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[
1
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.
P
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tead
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v
io
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.
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n
m
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ics,
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o
l
r
ep
r
esen
t
o
n
e
o
f
th
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c
h
i
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to
p
ics.
[
2
]
.
T
h
e
tu
n
i
n
g
p
r
o
ce
s
s
o
f
co
n
v
en
tio
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co
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tr
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f
ac
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s
m
aj
o
r
ch
all
en
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s
a
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d
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.
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n
d
ir
r
eg
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lar
o
p
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a
tio
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o
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es
[
3
]
.
R
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,
M
u
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E
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s
o
lv
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p
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[
4
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.
GA
an
d
DS
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ate
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ies
r
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s
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atter
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[
5
]
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m
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s
[
6
]
.
I
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1
9
7
5
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J
o
h
n
Ho
llan
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as
o
r
ig
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tech
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G
A
ar
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a
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a
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d
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lo
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s
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ch
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I
t r
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r
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at
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r
al
ev
o
lu
tio
n
p
r
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ce
s
s
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itatio
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[
7
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.
Ma
n
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f
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s
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th
e
co
n
tr
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DC
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to
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s
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e
to
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tag
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Fo
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P
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co
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l
alg
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r
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m
s
[
8
]
.
P
I
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h
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m
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T
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P
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co
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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P
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&
Dr
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S
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t
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Vo
l.
9
,
No
.
4
,
Dec
em
b
er
2
0
1
8
:
1467
–
1
4
7
5
1468
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s
s
y
s
te
m
s
.
I
n
t
h
is
ar
tic
le,
t
h
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
is
to
d
esi
g
n
o
u
tp
u
t
d
er
iv
ativ
e
co
n
tr
o
ller
-
b
ased
D
S
an
d
G
A
s
tr
ateg
ie
s
f
o
r
tu
n
i
n
g
th
e
co
n
tr
o
ll
er
p
ar
a
m
eter
s
.
T
h
e
m
o
tiv
atio
n
t
h
at
u
r
g
es
u
s
to
u
s
e
o
u
tp
u
t
d
er
i
v
ati
v
e
co
n
tr
o
ller
o
r
ig
in
ate
f
r
o
m
t
h
e
f
ac
t
t
h
at,
o
u
tp
u
t
d
er
iv
ati
v
e
co
n
tr
o
ller
d
o
esn
’
t
g
e
n
er
ate
p
lan
t
ze
r
o
also
d
o
esn
’
t
r
ed
u
ce
s
y
s
te
m
o
r
d
er
(
o
r
s
y
s
t
e
m
d
y
n
a
m
ics
i
n
cr
ea
s
e)
.
o
u
tp
u
t
d
er
iv
at
iv
e
co
n
tr
o
l
ler
g
e
n
er
ates
s
y
s
te
m
s
ti
f
f
est,
lo
w
er
s
en
s
iti
v
it
y
to
d
is
t
u
r
b
an
ce
.
Als
o
,
it
h
a
s
t
h
e
ef
f
ec
t
o
f
atten
u
atio
n
w
h
ich
lead
s
to
d
e
cr
ea
s
e
th
e
p
er
ce
n
t
o
v
er
s
h
o
o
t
an
d
en
h
an
ce
s
y
s
te
m
p
er
f
o
r
m
an
ce
.
DS
a
n
d
GA
s
tr
ateg
ie
s
ar
e
p
r
o
p
o
s
ed
f
o
r
c
o
n
tr
o
ller
p
ar
am
eter
s
tu
n
in
g
o
p
ti
m
al
l
y
.
T
h
ese
s
tr
ate
g
ies
h
av
e
v
ar
io
u
s
ad
v
a
n
ta
g
es
lik
e
s
i
m
p
le
s
ea
r
c
h
m
et
h
o
d
,
o
p
ti
m
al
s
ea
r
ch
ar
ea
s
ca
n
,
n
ee
d
lo
w
al
g
o
r
ith
m
p
ar
a
m
eter
s
an
d
av
o
id
lo
ca
l
o
p
tim
a
en
tr
ap
m
en
t.
T
h
ese
ad
v
an
ta
g
e
s
m
ak
e
t
h
e
m
g
o
o
d
ef
f
ec
tiv
e
o
p
ti
m
izatio
n
m
et
h
o
d
s
.
