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I
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10
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5
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2
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5261
5252
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s
ch
ed
u
le
an
d
g
ai
n
-
p
h
a
s
e
m
ar
g
in
m
et
h
o
d
s
d
o
n
o
t
p
r
o
v
id
e
b
etter
r
esu
lts
f
o
r
h
ig
h
-
o
r
d
er
n
o
n
-
li
n
ea
r
co
n
tr
o
l
s
y
s
te
m
s
.
T
h
er
ef
o
r
e,
th
e
P
S
O
w
a
s
p
r
o
p
o
s
ed
to
o
v
er
co
m
e
t
h
e
li
m
itatio
n
.
T
h
e
r
esu
lts
o
b
tai
n
ed
f
r
o
m
th
e
f
r
eq
u
e
n
c
y
a
n
d
tr
an
s
ie
n
t
s
tab
ilit
y
r
esp
o
n
s
e
s
o
f
t
h
e
P
SO
tu
n
ed
P
I
co
n
tr
o
ller
f
o
r
A
G
C
w
er
e
b
etter
th
a
n
th
e
R
o
o
t
L
o
cu
s
an
d
Z
ie
g
ler
-
Nich
o
ls
m
eth
o
d
s
.
I
n
r
ef
.
[
7
]
,
th
e
g
ain
s
o
f
th
e
P
I
co
n
tr
o
ller
w
er
e
t
u
n
ed
u
s
in
g
th
e
P
SO,
w
h
er
e
t
h
e
co
n
tr
o
l
s
y
s
te
m
co
n
s
id
er
ed
w
a
s
a
P
I
co
n
tr
o
ller
ca
s
ca
d
ed
w
it
h
a
g
en
er
al
p
lan
t.
O
n
e
o
f
th
e
li
m
i
tatio
n
s
o
f
clas
s
ical
P
S
O,
it
m
a
y
co
n
v
er
g
e
in
a
lo
ca
l
o
p
tim
u
m
,
lead
i
n
g
to
s
tag
n
atio
n
o
f
it
s
s
w
ar
m
[
7
]
,
th
er
ef
o
r
e,
t
h
e
m
u
lti
-
ep
o
ch
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
w
a
s
p
r
o
p
o
s
ed
.
A
ls
o
,
t
h
e
P
SO
ca
n
co
n
v
er
g
e
i
n
a
g
lo
b
al
o
p
tim
u
m
o
r
u
n
e
x
p
ec
ted
l
y
i
n
to
a
lo
ca
l
o
p
ti
m
u
m
[
8
]
,
b
ec
au
s
e
o
f
li
m
itat
io
n
i
n
it
s
co
n
v
er
g
e
n
ce
,
an
d
th
i
s
a
f
f
ec
ts
its
ab
ilit
y
to
ef
f
ec
ti
v
el
y
r
e
g
u
l
ate
th
e
v
e
lo
cities
a
n
d
d
ir
ec
tio
n
s
o
f
i
ts
p
ar
ticle
s
[
9
,
1
0
]
.
T
h
e
g
ai
n
s
o
f
th
e
P
I
D
C
o
n
tr
o
ller
o
f
t
h
e
A
GC
co
n
n
ec
ted
to
th
er
m
al
P
lan
t
w
er
e
t
u
n
e
d
u
s
in
g
GW
O
to
o
p
ti
m
ize
t
h
e
w
ei
g
h
t
p
ar
a
m
eter
s
ap
p
lied
in
th
e
co
n
tr
o
ller
[
1
1
]
.
W
h
ile
th
e
au
t
h
o
r
s
i
n
[
1
2
]
tu
n
ed
t
h
e
P
I
D
c
o
n
tr
o
ller
f
o
r
Fra
ctio
n
al
-
o
r
d
er
Sp
h
er
ical
T
an
k
s
y
s
te
m
u
s
i
n
g
GW
O.
T
h
e
g
ain
p
ar
am
e
ter
s
o
f
th
e
Fra
ctio
n
al
Or
d
er
P
I
D
c
o
n
tr
o
ller
w
er
e
t
u
n
ed
u
s
i
n
g
G
W
O
w
h
er
e
th
e
i
n
teg
r
al
ti
m
e
m
u
ltip
lied
a
b
s
o
lu
te
o
f
er
r
o
r
(
I
T
A
E
)
o
f
th
e
s
y
s
te
m
f
r
eq
u
e
n
c
y
d
ev
iatio
n
s
o
f
t
w
o
ar
ea
s
a
n
d
th
e
tie
-
li
n
e
p
o
w
er
d
ev
iatio
n
w
er
e
m
in
i
m
ized
[
1
3
]
.
T
h
e
s
im
u
latio
n
r
es
u
lt
s
o
b
tain
ed
in
[
1
3
]
f
r
o
m
th
e
GW
O
tu
n
in
g
m
et
h
o
d
w
er
e
b
etter
th
an
th
e
r
esu
lts
o
b
tain
ed
u
s
i
n
g
th
e
Z
ie
g
ler
Nich
o
l
s
tu
n
i
n
g
te
ch
n
iq
u
e.
