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In
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C
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M.
Yasi
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Facu
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lab
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ized
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R
e
f
er
en
ce
[
2
]
–
[
4
]
w
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p
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if
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t
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io
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[
5
]
–
[
8
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.
R
ef
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en
ce
[
9
]
p
r
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a
m
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f
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A
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[
1
1
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I
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P
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P
SO)
tech
n
iq
u
e
is
o
n
e
o
f
m
o
d
er
n
h
eu
r
i
s
tic
al
g
o
r
ith
m
t
h
at
h
as
b
ee
n
u
s
ed
in
s
o
l
v
in
g
co
n
ti
n
u
o
u
s
n
o
n
l
in
ea
r
o
p
tim
izatio
n
p
r
o
b
le
m
[
1
2
]
.
PS
O
is
also
an
iter
atio
n
m
e
th
o
d
th
at
w
ill
lead
th
e
p
ar
ticles
a
n
d
f
in
a
ll
y
s
w
ar
m
to
o
b
tain
t
h
e
o
p
ti
m
u
m
r
e
g
io
n
as
w
ell
as
o
b
tain
i
n
g
t
h
e
b
est
p
o
in
t in
t
h
e
s
ea
r
c
h
s
p
ac
e.
T
h
er
ef
o
r
e,
th
i
s
m
et
h
o
d
m
a
y
g
iv
en
b
etter
p
er
f
o
r
m
a
n
ce
as
co
m
p
ar
ed
to
t
h
e
cla
s
s
ical
m
e
th
o
d
b
ec
au
s
e
it
d
o
es
n
o
t
n
ee
d
to
s
o
lv
e
th
e
co
m
p
le
x
m
at
h
e
m
a
tical
f
o
r
m
u
las
i
n
f
i
n
d
in
g
t
h
e
b
est
s
o
lu
t
io
n
i
n
E
L
D
p
r
o
b
lem
.
U
n
f
o
r
t
u
n
a
tel
y
,
t
h
is
m
et
h
o
d
s
till
in
r
esear
ch
p
r
o
g
r
ess
f
o
r
p
r
o
v
in
g
its
p
o
ten
tial
i
n
s
o
lv
in
g
an
y
c
o
n
s
tr
ain
t
o
p
ti
m
i
za
tio
n
p
r
o
b
le
m
s
o
th
a
t
it
ca
n
b
e
u
s
ed
to
o
p
ti
m
ize
a
w
id
e
r
an
g
e
o
f
f
u
n
ctio
n
s
w
i
th
v
ar
io
u
s
co
n
s
tr
ain
t
s
[
1
3
]
.
T
h
e
ap
p
licatio
n
s
o
f
C
u
c
k
o
o
Sear
c
h
A
l
g
o
r
ith
m
(
C
S
A
)
to
s
o
lv
e
en
g
i
n
ee
r
in
g
o
p
ti
m
izat
io
n
p
r
o
b
lem
s
h
av
e
s
h
o
w
n
i
ts
p
r
o
m
is
i
n
g
ef
f
icie
n
c
y
.
T
h
e
C
S
A
w
a
s
i
n
s
p
ir
ed
b
y
t
h
e
o
b
lig
a
te
b
r
o
o
d
p
ar
asit
is
m
o
f
s
o
m
e
cu
c
k
o
o
s
p
ec
ies b
y
la
y
in
g
th
eir
eg
g
s
i
n
t
h
e
n
e
s
ts
o
f
h
o
s
t
b
ir
d
s
.
T
h
e
b
r
ee
d
in
g
b
eh
a
v
io
u
r
o
f
c
u
ck
o
o
ca
n
b
e
ap
p
lied
to
v
ar
io
u
s
o
p
ti
m
izatio
n
p
r
o
b
lem
s
[
1
4
]
.
C
SA
o
b
tai
n
ed
b
etter
s
o
lu
tio
n
s
th
a
n
e
x
i
s
ti
n
g
s
o
lu
t
io
n
s
i
n
[
1
0
]
,
[
1
1
]
,
[
1
5
]
,
[
1
6
]
.
