Performance comparisons between these algorithms are presented in Figs. 4 and 5.
However, even though NSGA-II is better in some scenarios and MOCell in others, the magnitude of the differences in time between these algorithms was small.
The main differences between these algorithms are digital or analog implementation, simplicity of the design, sensor requirements, convergence speed, range of effectiveness, as well as hardware costs.
This affirmation is based on the fact that the differences between these algorithms are in the optimization process, always aiming at a shorter processing time and lower use of resources, such as computational memory.
Therefore, some testing and comparisons between these algorithms are strongly required.
Other groups have had success with various text-processing algorithms in identifying RA cases and have even found these algorithms are portable between settings [ 18, 19].
These algorithms are an hybrid variant between CS and RB strategies.
An example of this is seen in Figure 3, where the difference in convergence speed between the algorithms is large.
Another difference between the algorithms is the requirement that the test tree be rooted.
A further notable difference between the algorithms is that LUO outputs much larger peripheries for each cluster.