Gr
ea
test
n
u
m
b
er
o
f
r
ea
s
ea
r
c
h
e
r
s
g
i
v
e
atte
n
tio
n
to
tr
ad
itio
n
a
l
P
I
Ds
co
n
tr
o
ller
ap
p
licatio
n
i
n
d
c
m
o
to
r
co
n
tr
o
l
u
s
i
n
g
v
ar
io
u
s
o
p
ti
m
iz
atio
n
m
et
h
o
d
.
I
n
2
0
1
4
Sin
g
h
et.
A
l.,
[
10
]
p
r
esen
t
P
I
D
-
co
n
tr
o
ller
b
ased
GA
f
o
r
s
p
ee
d
co
n
tr
o
l
o
f
d
c
d
r
iv
e.
I
n
2
0
1
4
I
b
r
ah
i
m
et.
A
l.,
[
11
]
in
t
r
o
d
u
ce
An
t
C
o
lo
n
y
Op
ti
m
izat
io
n
(
A
C
O)
t
u
n
i
n
g
P
I
D
co
n
tr
o
ller
to
c
o
n
tr
o
l
s
ep
ar
ated
ex
cited
d
c
m
o
to
r
s
p
ee
d
.
I
n
2
0
1
4
I
b
r
ah
im
et.
A
l.,
[
12
]
p
r
o
p
o
s
ed
P
I
D
co
n
tr
o
ller
o
p
tim
ized
b
ased
B
ac
ter
ial
Fo
r
ag
in
g
(
B
F)
an
d
P
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
P
SO)
s
tr
ateg
ies
f
o
r
d
c
-
d
r
iv
e
s
p
ee
d
co
n
tr
o
l.
I
n
2
0
1
5
D
i
eg
o
et
.
A
l
.,
[
13
]
in
tr
o
d
u
ce
P
I
D
co
n
tr
o
ller
b
ased
AC
O
t
o
co
n
tr
o
l
th
e
m
o
to
r
s
p
ee
d
in
r
o
b
o
tic
ar
m
.
W
h
ile
i
n
2
0
1
6
Su
m
a
n
an
d
Gir
i
[
14
]
p
r
esen
t
G
A
m
eth
o
d
f
o
r
P
I
D
co
n
tr
o
ller
tu
n
in
g
f
o
r
s
p
ee
d
co
n
tr
o
l
o
f
d
c
m
o
to
r
.
I
n
2
0
1
6
A
b
d
u
la
m
ee
r
et.
A
l.,
[
15
]
p
r
o
p
o
s
e
co
n
v
en
tio
n
al
tu
n
i
n
g
m
et
h
o
d
s
f
o
r
P
I
D
co
n
tr
o
ller
tu
n
i
n
g
.
I
n
2
0
1
8
Sh
a
m
s
eld
i
n
et.
A
l.,
[
16
]
p
r
o
p
o
s
e
B
L
D
C
m
o
to
r
s
p
ee
d
co
n
tr
o
l
ar
r
an
g
e
m
e
n
t
u
s
in
g
n
o
n
li
n
ea
r
P
I
D
co
n
tr
o
ller
b
ased
GA
.
2.
P
M
DC
M
M
O
DE
L
P
MD
C
M
is
u
s
ed
in
v
ar
io
u
s
ap
p
licatio
n
s
d
u
e
to
its
ad
v
an
ta
g
es
li
k
e
s
af
et
y
,
g
o
o
d
s
tab
ilit
y
,
lo
w
e
co
s
t,
ea
s
y
co
n
tr
o
l
ar
r
an
g
e
m
e
n
t
r
eq
u
ir
ed
lo
w
v
o
lta
g
es
r
u
n
ab
ilit
y
.
T
h
e
P
MD
C
M
m
at
h
e
m
a
tical
m
o
d
el
is
co
m
es
f
r
o
m
its
elec
tr
ical
an
d
m
ec
h
a
n
ical
e
q
u
atio
n
s
(
s
ee
E
q
u
.
(
1
)
to
(
5
)
)
[
17
]
:
)
t
(
E
t
)
t
(
i
L
)
t
(
i
R
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t
(
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b
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t
K
t
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m
b
b
(2
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)
(
.