A
n
i
m
p
o
r
tan
t
ad
v
a
n
ta
g
e
o
f
C
S
A
i
s
i
ts
s
i
m
p
lic
it
y
.
A
m
u
ltio
b
j
ec
tiv
e
o
p
tim
izatio
n
n
a
m
el
y
M
u
ltio
b
j
ec
tiv
e
C
u
c
k
o
o
Sear
ch
A
l
g
o
r
ith
m
(
MO
C
S
A
)
is
p
r
o
p
o
s
ed
in
t
h
is
p
ap
er
as
it
ta
k
in
g
f
u
e
l
co
s
t
m
i
n
i
m
izatio
n
a
n
d
ca
r
b
o
n
e
m
is
s
io
n
m
i
n
i
m
izat
io
n
as
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
m
a
in
o
b
j
ec
tiv
e
f
u
n
ctio
n
o
f
E
L
D
is
to
m
in
i
m
ize
th
e
to
t
al
p
o
w
er
g
e
n
er
atio
n
co
s
t.
I
t
s
h
o
u
ld
m
ee
t
th
e
lo
ad
d
e
m
an
d
a
n
d
s
a
tis
f
y
i
n
g
all
th
e
co
n
s
tr
ain
t
s
.
T
w
o
o
b
j
ec
tiv
e
f
u
n
ct
io
n
s
ar
e
co
n
s
id
er
ed
w
h
ich
ar
e
co
s
t
m
i
n
i
m
izatio
n
a
n
d
ca
r
b
o
n
e
m
i
s
s
io
n
r
ed
u
ctio
n
.
T
h
e
an
al
y
s
is
i
s
d
iv
id
ed
in
to
t
h
r
ee
ca
s
es.
F
ir
s
tl
y
,
E
L
D
w
i
th
co
s
t
m
i
n
i
m
izatio
n
.
Seco
n
d
l
y
,
E
L
D
w
it
h
ca
r
b
o
n
em
is
s
io
n
m
in
i
m
izatio
n
.
L
a
s
tl
y
,
m
u
ltio
b
j
ec
tiv
e
E
L
D
co
n
s
id
er
i
n
g
b
o
th
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
s
i
m
u
lta
n
eo
u
s
l
y
.
E
co
n
o
m
ic
L
o
ad
Dis
p
atch
(
E
L
D)
p
r
o
b
lem
c
o
n
s
id
er
in
g
co
s
t
as
o
b
j
ec
tiv
e
f
u
n
c
tio
n
ca
n
b
e
m
o
d
eled
as (
1
)
.
(
)
∑
(
)
(
1
)
W
h
er
e
is
th
e
to
tal
f
u
el
co
s
t,
(
)
is
th
e
f
u
el
co
s
t
o
f
g
e
n
er
ati
n
g
u
n
i
t
i
an
d
n
is
th
e
n
u
m
b
er
o
f
g
en
er
ato
r
.
T
h
e
f
u
el
co
s
t
f
u
n
ct
io
n
o
f
a
g
e
n
er
atin
g
u
n
it
i
s
u
s
u
all
y
d
e
s
cr
ib
ed
b
y
a
q
u
ad
r
atic
f
u
n
ct
io
n
o
f
p
o
w
er
o
u
tp
u
t,
as
s
h
o
w
n
in
(
2
)
.
(
)
(
2
)
W
h
er
e
ar
e
f
u
el
co
s
t
co
ef
f
ic
ien
ts
o
f
u
n
it
i.
T
h
e
u
n
its
f
o
r
th
e
ar
e
$
/MW.
T
h
e
em
i
s
s
i
o
n
eq
u
atio
n
o
f
a
g
en
er
ati
n
g
u
n
it i
s
u
s
u
all
y
d
escr
ib
ed
b
y
a
q
u
ad
r
atic
f
u
n
ct
io
n
o
f
p
o
w
er
o
u
tp
u
t,
as
(
)
(
3
)
W
h
er
e
ar
e
em
i
s
s
io
n
co
ef
f
icie
n
ts
o
f
u
n
it i.
