IEEE Computational Intelligence Magazine - November 2020 - 70

Each algorithm runs 30 times with different random
seeds. Fig. 4 presents the resulting objective function values
obtained by the six algor ithms over the 30 runs. As the
weights in the objective function (12) are normalized, the

objective function value represents the average index of
drugs selected from the alternative sets. On the three
instances in Hangzhou, the median objective function values
of the WWO algorithm are 1.83, 2.71, and 0.39, respectively,

6

4

5

3
f (x)

f (x)

4
2

3
2

1
1
0

0
GA

BBO

DE

CS

GWO

WWO

GA

BBO

DE

GWO

WWO

CS

GWO

WWO

(b)

(a)
3

2

1.5

2
f (x)

f (x)

CS

1

1
0.5

0

0
GA

BBO

DE

CS

GWO

WWO

GA

BBO

(c)

DE
(d)

2

f (x)

1.5

1

0.5

0

GA

BBO

DE

CS

GWO

WWO

(e)
Q1

Min Median

Max

Q3

FIGURE 4 Comparison of the resulting objective function values obtained by the algorithms for prevention program optimization. Each box plot
shows the maximum, minimum, median, first quartile (Q1), and third quartile (Q3) of objective function values over the 30 runs of an algorithm.
(a) Hangzhou, 1st round. (b) Hangzhou, 2nd round. (c) Hangzhou, 3rd round. (d) Shaoxing, 1st round, and (e) Shaoxing, 2nd round.

70

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2020



IEEE Computational Intelligence Magazine - November 2020

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