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=
(
H
−
U
)
•
100
Δ
(3.10)
U
where
H
represents the obtained value and
U
is the reported optimal. For the Car and
Hel set of problems, EDE easily obtains the optimal values, and on average around 1%
above the optimal for the reC instances.
Table 3.33.
FSS comparison
DDE
GA
EDE
%
%
%
DDE
−
GA
EDE
−
DDE EDE
−
GA
F5x10
79.4
-
78
-
101.79
-
F8x15
138.6
143
134
103.17
103.43
106.71
F10x25
207.6
205
194
98.74
107.01
105.67
F15x25
257.6
248
240
96.27
107.33
103.33
F20x50
474.8
468
433
98.56
109.65
108.08
F25x75
715.4
673
647
94.07
110.57
104.01
F 30 x 100
900.4
861
809
95.62
111.29
106.42
Ho Chang
213
213
213
100
100
100
Table 3.34.
Comparison of FSS instances
Instance
Size
Optimal
EDE
% to Opti-
mal
Car 1
11 x 5
7038
7038
0
Car 2
13 x 4
7166
7166
0
Car 3
12 x 5
7312
7312
0
Car 4
14 x 4
8003
8003
0
Car 5
10 x 6
7720
7720
0
Car 6
8 x 9
8505
8505
0
Car 7
7 x 7
6590
6590
0
Car 8
8 x 8
8366
8366
0
Hel 2
20 x 10
135
135
0
reC 01
20 x 5
1247
1249
0.16
reC 03
20 x 5
1109
1111
0.18
reC 05
20 x 5
1242
1249
0.56
reC 07
20 x 10
1566
1584
1.14
reC 09
20 x 10
1537
1574
2.4
reC 11
20 x 10
1431
1464
2.3
reC 13
20 x 15
1930
1957
1.39
reC 15
20 x 15
1950
1984
1.74
reC 17
20 x 15
1902
1957
2.89
reC 19
30 x 10
2093
2132
1.86
reC 21
30 x 10
2017
2065
2.37
reC 23
30 x 10
2011
2073
3.08
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