T
h
e
u
n
its
f
o
r
th
e
ar
e
k
g
/
h
.
T
h
er
e
ar
e
tw
o
m
eth
o
d
s
f
o
r
s
o
lv
i
n
g
a
n
o
p
ti
m
izatio
n
p
r
o
b
le
m
s
w
h
ic
h
ar
e
an
al
y
tica
l
m
eth
o
d
an
d
n
u
m
er
ical
m
et
h
o
d
.
T
h
e
an
al
y
t
ical
m
e
th
o
d
i
n
v
o
l
v
es
p
r
ec
is
e
m
at
h
e
m
a
tical
d
er
iv
a
tio
n
a
n
d
f
o
r
m
u
la
to
o
b
tain
t
h
e
s
o
lu
t
io
n
.
Ho
w
e
v
er
,
th
i
s
m
et
h
o
d
d
ep
en
d
s
s
tr
ictl
y
o
n
t
h
e
p
r
o
b
lem
c
h
ar
ac
ter
is
t
ics
w
h
ic
h
is
n
o
t
s
u
itab
le
f
o
r
s
o
lv
i
n
g
r
ea
li
s
tic
p
r
o
b
le
m
s
.
T
h
e
n
u
m
er
ical
m
eth
o
d
is
co
n
s
tr
u
cted
w
it
h
a
s
er
ies
o
f
iter
a
tio
n
s
to
o
b
tain
t
h
e
o
p
tim
a
l
s
o
l
u
tio
n
[
1
7
]
.
T
h
e
o
p
ti
m
al
s
o
lu
t
io
n
c
a
n
b
e
o
b
tain
ed
b
y
s
elec
t
in
g
t
h
e
s
u
i
tab
le
v
ar
i
ab
les
an
d
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
.
T
h
is
m
eth
o
d
is
m
o
r
e
s
u
itab
le
to
s
o
lv
e
t
h
e
r
ea
l p
r
o
b
le
m
s
w
it
h
m
an
y
co
n
s
tr
ain
ts
.
In
m
u
lti
-
o
b
j
ec
tiv
e
o
p
ti
m
izat
i
o
n
,
b
o
th
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
n
ee
d
to
b
e
co
n
s
id
er
ed
s
i
m
u
ltan
eo
u
s
l
y
.
T
h
e
f
it
n
ess
f
o
r
b
o
th
o
b
j
ec
tiv
e
f
u
n
ct
io
n
s
ar
e
ca
lcu
lated
in
d
iv
id
u
all
y
.
U
s
u
al
l
y
,
b
o
t
h
o
b
jectiv
e
f
u
n
ct
io
n
s
ar
e
co
n
tr
ad
icto
r
y
w
it
h
ea
c
h
o
th
er
.
T
h
er
ef
o
r
e,
b
o
th
f
it
n
e
s
s
n
ee
d
to
b
e
n
o
r
m
alize
d
a
s
(
4
)
in
o
r
d
er
to
ca
lcu
late
t
h
e
f
i
n
al
s
o
lu
tio
n
.
T
h
e
a
n
al
y
s
i
s
i
s
ca
r
r
ied
o
u
t
b
y
as
s
u
m
in
g
th
at
all
w
e
ig
h
ti
n
g
f
ac
to
r
s
,
α
i
h
a
v
e
th
e
s
a
m
e
v
al
u
es.
T
h
e
v
alu
e
o
f
all
w
ei
g
h
ti
n
g
f
a
cto
r
s
s
h
o
u
ld
f
u
l
f
il
(
5
)
.
I
n
t
h
is
p
ap
er
,
th
e
f
in
a
l
s
o
l
u
tio
n
i
s
s
e
l
ec
ted
b
ased
o
n
th
e
m
ax
i
m
u
m
v
a
lu
e
o
f
m
u
ltio
b
j
ec
tiv
e
f
it
n
ess
,
F
T
as d
escr
ib
ed
in
(
6
)
.
