ดาวน์โหลด All Proceeding - AS Nida

and large-size problems. For small-size problem, the inverse mutation (IM) was the best.
Table 3 Relative performance of crossover operators in each dataset.
No.
Crossover
operators
M10P3 M20P5 M15P9 M30P10
Mean STD Mean STD Mean STD Mean STD
1 AEX 190.98 5.13 1392.90 54.07 1434.43 26.20 4955.93 119.04
2 CX 196.32 9.27 1365.90 60.04 1421.09 32.75 4834.39 175.30
3 DX 194.87 6.58 1376.14 44.16 1426.53 28.68 4960.76 104.14
4 ER 201.30 13.64 1420.37 46.04 1464.28 38.30 4957.95 109.29
5 EERX 193.18 7.02 1378.10 43.21 1434.24 24.92 4885.18 104.14
6 IPX 189.29 5.60 1321.90 46.45 1386.56 34.21 4768.79 128.72
7 LOX 188.28 2.09 1363.12 45.16 1398.62 29.32 4876.70 104.98
8 MPX 188.03 1.75 1339.74 48.73 1386.72 35.99 4814.12 130.41
9 1PX 190.20 6.15 1373.12 57.59 1411.05 42.15 4832.64 130.94
10 OX 188.60 2.82 1370.66 49.59 1401.60 28.18 4916.63 104.33
11 PMX 189.61 4.91 1326.39 50.21 1393.36 33.69 4742.06 115.66
12 PBX 188.23 2.89 1324.06 48.52 1393.15 41.93 4698.11 125.64
13 SCX 190.28 5.24 1344.57 53.52 1399.47 34.35 4753.41 138.54
14 2PCX 187.94 2.68 1326.67 47.77 1385.58 28.85 4687.40 127.55
15 2PEX 188.95 3.90 1353.59 58.64 1414.82 44.70 4796.12 156.25
16 2PECX 188.48 3.01 1318.07 47.76 1380.73 35.33 4715.41 152.31
Table 4 Relative performance of mutation operators in each dataset.
No.
Mutation
operators
M10P3 M20P5 M15P9 M30P10
Mean STD Mean STD Mean STD Mean STD
1 CIM 195.80 11.66 1406.70 59.82 1436.51 48.65 4928.57 180.86
2 E2ORS 192.25 8.04 1349.76 44.52 1420.54 38.54 4795.55 123.60
3 IM 188.36 3.02 1343.94 50.55 1407.67 36.24 4803.68 151.57
4 SOM 189.16 3.88 1354.76 51.45 1412.19 39.63 4818.91 133.43
5 3OAS 190.73 5.51 1368.23 57.49 1412.07 33.76 4872.61 142.87
6 3ORS 191.07 5.89 1341.73 48.64 1403.83 33.38 4765.45 136.23
7 2OAS 191.20 6.63 1364.96 51.56 1421.32 42.40 4883.68 127.93
8 2ORS 188.69 3.40 1317.57 51.86 1396.82 31.90 4729.35 146.67
Table 5 Ranking of crossover and mutation operations.
Dataset
Rank of crossover operator
1
Rank of mutation operator
st 2 nd 3 rd 1 st 2 nd 3 rd
M10P3 2PCX MPX PBX IM 2ORS SOM
M20P5 2PECX IPX PBX 2ORS 3ORS IM
M15P9 2PECX 2PCX IPX 2ORS 3ORS IM
M30P10 2PCX PBX 2PECX 2ORS 3ORS E2ORS
The main effect plots (fitted mean) in figure 5 and 6 show
the appropriate crossover and mutation operators for each dataset,
which differ from the previous work [38], in which the best operators
256
were EERX and 2OAS. Each crossover and mutation operator performs
a different mechanism on the balancing between exploitation and
exploration. The worst crossover and mutation operators were ER and
CIM for all datasets. The interaction of both operators as shown in
figure 7 indicated similar results. The suggested best genetic operators
from both main effect plots and interaction plots were different. From
the main effect plots, the best crossover and mutation for M10P3 and
M30P10 were 2PCX/IM and 2PCX/ORS, respectively, while
interaction plots suggested that the best operators were PBX/CIM and
SCX/2ORS, respectively. The student’s t-test was applied to test the