are roughly flat except the PSO algorithm.
To evaluate this question the solutions produced by each algorithm are considered and evaluated according to the corresponding fitness values.
The MC scores, NMI scores and the number of clusters found by each algorithm are reported in Table 3.
Finally, the results obtained by each algorithm are averaged over 30 runs using the stratified 10-fold cross-validation process.
Hence, the number of final non-dominated solutions obtained by each algorithm is important to calculate this metric (Bandyopadhyay et al. 2004).
In case 3, the result comparison of FEs' cost by each algorithm is similar to that in case 2. That is, PSO consumes the least FEs among the four algorithms, though its allocation solution is the worst; PADE costs less FEs than DE, ABC, and jDE; its solution is the best compared with PSO, DE, jDE, and ABC.
Both performance metrics provided by each algorithm are evaluated in terms of of the direct BS-UT link, while the of the other links are equal to, or greater with a constant value than the current value of of the direct link.
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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