T
h
e
f
u
zz
y
P
I
D
co
n
tr
o
ller
w
it
h
f
il
ter
(
Fu
zz
y
-
P
I
DF)
w
a
s
d
esi
g
n
ed
an
d
tu
n
ed
u
s
i
n
g
GW
O
in
[
1
4
]
f
o
r
co
n
tr
o
llin
g
th
e
tie
-
li
n
e
p
o
w
er
d
ev
iatio
n
a
n
d
s
y
s
te
m
f
r
eq
u
e
n
c
y
d
ev
iatio
n
in
t
w
o
ar
ea
s
.
T
h
e
d
esig
n
ed
co
n
tr
o
l
ler
p
er
f
o
r
m
ed
b
etter
th
an
t
h
e
GW
O
t
u
n
ed
P
I
D
co
n
tr
o
ller
in
ter
m
s
o
f
t
h
e
m
i
n
i
m
ized
o
b
j
ec
tiv
e
f
u
n
cti
o
n
(
I
T
E
A
)
,
s
y
s
te
m
f
r
eq
u
en
c
y
a
n
d
tie
-
l
in
e
p
o
w
er
d
ev
iatio
n
s
.
T
h
e
g
ai
n
s
o
f
th
e
P
I
D
co
n
tr
o
ller
in
t
h
e
D
C
m
o
to
r
s
p
ee
d
co
n
tr
o
l
w
er
e
o
p
tim
ized
u
s
in
g
GW
O,
a
n
d
it
p
er
f
o
r
m
ed
b
etter
t
h
an
P
SO,
Z
ie
g
ler
Nic
h
o
ls
a
n
d
ar
tif
icia
l
b
ee
co
lo
n
y
(
A
B
C
)
o
p
tim
izatio
n
tec
h
n
iq
u
es i
n
ter
m
s
o
f
tr
an
s
ien
t r
esp
o
n
s
e
[
1
5
]
.
T
h
e
P
I
g
ain
s
o
f
th
e
v
o
lta
g
e
s
o
u
r
ce
co
n
v
er
ter
(
V
SC
)
w
er
e
t
u
n
ed
o
n
li
n
e
u
s
in
g
th
e
n
e
u
r
o
n
-
p
r
o
g
r
am
m
i
n
g
m
et
h
o
d
[
1
6
]
.
Su
b
s
eq
u
e
n
tl
y
,
t
h
e
VSC
w
a
s
ap
p
lied
to
co
n
tr
o
l
t
h
e
r
ea
cti
v
e
p
o
w
er
,
b
u
s
v
o
ltag
e,
an
d
s
y
s
te
m
f
r
eq
u
e
n
c
y
in
a
m
ic
r
o
-
g
r
id
.
T
h
e
r
esu
lt
o
b
tain
ed
f
r
o
m
th
e
o
n
li
n
e
t
u
n
in
g
m
et
h
o
d
w
a
s
co
m
p
ar
ed
w
it
h
th
e
u
n
-
t
u
n
ed
P
I
co
n
tr
o
ller
.
T
h
e
o
n
lin
e
t
u
n
in
g
i
m
p
r
o
v
ed
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
co
n
tr
o
ller
i
n
tr
an
s
ien
t r
esp
o
n
s
e
an
d
s
ettli
n
g
ti
m
e.
B
u
t,
it is
co
m
p
lex
a
n
d
r
eq
u
ir
es
m
o
r
e
co
m
p
u
tin
g
r
eso
u
r
ce
s
.
T
h
e
p
itch
a
n
g
le
P
I
co
n
tr
o
lle
r
w
a
s
t
u
n
ed
i
n
[
1
7
]
u
s
in
g
g
r
o
u
p
g
r
e
y
w
o
l
f
o
p
ti
m
izer
(
G
GW
O)
f
o
r
co
n
tr
o
llin
g
t
h
e
d
o
u
b
l
y
f
ed
i
n
d
u
ctio
n
g
e
n
er
ato
r
(
DFI
G)
.
T
h
e
r
ea
ctiv
e
p
o
w
er
an
d
g
e
n
er
a
to
r
s
p
ee
d
co
n
tr
o
l
lo
o
p
s
w
er
e
co
n
s
id
er
ed
i
n
t
h
e
t
u
n
in
g
p
r
o
ce
s
s
.
T
h
e
r
esp
o
n
s
e
o
f
t
h
e
tu
n
ed
co
n
tr
o
ller
w
a
s
te
s
ted
t
h
r
o
u
g
h
th
e
i
m
p
le
m
e
n
tatio
n
o
f
Ma
x
i
m
u
m
P
o
w
er
tr
ac
k
in
g
an
d
to
en
s
u
r
e
t
h
e
f
a
u
lt
r
id
e
ca
p
ab
ilit
y
o
f
t
h
e
D
FIG
w
i
n
d
tu
r
b
in
e.