)
m
i
n
(
)
m
a
x
(
)
m
a
x
(
i
i
i
i
ni
f
f
f
f
f
(
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
1
6
8
–
17
4
170
W
h
er
e
f
ni
is
n
o
r
m
a
lized
v
al
u
e
f
o
r
i
th
o
b
j
ec
tiv
e
f
u
n
ctio
n
1
1
k
i
i
(
5
)
W
h
er
e
α
i
is
w
ei
g
h
ti
n
g
f
ac
to
r
f
o
r
i
th
o
b
j
ec
tiv
e
f
u
n
c
tio
n
k
i
ni
i
T
f
F
1
(
6
)
W
h
er
e
k
is
n
u
m
b
er
s
o
f
o
b
j
ec
tiv
e
f
u
n
ctio
n
.
In
s
o
lv
i
n
g
E
L
D
p
r
o
b
lem
s
,
th
e
r
e
ar
e
co
n
s
tr
ain
ts
t
h
at
n
ee
d
to
b
e
co
n
s
id
er
ed
s
u
ch
a
s
tr
an
s
m
i
s
s
io
n
lo
s
s
,
p
o
w
er
b
alan
ce
d
,
an
d
g
en
er
ato
r
li
m
it.
T
h
e
eq
u
atio
n
f
o
r
tr
an
s
m
is
s
io
n
lo
s
s
is
e
x
p
r
ess
ed
as (
7
)
.
∑
∑
(
7
)
W
h
er
e
th
e
co
ef
f
icie
n
t
ar
e
ca
lle
d
lo
s
s
co
ef
f
icie
n
t
o
r
B
-
co
ef
f
icien
ts
.
B
-
co
ef
f
icie
n
ts
ar
e
ass
u
m
ed
co
n
s
ta
n
t.
B
esid
e
tr
an
s
m
i
s
s
io
n
lo
s
s
,
th
e
o
p
tim
izatio
n
also
s
h
o
u
ld
co
n
s
i
d
er
ed
th
e
p
o
w
er
b
ala
n
ce
d
in
t
h
e
s
y
s
te
m
.
T
h
e
to
tal
p
o
w
er
g
en
er
atio
n
s
h
o
u
ld
b
e
eq
u
al
to
th
e
to
tal
d
e
m
a
n
d
p
lu
s
lo
s
s
es.
∑
(
8
)
W
h
er
e
is
t
h
e
to
tal
lo
ad
d
e
m
a
n
d
,
is
t
h
e
to
tal
tr
a
n
s
m
i
s
s
io
n
l
o
s
s
es,
a
n
d
is
t
h
e
to
tal
p
o
w
er
g
en
er
atio
n
.
Gen
er
ato
r
li
m
it
o
f
ea
c
h
u
n
it
n
ee
d
to
b
e
co
n
s
id
er
ed
in
s
o
l
v
in
g
E
L
D
p
r
o
b
le
m
.
T
h
e
to
tal
p
o
w
er
o
u
tp
u
t
f
o
r
ea
c
h
o
f
g
e
n
er
atin
g
u
n
it
s
h
o
u
ld
lie
b
et
w
ee
n
lo
w
er
an
d
u
p
p
er
o
p
er
a
tin
g
li
m
its
.
(
9
)
W
h
er
e
is
th
e
m
in
i
m
u
m
p
o
w
er
o
u
tp
u
t li
m
it,
an
d
is
th
e
m
ax
i
m
u
m
p
o
w
er
o
u
tp
u
t li
m
i
t.
I
n
2
0
0
9
,
Xin
-
Sin
Ya
n
g
an
d
Su
as
h
Deb
h
a
s
p
r
esen
ted
an
alg
o
r
ith
m
t
h
at
ca
p
ab
le
in
g
i
v
in
g
a
g
r
ea
t
ef
f
icien
c
y
i
n
s
o
lv
i
n
g
v
ar
io
u
s
o
p
tim
izatio
n
p
r
o
b
le
m
s
an
d
r
e
al
-
w
o
r
ld
ap
p
licatio
n
w
h
ich
is
ca
lled
as
C
u
c
k
o
o
Sear
ch
A
l
g
o
r
ith
m
(
C
S
A
)
[
1
4
]
.
