Exact(6)
From Figure9, we can see that for a network size, the performance of Algorithm 1 becomes better as γmax increases.
Furthermore, given a Δ down step size, the performance of the eOLLA does not vary with the traffic load.
Once the scatterer becomes more than several hundred wavelengths in size, the performance of the algorithm of this paper deteriorates significantly.
Comparing the results for population sizes: 8 and 512 (Figure 9(b) and Figure 10(b)), we can see that with the increase of the population size, the performance of WMN is increased and the mesh routers can cover more mesh clients.
The evolutionary potential of a population was not related to its size, the performance of the population or its neutral genetic diversity.
For each sample size, the performance of each strategy (M, H) was compared to a pure random strategy by comparing the average score of 30 100 core collections sampled independently.
Similar(53)
At some node sizes, the performance of "Edge-range" is almost identical to our proposed hybrid method.
For large sample sizes, the performance of RDBC and B-RDBC with penalty P 2 is compatible with MW criterion.
Through two illustrative problems of different sizes the performance of the MAI algorithm has been compared with most recent methods.
As expected, for large sample sizes, the performance of the proposed estimators and that of the MLEs are very close in terms of the average bias and RMSE.
First of all, note that for high Δ down sizes the performance of both OLLA techniques is degraded, independently of the traffic load.
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