B
ased
o
n
t
h
e
co
m
p
ar
is
o
n
o
f
r
es
u
lt
s
,
th
e
GGW
O
t
u
n
i
n
g
m
et
h
o
d
p
er
f
o
r
m
ed
b
ett
er
th
a
n
m
o
t
h
f
la
m
e
o
p
tim
izer
(
MFO)
,
P
SO,
an
d
GA
t
u
n
in
g
tec
h
n
iq
u
es.
I
n
th
i
s
s
t
u
d
y
,
t
h
e
class
ica
l
G
W
O
w
as
ap
p
lied
in
t
u
n
i
n
g
th
e
g
ain
s
o
f
t
h
e
P
I
co
n
tr
o
ller
i
n
th
e
p
itc
h
co
n
tr
o
l
s
y
s
te
m
o
f
t
h
e
3
MW
f
ix
e
-
s
p
ee
d
W
in
d
T
u
r
b
in
e.
T
h
e
GW
O
is
p
r
o
p
o
s
ed
to
ad
d
r
es
s
th
e
l
i
m
i
tatio
n
s
o
f
th
e
clas
s
ical
G
A
an
d
P
SO
t
u
n
in
g
m
et
h
o
d
s
f
o
r
th
e
P
I
co
n
tr
o
ller
b
ec
au
s
e
it
ca
n
a
v
o
id
lo
ca
l
o
p
ti
m
u
m
a
n
d
h
a
s
w
e
l
l
-
o
r
g
a
n
i
z
e
d
e
x
p
l
o
r
a
t
i
o
n
a
n
d
e
x
p
l
o
i
t
a
t
i
o
n
[
1
7
]
.
I
n
a
d
d
i
t
i
o
n
,
i
t
i
s
s
i
m
p
l
e
,
r
o
b
u
s
t
a
n
d
c
a
n
b
e
a
p
p
l
i
e
d
t
o
c
o
m
p
l
e
x
o
p
tim
izatio
n
ta
s
k
s
[
1
8
]
.
2.
P
O
WE
R
SY
ST
E
M
M
O
DE
L
DE
SCR
I
P
T
I
O
N
AND
S
T
A
NDAR
D
O
B
J
E
CT
I
VE
F
U
N
CT
I
O
N
S
T
h
e
W
in
d
T
u
r
b
in
e
u
n
d
er
s
t
u
d
y
is
o
b
tain
ed
f
r
o
m
[
1
9
]
an
d
is
p
r
esen
ted
in
Fi
g
u
r
e
1
.
I
t
is
a
2
2
.
9
k
V
1
k
m
s
in
g
le
li
n
e
co
n
n
ec
ted
to
2
2
.
9
k
V
1
0
k
m
d
o
u
b
le
cir
cu
it
d
is
t
r
ib
u
tio
n
li
n
es.
T
h
e
o
th
er
e
n
d
o
f
th
e
s
i
n
g
le
li
n
e
is
co
u
p
led
to
a
3
MW
W
in
d
T
u
r
b
in
e
Sq
u
ir
r
el
C
a
g
e
I
n
d
u
ctio
n
Ge
n
e
r
ato
r
th
r
o
u
g
h
a
0
.
6
9
0
k
V/2
2
.
9
k
V/
4
MV
A
s
tep
-
u
p
tr
an
s
f
o
r
m
er
.
T
h
e
o
th
er
en
d
o
f
th
e
d
is
tr
ib
u
tio
n
lin
e
i
s
co
n
n
ec
ted
to
a
1
5
4
k
V/6
0
Hz
i
n
f
in
i
te
b
u
s
t
h
r
o
u
g
h
a
2
2
.
9
k
V/1
5
4
k
V/3
0
MW
s
tep
-
u
p
tr
an
s
f
o
r
m
er
.
A
ls
o
,
a
5
0
0
k
W
ac
tiv
e
lo
ad
is
co
n
n
ec
ted
to
th
e
P
C
C
.
2
.
1
.
Wind
t
urb
ine
m
o
del
T
h
e
W
in
d
T
u
r
b
in
e
is
ap
p
lied
f
o
r
co
n
v
er
tin
g
th
e
k
in
et
ic
en
er
g
y
o
f
t
h
e
W
in
d
to
m
ec
h
an
i
ca
l
p
o
w
er
.
I
t
is
ap
p
lied
to
th
e
g
en
er
ato
r
i
n
p
u
t
f
o
r
p
r
o
d
u
cin
g
e
lectr
ica
l
p
o
w
er
.