I
n
th
e
p
a
s
t
f
e
w
d
ec
ad
es,
n
u
m
er
o
u
s
r
e
s
ea
r
ch
p
ap
er
h
a
v
e
b
ee
n
is
s
u
ed
r
e
g
ar
d
in
g
cu
ck
o
o
s
ea
r
ch
f
i
n
d
in
g
.
T
h
i
s
i
s
b
ec
au
s
e
C
S
A
tec
h
n
iq
u
e
p
r
o
v
i
d
e
a
b
etter
p
o
p
u
latio
n
s
i
n
f
a
s
t
er
r
u
n
ti
m
e
w
it
h
o
u
t
ex
ce
s
s
iv
e
e
x
p
er
i
m
e
n
tatio
n
f
o
r
p
ar
am
eter
r
u
n
n
i
n
g
.
In
th
i
s
p
ap
er
,
MO
C
S
A
i
s
ap
p
lied
to
s
o
lv
e
E
L
D
f
o
r
co
s
t
m
i
n
i
m
izatio
n
,
E
L
D
f
o
r
e
m
is
s
io
n
m
i
n
i
m
izat
io
n
,
a
n
d
E
L
D
f
o
r
m
u
ltio
b
j
ec
tiv
e
o
p
ti
m
izatio
n
.
C
u
c
k
o
o
s
ea
r
ch
is
a
n
o
p
ti
m
iz
atio
n
b
ased
o
n
b
eh
av
io
u
r
o
f
C
u
c
k
o
o
.
I
t
w
as
i
n
s
p
ir
ed
b
y
t
h
e
o
b
lig
ate
b
r
o
o
d
p
ar
asit
is
m
b
y
s
o
m
e
C
u
ck
o
o
s
p
ec
ies
b
y
la
y
in
g
t
h
eir
e
g
g
s
i
n
th
e
n
es
t
o
f
o
th
er
h
o
s
t
b
ir
d
.
T
h
er
e
ar
e
tw
o
s
tag
e
s
o
f
p
r
o
b
ab
ilit
y
g
e
n
er
ati
n
g
in
co
n
v
en
t
io
n
al
m
et
h
o
d
s
.
T
h
e
f
ir
s
t
s
ta
g
e
i
s
L
év
y
f
li
g
h
t
w
h
ic
h
r
an
d
o
m
l
y
g
en
er
ate
s
an
d
th
e
s
ec
o
n
d
s
ta
g
e
is
ex
p
lai
n
ed
th
e
ac
tio
n
o
f
h
o
s
t
b
ir
d
s
to
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an
d
o
n
C
u
ck
o
o
eg
g
s
.
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n
ad
d
itio
n
,
th
e
b
ir
d
s
’
f
li
g
h
t
b
eh
av
io
u
r
h
a
v
e
a
b
it
ch
ar
ac
ter
is
ti
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o
f
L
é
v
y
f
li
g
h
t,
w
h
er
e
th
e
L
év
y
f
l
ig
h
t
is
a
r
an
d
o
m
w
al
k
.
T
h
er
e
ar
e
th
r
ee
ty
p
es
o
f
b
r
o
o
d
p
a
r
asit
is
m
w
h
ic
h
ar
e
in
tr
asp
ec
if
ic
b
r
o
o
d
p
ar
asit
is
m
,
n
est
ta
k
e
-
o
v
er
an
d
co
-
o
p
er
ativ
e
b
r
ee
d
in
g
.
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h
e
b
e
h
av
io
u
r
o
f
p
ar
asit
ic
cu
ck
o
o
s
i
s
o
f
ten
ch
o
s
e
a
n
e
s
t
w
h
er
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th
e
h
o
s
t
b
ir
d
j
u
s
t
laid
its
o
w
n
eg
g
s
.
