IEEE Computational Intelligence Magazine - August 2022 - 61

NSGA-II
MOEA/D
1
0.8
0.6
0.4
0.5
1
Experiment
NPIs
FIGURE 3 Hyper volume convergence.
GLMONSGA IIand
GLMONSGA III.
Finally,
it is visible that NSGA - II performs
similarly to the corresponding
GLMO implementation which uses
NSGA - II internally.
B. Comparison
The experiment results are compared
with the work in [7], which optimizes
only two policies. The result is indicated
as E ..sd
l
+ld
in Table IV. For this experiment,
identical initial parameters for
both the policies and optimization were
used. The definition of policies as outlined
in [7] remains unchanged, and the
optimizer operates on the trigger times.
However, the optimizer is adjusted to
respect the same three objectives as presented
in this paper. Figure 5 visualizes
the comparison between ENPIPIECON++
and E .sd
+ld . The proposed approach
offers a multitude of possible containment
strategies, while [7] only provides
solutions on a linear scale without variations
in the measures' strength. Evidently,
the rigid approach of [7] limits the
possibilities for optimization, as the
obtained solutions are limited in performance
regarding the objectives and the
coverage of the objective space itself.
C. Influence of Parameters
Due to their inherent nature, certain
model parameters are more influential
than others, e.g., cr or tp. The potential of
strategies is further guided by the limits
1
0.5
1
0.5
0.5
f1′
1
MOEA/D
GLMO (NSGA-II)
0.5
f2′
1
NSGA-II
GLMO (NSGA-III)
FIGURE 4 The scatter plots visualize the aggregated Pareto fronts over 31 runs.
0.5
f3′
1
for each parameter during the optimization
(see Table II). For this reason, the
discussion concentrates mainly on the
general dynamics illustrated by the pan1
0.5
demic
model. The parameters with high
influence are detected using a correlation
coefficient and regression analysis
(see Figure 6) and by looking at the
0.5
NPIs + ECON
1
NPIs + PIs
0.5
1
NPIs + PIs + ECON
0.5
1
GLMO (NSGA-III)
GLMO (NSGA-II)
AUGUST 2022 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 61
Median (HV)
f3′
f2′
f1′

IEEE Computational Intelligence Magazine - August 2022

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