T
h
e
m
o
d
els
r
ep
r
esen
ti
n
g
th
e
w
i
n
d
t
u
r
b
in
e
m
ec
h
a
n
ical
p
o
w
er
an
d
to
r
q
u
e
[
2
0
-
2
3
]
ar
e
r
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Sta
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u
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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5261
5254
3.
P
RO
P
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D
M
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T
H
O
D
3
.
1
.
F
o
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s
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Fig
u
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2
.
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h
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ted
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h
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[
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n
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f
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f
r
o
m
t
h
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d
is
p
r
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in
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as a
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Sub
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w
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er
e
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e
s
i
m
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m
e,
dt
is
th
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s
a
m
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l
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ti
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d
ar
e
th
e
u
p
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er
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w
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s
tr
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ts
in
th
e
m
i
n
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m
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p
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s
s
.
Fig
u
r
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2
.
T
h
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PI
co
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ed
w
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er
v
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-
m
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in
p
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Fig
u
r
e
3
.
T
h
e
tr
an
s
f
er
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lo
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m
o
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e
p
itc
h
a
n
g
le
P
I
co
n
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o
l s
y
s
te
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Op
tima
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in
g
o
f
p
r
o
p
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r
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a
l in
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l c
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n
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…
(
A
li
yu
Ha
mza
S
u
le
)
5255
Fro
m
th
e
liter
at
u
r
e,
th
e
I
T
A
E
an
d
I
A
E
ar
e
ap
p
li
ed
b
y
r
esear
ch
er
s
in
f
o
r
m
u
late
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
.
I
n
t
h
i
s
s
t
u
d
y
,
th
e
I
T
SE
is
s
elec
ted
in
th
e
f
o
r
m
u
la
te
d
o
b
j
ec
tiv
e
f
u
n
c
tio
n
i
n
(
1
3
)
.
Sectio
n
s
3
.
2
,
3
.
3
an
d
3
.
4
d
escr
ib
e
d
th
e
GW
O,
P
SO,
an
d
G
A
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n
i
n
g
m
et
h
o
d
s
.
3
.
2
.
G
re
y
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lf
o
ptim
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t
u
nin
g
m
et
ho
d
T
h
e
Gr
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o
lf
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ti
m
izer
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n
e
o
f
t
h
e
p
o
p
u
latio
n
-
b
ased
a
lg
o
r
ith
m
s
d
ev
elo
p
ed
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y
Mir
j
alili
et
al.
,
in
2
0
1
4
f
o
r
o
p
ti
m
izi
n
g
d
i
f
f
e
r
en
t
t
y
p
e
s
o
f
o
b
j
ec
tiv
e
f
u
n
ct
io
n
s
[
2
8
]
.
T
h
e
s
o
cial
lead
er
s
h
ip
o
f
t
h
e
W
o
lv
e
s
(
s
ea
r
ch
ag
en
ts
)
is
class
if
ied
in
to
α
W
o
lf
,
β
W
o
lv
es,
δ
W
o
lv
es,
an
d
⍵
W
o
lv
es
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ased
o
n
f
it
n
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s
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t
u
n
i
n
g
.
T
h
e
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n
i
n
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ain
s
(
s
ea
r
c
h
in
g
o
f
p
r
e
y
)
b
y
th
e
W
o
lv
e
s
is
m
o
d
elled
b
y
(
1
4
)
-
(
1
6
)
.
⃗
⃗
=
|
1
−
|
(
1
4
)
⃗
⃗
=
|
2
−
|
(
1
5
)
⃗
⃗
=
|
3
−
|
(
1
6
)
1
=
−
1
∙
(
⃗
⃗
)
(
1
7
)
2
=
−
2
∙
(
⃗
⃗
)
(
1
8
)
3
=
−
3
∙
(
⃗
⃗
)
(
1
9
)
(
+
1
)
=
1
+
2
+
3
3
(
2
0
)
I
n
(
1
7
)
-
(
1
9
)
m
o
d
elled
th
e
b
est
p
o
s
itio
n
s
o
f
α
β
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d
δ
W
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lv
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[
2
9
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.
T
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l
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to
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[
3
0
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p
r
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(
2
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d
(
2
2
)
.
I
f
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=
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(
2
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T
h
e
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r
is
v
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f
r
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m
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,
w
h
ile
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s
1
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d
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d
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d
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r
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th
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s
s
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Fo
r
th
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GW
O
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to
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,
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n
g
th
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n
u
m
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f
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c
h
a
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m
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3
.
3
.
P
a
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s
w
a
rm
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t
un
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1
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9
5
as
o
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f
t
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s
w
ar
m
p
o
p
u
lat
io
n
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b
ased
alg
o
r
ith
m
s
[
3
1
]
.
I
t
m
i
m
ic
s
t
h
e
s
o
cial
b
e
h
av
io
u
r
o
f
f
lo
ck
i
n
g
o
f
b
ir
d
s
[
3
2
]
o
r
s
ch
o
o
lin
g
o
f
f
i
s
h
es
[
2
]
an
d
th
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y
n
a
m
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I
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m
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cr
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s
[
3
3
]
.