)
(
t
i
K
t
T
a
t
m
(
3
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k
K
K
b
t
(
4
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t
(
T
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t
(
B
t
)
t
(
J
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t
(
T
L
m
m
m
m
m
d
d
(5
)
E
b
is
th
e
r
o
to
r
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m
f
(
V)
.
R
a
is
r
o
to
r
r
esis
tan
ce
)
(
.
V
a
is
r
o
to
r
v
o
ltag
e
(
v
o
lt).
i
a
is
th
e
r
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r
cu
r
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a
m
p
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b
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co
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t o
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m
f
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s
/r
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ta
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to
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N.
m
/
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)
.
J
: m
o
m
en
t o
f
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er
tia
(
k
g
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m
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L
a
: r
o
to
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m
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icie
n
t o
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co
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s
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r
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/r
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m
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ec
h
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(
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m
)
.
ω
m
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m
o
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r
s
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d
(
r
a
d
/s
).
T
L
is
th
e
o
u
tp
u
t
-
to
r
q
u
e
(
N.
m
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J
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&
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1469
3.
O
UT
P
UT
D
E
R
I
VAT
I
V
E
C
O
NT
RO
L
L
E
R
Ou
tp
u
t
d
er
iv
ati
v
e
co
n
tr
o
ller
c
an
b
e
i
m
p
le
m
e
n
ted
b
y
in
ter
n
all
y
f
ee
d
i
n
g
b
ac
k
t
h
e
p
la
n
t
o
u
tp
u
t
s
ig
n
al
d
er
iv
ativ
e.
T
h
is
ca
n
b
e
d
o
n
e
u
s
i
n
g
tac
h
o
g
e
n
er
ato
r
.
T
h
en
c
o
m
p
ar
i
n
g
t
h
e
i
n
ter
n
all
y
d
er
iv
ativ
e
s
i
g
n
a
l
w
it
h
a
p
r
o
p
o
r
tio
n
al
er
r
o
r
s
ig
n
al
(
Fi
g
u
r
e1
)
[
18
]
.
T
h
is
co
n
tr
o
ller
ty
p
e
d
o
esn
’
t
g
e
n
er
ate
p
lan
t
ze
r
o
w
h
ile
s
o
m
e
o
th
er
co
n
tr
o
ller
s
d
o
th
is
.
So
t
h
is
co
n
tr
o
ller
d
o
esn
’
t
i
n
cr
e
ase
th
e
s
y
s
te
m
o
v
er
s
h
o
o
t.
T
h
is
m
ea
n
s
t
h
e
co
n
tr
o
ller
en
h
a
n
ce
t
h
e
s
y
s
te
m
d
am
p
i
n
g
[
19
]
.
1.
Fig
u
r
e
1
.
Ou
tp
u
t d
er
iv
at
iv
e
co
n
tr
o
ller
s
ch
e
m
e
[
20
]
4.
DS A
ND
G
A
O
P
T
I
M
I
Z
A
T
I
O
N
T
E
CH
N
I
Q
UE
S
DS
m
et
h
o
d
is
o
n
e
o
f
th
e
m
o
s
t
i
m
p
o
r
tan
t
o
p
ti
m
iza
tio
n
m
et
h
o
d
s
.
I
t
h
as
th
e
ab
ilit
y
f
o
r
f
u
n
ct
io
n
m
i
n
i
m
izatio
n
b
y
co
m
p
ar
is
io
n
m
et
h
o
d
.
I
t
d
o
es
n
o
t
r
eq
u
ir
e
an
y
d
er
iv
at
iv
e
in
f
o
r
m
a
tio
n
.
As
w
ell
as,
it
d
o
es
n
o
t
b
u
ild
d
er
iv
ativ
e
ap
p
r
o
x
i
m
atio
n
.
P
atter
n
s
ea
r
ch
(
P
S)
m
et
h
o
d
s
ca
n
b
e
r
eg
ar
d
ed
as
a
DS
tec
h
n
iq
u
e
[
21
]
.
P
S
is
s
u
itab
le
f
o
r
s
o
lv
i
n
g
v
ar
io
u
s
o
p
ti
m
izatio
n
p
r
o
b
le
m
s
th
a
t
lie
o
u
ts
id
e
th
e
s
ea
r
c
h
.
I
t
h
as
s
i
m
p
le
co
n
ce
p
t,
ea
s
y
i
m
p
le
m
en
t
io
n
a
n
d
h
av
e
e
f
f
ic
i
en
t
co
m
p
u
tat
io
n
p
r
o
ce
s
s
.
