Mean normalized optimization results in fourteen benchmark functions. The values shown are the minimum objective function values found by each algorithm, averaged over 100 Monte Carlo simulations.
ABC
ACO
BA
CS
DE
ES
GA
HS
KH
LKH
PBIL
PSO
F01
5.26
6.20
7.81
6.84
4.85
7.54
6.70
7.74
1.85
1.00
7.84
6.53
F02
2.63
10.01
14.42
6.82
3.80
9.77
4.25
9.42
3.67
1.00
9.02
8.27
F03
31.49
10.22
182.64
61.09
17.19
78.85
32.57
155.54
4.59
1.00
176.91
64.57
F04
1.00
F05
1.00
F06
1.00
F07
1.22
2.30
3.42
2.64
1.99
3.16
2.04
2.89
1.25
1.00
3.18
2.34
F08
15.83
85.99
89.29
25.57
13.40
110.42
23.00
75.23
5.34
1.00
90.29
26.64
F09
1.77
1.11
3.93
2.86
2.24
2.73
1.00
3.33
2.10
1.93
3.47
3.35
F10
51.56
43.79
123.64
29.15
68.30
72.56
48.30
66.46
33.17
1.00
75.49
51.20
F11
1.00
2.84
4.57
2.79
1.23
4.32
2.19
3.52
1.50
4.22
3.51
2.58
F12
14.65
9.13
15.73
11.05
11.98
14.52
12.31
14.91
2.46
1.00
15.61
12.52
F13
851.62
1.00
F14
205.78
95.69
427.88
103.81
700.63
227.85
29.20
1.00
411.03
Time
2.40
3.22
1.08
2.02
1.95
2.03
2.38
2.77
4.66
4.30
1.00
2.37
Total
1
0
0
0
0
0
1
0
0
12
0
0
values are normalized so that the minimum in each row is 1.00. These are not the absolute minima found by each algorithm, but the average minima found by each algorithm.