A
n
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m
e
m
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f
th
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w
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ca
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P
I
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p
tim
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l
g
ai
n
s
.
T
h
e
o
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f
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SO
in
v
o
lv
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th
e
i
n
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tio
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f
t
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p
ar
ticl
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th
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ies.
A
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2
3
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(
2
4
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d
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p
ac
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[
3
4
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.
⃗
+
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=
⃗
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pb
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s
t
i
−
)
+
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gb
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−
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2
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2
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T
h
e
s
tab
ilit
y
o
f
th
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P
SO d
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t
h
e
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9
to
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4
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T
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c1
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c2
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p
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f
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d
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co
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v
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co
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f
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an
d
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f
ac
to
r
r
esp
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tiv
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.
A
l
s
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an
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n
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m
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s
1
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d
2
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f
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attain
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b
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t
h
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s
ea
r
ch
a
g
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ts
[
3
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
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I
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&
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5
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2
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3
.
4
.
G
enet
ic
a
lg
o
rit
h
m
t
un
i
ng
m
et
ho
d
T
h
e
GA
m
i
m
ics
t
h
e
b
io
lo
g
i
ca
l
ev
o
lu
tio
n
an
d
g
en
et
ic
m
ec
h
a
n
i
s
m
in
t
h
e
t
u
n
i
n
g
o
f
P
I
g
ain
s
.
I
t
is
ap
p
lied
to
s
o
lv
e
co
m
p
le
x
co
n
tr
o
l
p
r
o
b
lem
s
[
3
6
]
w
h
ic
h
t
r
ad
itio
n
al
alg
o
r
it
h
m
s
ca
n
n
o
t
s
o
lv
e.
T
h
e
o
p
er
atio
n
o
f
GA
b
e
g
in
s
w
it
h
th
e
i
n
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izatio
n
o
f
p
o
p
u
latio
n
s
w
h
ic
h
ar
e
ca
n
d
id
ate
s
o
lu
tio
n
s
to
th
e
P
I
o
p
tim
al
g
a
in
s
.
Du
r
in
g
iter
atio
n
t
h
r
ee
o
p
er
ato
r
s
ar
e
ex
ec
u
ted
n
a
m
el
y
:
T
h
e
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
ta
t
io
n
o
n
p
ar
en
ts
f
r
o
m
its
p
o
p
u
latio
n
to
p
r
o
d
u
ce
o
f
f
s
p
r
in
g
to
f
o
r
m
a
n
e
w
p
o
p
u
l
atio
n
[
3
7
]
f
o
r
th
e
n
e
x
t
g
en
e
r
atio
n
o
r
iter
atio
n
.
T
h
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ch
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m
o
s
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m
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s
o
f
co
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s
ta
n
t
len
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s
ar
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latio
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s
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th
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p
r
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p
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s
ed
ca
n
d
id
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s
o
lu
tio
n
s
to
th
e
P
I
o
p
tim
al
g
a
in
s
.
B
esid
es,
th
e
f
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n
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s
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ch
c
h
r
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m
o
s
o
m
e
is
r
an
k
ed
b
ased
o
n
th
e
o
b
je
ctiv
e
f
u
n
ctio
n
[
3
8
]
I
T
SE,
b
ef
o
r
e
th
e
ap
p
licatio
n
o
f
o
p
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r
s
.
T
h
e
o
p
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in
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f
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s
in
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cr
o
s
s
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d
m
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tatio
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[
2
]
.
St
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s
t
w
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a
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th
r
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e
r
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ted
ly
i
m
p
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m
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ted
d
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r
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s
u
n
ti
l
th
e
b
est
o
p
ti
m
al
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T
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Gen
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Alg
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r
it
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n
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n
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to
ler
an
ce
o
r
co
n
s
tr
ai
n
t to
ler
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.
3
.
5
.
Sta
nd
a
rdiza
t
io
n o
f
t
un
ing
pa
ra
m
et
er
s
a
nd
o
pera
t
o
rs o
f
a
lg
o
rit
h
m
s
T
h
e
p
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f
o
r
m
a
n
ce
o
f
clas
s
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GW
O
in
t
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n
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n
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th
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an
d
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n
s
o
f
p
itc
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P
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tr
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v
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co
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tr
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s
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m
f
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W
in
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m
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w
it
h
th
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cla
s
s
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P
SO
a
n
d
G
A
tu
n
in
g
m
et
h
o
d
s
.
T
h
e
s
elec
ted
p
ar
am
eter
s
an
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p
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r
s
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s
ed
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o
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n
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n
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m
s
ar
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s
tan
d
ar
d
ized
as
p
r
esen
ted
i
n
T
ab
le
1
.