PS
h
as
g
o
o
d
b
alan
ce
d
o
p
er
ato
r
f
o
r
i
m
p
r
o
v
in
g
a
n
d
ad
j
u
s
tin
g
t
h
e
g
lo
b
al
lo
ca
l
s
ea
r
ch
ar
ea
.
T
h
is
m
et
h
o
d
b
eg
i
n
s
b
y
g
e
n
er
ati
n
g
p
o
in
t
s
s
et
n
a
m
e
d
m
es
h
,
s
u
r
r
o
u
n
d
in
g
th
e
c
u
r
r
en
t
p
o
in
t.
T
h
i
s
p
o
in
t
co
u
ld
b
e
t
h
e
p
o
in
t
o
f
i
n
itial
s
tar
tin
g
t
h
at
g
i
v
e
n
b
y
t
h
e
o
p
er
ato
r
o
r
it
co
u
ld
b
e
ca
lcu
lated
f
r
o
m
t
h
e
p
r
ec
ed
in
g
alg
o
r
ith
m
s
tep
.
B
y
ad
d
in
g
p
atter
n
(
s
et
o
f
v
ec
to
r
s
)
to
th
e
cu
r
r
en
t
p
o
in
t,
th
e
m
e
s
h
is
f
o
r
m
ed
.
T
h
e
alg
o
r
ith
m
f
lo
wch
ar
t is iil
u
s
tr
ated
in
Fi
g
u
r
e2
[
22
]
.
R
e
a
d
D
a
t
a
S
e
t
S
t
a
r
t
i
n
g
P
o
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t
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t
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M
e
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o
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v
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O
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t
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F
u
n
c
t
i
o
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s
O
n
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o
f
t
h
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S
t
o
p
p
i
n
g
C
r
i
t
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r
i
a
O
c
c
u
r
r
e
d
?
I
s
O
.
F
.
o
f
M
e
s
h
P
o
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t
s
L
e
s
s
O
.
F
.
X
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e
d
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D
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M
e
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h
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i
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S
t
o
p
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s
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Fig
u
r
e
2
.
P
S a
lg
o
r
ith
m
f
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w
c
h
ar
t
GA
is
a
n
e
x
p
lo
r
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tec
h
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iq
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e
e
m
u
late
s
t
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at
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al
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n
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s
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t
i
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v
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l
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o
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th
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ee
s
tep
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i.e
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,
r
ep
r
o
d
u
ctio
n
,
cr
o
s
s
o
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er
,
an
d
m
u
tatio
n
[
23
]
.
I
t
is
a
p
o
w
er
f
u
l
w
ell
-
k
n
o
w
n
r
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d
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tio
n
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r
c
h
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et
h
o
d
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h
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e
ar
e
th
r
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m
e
n
tar
y
d
if
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er
en
ce
b
et
w
ee
n
G
A
an
d
o
t
h
er
tr
ad
itio
n
al
o
p
ti
m
izat
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n
tec
h
n
iq
u
es.
First,
G
A
w
o
r
k
s
o
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th
e
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ar
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m
eter
s
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p
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h
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o
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tain
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Seco
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e
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ilit
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to
Evaluation Warning : The document was created with Spire.PDF for Python.
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d
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ltan
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h
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t
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r
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m
ak
e
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A
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r
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ts
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tr
ap
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t.
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d
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n
ee
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p
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u
s
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ata
f
r
o
m
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r
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lem
s
ea
r
ch
ar
ea
.
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t
is
ele
m
e
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tar
y
to
co
m
p
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te
o
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j
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tiv
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l
y
.
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A
h
a
v
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h
e
ab
ilit
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to
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o
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e
b
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th
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tr
ain
ed
a
n
d
u
n
co
n
s
tr
ai
n
ed
p
r
o
b
lem
s
[
24
]
.
GA
w
er
e
o
r
ig
i
n
all
y
p
r
o
p
o
s
ed
b
y
J
o
h
n
Ho
llan
d
(
1
9
7
5
)
.
GA
alg
o
r
ith
m
s
ta
g
e
s
ar
e
illu
s
tr
ated
as f
o
llo
w
s
:
1
)
Def
in
e
P
o
p
u
latio
n
Size
I
n
th
i
s
s
tep
th
e
al
g
o
r
ith
m
i
n
iti
al
p
o
p
u
latio
n
is
g
e
n
er
ated
.
