Information Technology Reference
In-Depth Information
ˀ
(
xxxx
+
)(
+
)
g
=
0.0357
5
6
8
9
−
1
−
0.04
g
=+ −
2
x
(
x
+
x
) sin
1
2
7
5
7
xx
58
1
ˀ
xx
−
g
=+ −
2
x
(
x
+
x
) sin
g
=
659130
6
5
−
1100
4
3
10
8
10
22
63 511
xxxxn
2
7455600
(
xxxx
−
)(
+
)
g
=
659130
9
8
6
5
−
1100
g
=
−
525
5
22
948 252
6
xxxxxn
xxxn
2
3511
7455600(
x xxx
+
xx
)
xx
g
=
6
5
−
525
g
=−
0.8
36
7
8
2
45822
xx
49
xx
g
=
36
−
1.2
9
xx
49
hx xx
=+ −
2
hx xx
=+
2
−
10
5
7
6
11
8
10
9
The optimization problem is stated using the state variables X and the constraint
conditions based on functions (6) (7) as follows:
22
2
22
2
Min
.
fX
(
)
=
0.8754(
xxx nx
(
+
+
9
x
−
30.2)
+
xxx nx
(
+
+
9
x
−
30.2))
1
1
3
5
1
7
6
2
4
8
2
10
9
121
1 21
+−
+ −
xxx
xx x
Min
.
f
(
X
)
=−
(1
−
0.1178
x
5
7
6
)
×
(1
−
0.1178
x
8
10
9
)
2
6
9
xx
+
xx
+
(8)
5
6
8
9
St g X for i
hX for j
X xxxxxxxxx
. .
(
),
=
=
1, 2,..., 9
i
( ,
10,11
j
10
]
T
=
[
x
1
2
3
4
5
6
7
8
9
It is noted that for the second objective function a minus sign is added to convert
the original maximization objective to the minimization objective.
4.2
Multiobjective Optimization Results
A population of 100 solutions was allowed to evolve over 500 generations. The same
parameters were used for both MOEA/D-CDP and MOEA/D-CDP-ID. In this case
study, The experimental results of using MOEA/D-CDP-ID for optimizing the two-
stage planetary gear transmission system are also provided. Figure 4 shows the final
Pareto front obtained in a typical run, which indicates that MOEA/D-CDP-ID outper-
forms the MOEA/D-CDP.
Search WWH ::
Custom Search