T
h
e
n
u
m
b
er
o
f
s
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c
h
ag
e
n
t
s
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n
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th
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ter
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s
f
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ea
ch
alg
o
r
ith
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3
0
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d
5
0
r
esp
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ctiv
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.
A
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s
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p
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f
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m
0
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5
0
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0
to
1
1
0
0
r
esp
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tiv
ely
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T
ab
le
1
.
T
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s
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p
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f
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n
n
in
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t
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r
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p
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m
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s
O
p
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f
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M
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U
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L
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O
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s
G
W
O
30
50
5
0
0
1
1
0
0
0
0
2
a
=
[
2
0
]
r
1
=
[
0
1
]
r
2
=
[
0
1
]
PSO
30
50
5
0
0
1
1
0
0
0
0
0
0
2
w
M
a
x
=
0
.
9
w
M
i
n
=
0
.
2
r
1
=
[
0
1
]
c
1
=
2
c
2
=
2
r
2
=
[
0
1
]
GA
30
50
5
0
0
1
1
0
0
0
0
2
P
c
=
0
.
9
5
P
m=0
.
0
0
1
Er
=
0
.
2
3
.
6
.
Co
ntr
o
ller
perf
o
r
m
a
nce
m
et
rics
T
h
e
p
e
r
f
o
r
m
a
n
c
e
m
e
t
r
i
c
s
f
o
r
c
o
m
p
a
r
i
n
g
t
h
e
t
u
n
e
d
P
I
D
c
o
n
t
r
o
l
l
e
r
a
r
e
r
e
p
o
r
t
e
d
i
n
[
3
9
]
a
r
e
t
h
e
m
i
n
i
m
i
z
e
d
o
b
j
ec
tiv
e
f
u
n
ct
io
n
,
th
e
t
u
n
ed
g
ain
s
o
f
t
h
e
P
I
D
co
n
tr
o
ller
,
t
h
e
s
ettli
n
g
ti
m
e
an
d
th
e
o
v
er
s
h
o
o
t.
W
h
ile
in
[
1
3
]
th
e
p
er
f
o
r
m
a
n
ce
m
etr
ics
r
ep
o
r
ted
ar
e
r
is
e
ti
m
e
t
r
,
s
e
ttli
n
g
t
i
m
e
t
s
,
p
er
ce
n
ta
g
e
o
v
er
s
h
o
o
t
M
p
%
,
s
tead
y
-
s
tate
er
r
o
r
,
g
ain
m
ar
g
i
n
,
p
h
ase
m
ar
g
in
,
a
n
d
o
b
j
ec
tiv
e
f
u
n
ct
io
n
.
I
n
th
i
s
s
t
u
d
y
,
th
e
p
er
f
o
r
m
a
n
c
e
m
e
tr
ics
co
n
s
id
er
ed
f
o
r
co
m
p
ar
in
g
th
e
tu
n
ed
P
I
co
n
tr
o
ller
s
ar
e
th
e
m
i
n
i
m
ized
o
b
j
ec
tiv
e
f
u
n
ctio
n
I
T
SE,
th
e
t
u
n
ed
g
ai
n
s
o
f
th
e
P
I
co
n
tr
o
ller
,
th
e
ti
m
e
co
n
s
ta
n
t,
t
h
e
s
ettl
in
g
ti
m
e
an
d
t
h
e
o
v
er
s
h
o
o
t.
4.
SI
M
UL
AT
I
O
N
R
E
S
UL
T
S
AND
DIS
CUSS
I
O
N
4
.
1
.
T
un
i
ng
re
s
ult
T
h
e
GW
O,
P
SO,
an
d
GA
co
d
es
w
er
e
ap
p
lied
in
th
e
Ma
tlab
2
0
1
6
b
t
o
tu
n
e
th
e
g
ai
n
s
o
f
th
e
P
I
co
n
tr
o
ller
in
th
e
p
itch
a
n
g
le
c
o
n
tr
o
l
s
y
s
te
m
s
h
o
w
n
i
n
Fig
u
r
e
2
.
T
h
ir
ty
n
u
m
b
er
o
f
m
i
n
i
m
i
za
tio
n
r
u
n
s
t
h
at
ar
e
w
id
el
y
ac
ce
p
ted
w
er
e
ex
ec
u
te
d
f
o
r
ea
ch
A
l
g
o
r
ith
m
a
n
d
t
h
e
t
u
n
i
n
g
r
es
u
lt i
s
p
r
ese
n
ted
in
T
ab
le
2
.
W
h
at
is
n
e
w
,
in
t
h
is
s
t
u
d
y
o
n
t
h
e
P
I
tu
n
i
n
g
p
r
o
b
lem
i
s
t
h
e
ap
p
licatio
n
o
f
t
h
e
GW
O
t
u
n
in
g
m
et
h
o
d
to
o
b
tain
ed
o
p
ti
m
al
g
ai
n
s
o
f
th
e
P
I
co
n
tr
o
ller
in
t
h
e
p
itch
a
n
g
le
co
n
tr
o
l
o
f
f
ix
ed
s
p
ee
d
w
in
d
t
u
r
b
in
e.