I
t is n
o
r
m
all
y
ab
o
u
t 2
0
-
1
0
0
in
d
iv
id
u
als.
2
)
R
ep
r
o
d
u
ctio
n
T
h
is
s
tep
s
p
ec
if
ie
s
th
e
r
eg
e
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er
atio
n
o
f
th
e
n
ex
t
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p
u
latio
n
.
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h
e
p
r
o
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ab
ilit
y
o
f
ch
r
o
m
o
s
o
m
e
to
s
u
r
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iv
e
r
elate
d
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its
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it
n
es
s
v
alu
e.
W
h
er
e
f
it
n
e
s
s
is
c
h
r
o
m
o
s
o
m
e
s
u
itab
ilit
y
s
u
r
v
iv
e
m
ea
s
u
r
e
[
25
]
.
Du
r
in
g
t
h
e
r
ep
r
o
d
u
ctio
n
s
tep
,
th
e
ch
r
o
m
o
s
o
m
e
f
i
tn
e
s
s
v
a
lu
e
is
es
ti
m
ate
d
.
T
h
e
co
s
t
f
u
n
ct
io
n
v
al
u
e
is
u
s
ed
in
th
e
s
elec
tio
n
s
tag
e
to
g
i
v
e
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ias
to
w
ar
d
s
ap
p
r
o
p
r
iate
ch
r
o
m
o
s
o
m
e.
I
t
is
s
i
m
ilar
to
ev
o
lu
tio
n
p
r
o
ce
s
s
in
n
a
tu
r
e,
a
f
i
t
ch
r
o
m
o
s
o
m
e
h
as a
h
i
g
h
er
s
ele
ctio
n
p
r
o
b
ab
ilit
y
a
n
d
ca
n
b
e
s
e
lecte
d
f
o
r
r
ep
r
o
d
u
ctio
n
s
tep
.
3
)
C
r
o
s
s
o
v
er
T
h
e
s
tep
co
m
es a
f
ter
s
elec
t
io
n
s
tep
.
Ne
w
i
n
d
iv
id
u
las i
s
g
e
n
e
r
ated
b
y
p
air
in
g
p
r
ev
io
u
s
i
n
d
i
v
id
u
al
s
.
4
)
Mu
tatio
n
I
t
is
a
s
to
ch
a
s
tic
p
r
o
ce
s
s
i
n
n
atu
r
al
e
v
o
lu
tio
n
.
I
t
h
a
v
e
t
h
e
ab
ilit
y
to
p
r
o
d
u
ce
n
e
w
i
n
d
iv
id
u
al
s
b
y
ch
an
g
i
n
g
a
p
ar
t o
f
i
n
d
iv
id
u
al
g
en
e
[
7
]
.
GA
s
tep
s
ar
e
clar
if
ie
d
in
Fig
u
r
e.
3
.
I
n
i
t
i
a
l
i
ze
p
o
p
u
l
a
t
i
o
n
M
e
a
s
u
r
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f
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t
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e
s
s
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l
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t
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o
n
M
u
t
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t
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n
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r
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r
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t
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t
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t
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O
p
t
i
m
u
m
s
o
l
u
t
i
o
n
Fig
u
r
e
3
.
GA
f
lo
w
ch
ar
t
[
25
]
5.
RE
SU
L
T
S
T
h
e
P
MD
C
M
p
ar
am
eter
s
ar
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0
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=
0
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5
P
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s
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b
ased
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m
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s
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e
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2
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.
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to
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e
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tiv
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ller
.
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co
n
tr
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p
ar
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eter
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tu
n
ed
b
ased
DS
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d
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A
s
tr
ate
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ie
s
.
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h
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n
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ased
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if
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t
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a
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Tech
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h
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p
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n
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r
e:
1)
J
I
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(
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n
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r
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r
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.
2)
J
I
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3)
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1
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ased
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ased
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(
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(
b
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(
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(
d
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Fig
u
r
e
4
.
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to
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h
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ized
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ased
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e
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izat
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n
m
eth
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d
s
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ased
(
a)
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(
b
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c)
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d
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1.4
Tim
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(s)
Ampli
tude
DS-JI
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Tim
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Ampli
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3
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4
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