T
h
is
h
a
s
n
o
t
b
ee
n
d
o
n
e
b
y
th
e
r
esear
ch
er
s
in
t
h
e
ar
ea
o
f
tu
n
in
g
t
h
e
P
I
co
n
tr
o
ller
.
A
ls
o
,
I
T
SE
s
tan
d
ar
d
o
b
j
ec
tiv
e
f
u
n
ctio
n
i
s
ap
p
lied
in
th
is
s
t
u
d
y
,
w
h
ile
f
r
o
m
th
e
l
ite
r
atu
r
e
m
o
s
t
r
esear
c
h
er
s
ap
p
lied
I
T
A
E
an
d
I
A
T
.
T
h
e
iter
atio
n
co
lu
m
n
co
n
tai
n
s
th
e
m
in
i
m
u
m
an
d
t
h
e
m
a
x
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
b
ef
o
r
e
th
e
co
n
v
er
g
en
ce
o
f
ea
ch
al
g
o
r
ith
m
.
Fro
m
th
e
m
i
n
r
o
w
an
d
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
co
lu
m
n
,
th
e
G
A
w
as
tr
ap
p
ed
in
to
a
lo
ca
l
o
p
tim
u
m
o
f
1
.
2
5
0
8
e*
1
0
-
12
in
th
e
1
0
th
iter
at
io
n
b
u
t,
th
e
GW
O
h
ad
co
n
v
er
g
ed
in
t
h
e
1
9
th
iter
atio
n
in
to
t
h
e
g
lo
b
al
o
p
ti
m
u
m
o
f
2
.
0
5
7
7
*
1
0
-
13
.
T
h
is
is
f
aste
r
th
an
P
SO
w
h
ich
co
n
v
er
g
ed
in
t
h
e
2
4
th
iter
atio
n
in
to
t
h
e
g
lo
b
al
o
p
ti
m
u
m
o
f
2
.
0
5
7
7
*
1
0
-
13
.
Fro
m
t
h
e
m
ea
n
a
n
d
th
e
s
ta
n
d
ar
d
d
ev
iatio
n
r
o
w
s
o
f
T
ab
le
2
,
th
e
GW
O
h
as
t
h
e
leas
t
a
v
er
ag
e
n
u
m
b
er
o
f
iter
atio
n
s
(
4
0
.
4
0
)
b
ef
o
r
e
co
n
v
er
g
en
ce
in
to
t
h
e
g
lo
b
al
o
p
ti
m
u
m
co
m
p
ar
e
to
th
e
o
th
er
t
w
o
A
l
g
o
r
ith
m
s
.
T
h
e
GW
O
an
d
P
SO
h
av
e
t
h
e
s
m
allest
s
tan
d
ar
d
d
ev
iatio
n
in
th
e
n
u
m
b
er
o
f
iter
atio
n
s
b
ef
o
r
e
co
n
v
er
g
e
n
ce
co
m
p
ar
ed
to
GA
.
T
h
e
s
ig
n
i
f
ica
n
ce
o
f
th
e
r
e
s
u
lt
i
s
th
at
t
h
e
G
A
h
a
s
ex
h
ib
ited
its
li
m
i
tatio
n
o
f
co
n
v
er
g
e
n
ce
i
n
to
lo
ca
l
o
p
ti
m
u
m
,
a
n
d
th
e
GW
O
tu
n
in
g
m
eth
o
d
is
f
a
s
ter
in
co
n
v
er
g
en
ce
b
ec
au
s
e
it
h
a
s
th
e
least
n
u
m
b
er
o
f
iter
atio
n
s
b
ef
o
r
e
co
n
v
er
g
en
ce
in
to
th
e
g
lo
b
al
o
p
tim
u
m
.
Als
o
,
it
h
as
th
e
least
s
ta
n
d
ar
d
d
ev
iatio
n
i
n
th
e
n
u
m
b
er
o
f
iter
atio
n
s
b
ef
o
r
e
co
n
v
er
g
e
n
ce
.
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u
r
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o
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s
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e
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ith
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9
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in
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eth
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6
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d
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.
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e
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t
u
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n
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ller
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as
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ig
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n
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n
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r
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Fu
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th
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o
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P
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co
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tr
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in
th
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p
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le
co
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l
s
y
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m
o
f
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in
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in
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e
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h
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tr
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s
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s
tab
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p
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co
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tr
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l.
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is
ca
n
p
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o
v
id
e
s
af
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y
to
W
in
d
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u
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b
in
e
co
m
p
o
n
en
ts
.
RE
F
E
R
E
NC
E
S
[1
]
S
.
Ku
m
a
r
S
u
m
a
n
a
n
d
V
.
Ku
m
a
r
G
iri
,
“
G
e
n
e
ti
c
A
l
g
o
rit
h
m
s
T
e
c
h
n
iq
u
e
s
Ba
se
d
Op
ti
m
a
l
P
ID
T
u
n
in
g
F
o
r
S
p
e
e
d
Co
n
tr
o
l
o
f
DC M
o
to
r
,
”
Am.
J
.
E
n
g
.
T
e
c
h
n
o
l
.
M
a
n
a
g
.
,
v
o
l
.
1
,
n
o
.
4
,
p
p
.
5
9
-
6
4
,
2
0
1
6
.
[2
]
O.
Ch
a
o
a
n
d
L
.
W
e
ix
in
g
,
“
Co
m
p
a
riso
n
b
e
tw
e
e
n
P
S
O
a
n
d
G
A
fo
r
p
a
r
a
m
e
ters
o
p
ti
m
iza
ti
o
n
o
f
P
ID
c
o
n
tro
ll
e
r,
”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
IEE
E
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
M
e
c
h
a
tro
n
ic
s a
n
d
Au
to
ma
ti
o
n
,
p
p
.
2
4
7
1
-
2
4
7
5
,
2
0
0
6
.
[3
]
M
.
E.
M
.
Ha
rb
y
,
S
.
E
.
El
m
a
sr
y
,
a
n
d
A
.
El
S
a
m
a
h
y
,
“
F
a
u
lt
A
n
a
l
y
si
s
a
n
d
Co
n
tr
o
l
o
f
a
G
rid
Co
n
n
e
c
ted
W
in
d
T
u
r
b
in
e
Dri
v
in
g
S
q
u
irrel
Ca
g
e
In
d
u
c
ti
o
n
G
e
n
e
r
a
to
r
Us
in
g
Ge
n
e
ti
c
A
l
g
o
rit
h
m
P
ID
Co
n
tro
l
ler,”
Nin
e
tee
n
t
h
In
ter
n
a
ti
o
n
a
l
M
id
d
l
e
Ea
st
Po
we
r
S
y
ste
ms
Co
n
f
e
re
n
c
e
,
p
p
.
1
9
-
2
1
,
2
0
1
7
.
[4
]
U.
S
u
lt
a
n
a
,
“
Distrib
u
ted
G
e
n
e
ra
t
io
n
a
n
d
Ba
tt
e
ry
Ch
a
rg
in
g
S
tatio
n
A
ll
o
c
a
ti
o
n
b
a
se
d
o
n
M
in
im
iza
ti
o
n
o
f
S
y
st
e
m
En
e
rg
y
L
o
ss
e
s u
sin
g
G
re
y
W
o
lf
Op
ti
m
ize
r,
”
Ph
.
D.
T
h
e
sis,
U
n
ive
rs
it
i
T
e
k
n
o
l
o
g
i
M
a
la
y
sia
,
2
0
1
7
.
[5
]
S
.
S
i
n
h
a
a
n
d
S
.
S
.
Ch
a
n
d
e
l,
“
Re
v
iew
o
f
re
c
e
n
t
tren
d
s
in
o
p
ti
m
iza
ti
o
n
tec
h
n
iq
u
e
s
f
o
r
so
lar
p
h
o
to
v
o
l
t
a
ic
-
w
in
d
b
a
se
d
h
y
b
rid
e
n
e
rg
y
s
y
ste
m
s,”
Ren
e
w.
S
u
sta
in
.
E
n
e
rg
y
Rev
.
,
v
o
l
.
5
0
,
p
p
.
7
5
5
-
7
6
9
,
2
0
1
5
.
[6
]
M
.
F
.
A
ra
n
z
a
,
e
t
a
l.
,
“
T
u
n
n
in
g
P
ID
c
o
n
tro
ll
e
r
u
si
n
g
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
o
n
a
u
to
m
a
ti
c
v
o
lt
a
g
e
re
g
u
lato
r
sy
ste
m
,
”
IOP
Co
n
fer
e
n
c
e
S
e
rie
s: M
a
ter
ia
ls S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
,
v
o
l.
1
2
8
,
p
p
.
1
-
9
,
2
0
1
6
.
[7
]
Y.
Ro
m
a
se
v
y
c
h
,
V
.
L
o
v
e
ik
in
,
a
n
d
S
.
Us
e
n
k
o
,
“
P
i
-
c
o
n
tro
l
ler
t
u
n
i
n
g
o
p
ti
m
iza
ti
o
n
v
ia
P
S
O
-
b
a
se
d
tec
h
n
i
q
u
e
,
”
Prz.
El
e
k
tro
tec
h
n
icz
n
y
,
v
o
l.
9
5
,
n
o
.
7
,
p
p
.
3
3
-
3
7
,
2
0
1
9
.
[8
]
W
.
R.
A
b
d
u
l
-
A
d
h
e
e
m
,
“
A
n
e
n
h
a
n
c
e
d
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
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