So
m
e
C
u
c
k
o
o
s
s
p
ec
ies
h
a
v
e
s
p
ec
ialized
in
th
e
i
m
itatio
n
i
n
co
lo
u
r
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d
ch
o
s
en
th
e
h
o
s
t
s
p
ec
ie
s
.
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h
er
ef
o
r
e,
it
ca
n
r
e
d
u
ce
s
th
e
p
r
o
b
a
b
ilit
y
o
f
eg
g
s
b
ein
g
ab
an
d
o
n
ed
an
d
at
th
e
s
a
m
e
ti
m
e
i
t
w
ill
i
n
cr
ea
s
es
t
h
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r
o
d
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ctiv
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y
.
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t
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n
b
e
as
s
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m
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th
at
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n
l
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o
n
e
eg
g
i
s
p
lace
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in
a
n
es
t
at
a
ti
m
e.
E
ac
h
e
g
g
r
ep
r
ese
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t
a
s
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tio
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w
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er
e
t
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e
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est
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ts
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tio
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w
h
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r
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s
th
e
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u
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k
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o
eg
g
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r
esen
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a
n
e
w
s
o
l
u
tio
n
.
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g
g
th
at
h
a
s
g
o
o
d
q
u
alit
y
is
ca
r
r
ied
o
v
er
to
th
e
n
ex
t
g
e
n
e
r
atio
n
s
.
I
n
ad
d
itio
n
,
t
h
e
ai
m
o
f
co
m
p
ar
is
o
n
is
to
s
elec
t
t
h
e
n
e
w
s
o
l
u
tio
n
s
to
s
u
p
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t
a
p
o
o
r
s
o
lu
tio
n
i
n
t
h
e
n
ests
.
Ne
x
t,
t
h
e
h
o
s
t
b
ir
d
w
ill
d
ec
id
e
eith
er
t
h
r
o
w
n
th
e
eg
g
o
r
n
est
an
d
th
e
b
ir
d
w
ill
b
u
ild
s
u
p
a
n
e
w
n
e
s
t
at
a
n
e
w
p
lace
.
I
n
C
u
c
k
o
o
s
ea
r
ch
alg
o
r
ith
m
,
ea
ch
C
u
c
k
o
o
w
il
l
la
y
s
o
n
l
y
o
n
e
eg
g
at
a
ti
m
e
w
i
th
d
u
m
p
s
eg
g
i
n
r
a
n
d
o
m
l
y
ch
o
s
e
n
n
est,
th
e
eg
g
s
w
it
h
h
ig
h
q
u
alit
y
w
i
ll
ca
r
r
y
o
v
er
th
e
n
e
x
t
g
e
n
er
atio
n
,
t
h
e
n
u
m
b
er
o
f
av
ailab
le
h
o
s
t
s
’
is
co
n
s
ta
n
t
an
d
th
e
h
o
s
t
b
ir
d
w
il
l
d
is
co
v
er
th
e
C
u
c
k
o
o
eg
g
w
i
th
a
p
r
o
b
a
b
il
it
y
b
et
w
ee
n
0
an
d
1
.
Fig
u
r
e
1
ill
u
s
tr
ate
s
t
h
e
f
lo
w
ch
ar
t
o
f
MO
C
S
A
f
o
r
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l
v
i
n
g
E
L
D
p
r
o
b
lem
.
I
n
t
h
e
i
n
itial
izatio
n
p
r
o
ce
s
s
,
th
e
p
o
p
u
latio
n
w
er
e
r
an
d
o
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l
y
g
e
n
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ated
w
it
h
i
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n
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ai
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ts
s
u
c
h
a
s
p
o
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g
e
n
er
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m
it
co
n
s
tr
ain
t
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d
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an
s
m
i
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n
lo
s
s
es.
T
h
e
s
y
s
te
m
r
ea
d
th
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d
ata
w
h
ich
c
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n
s
is
t
o
f
p
o
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d
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m
a
n
d
,
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e
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t,
m
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m
u
m
an
d
m
ax
i
m
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e
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er
atio
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m
it
s
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.
T
h
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v
er
y
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ate
o
f
alien
e
g
g
s
is
s
et
to
b
e
0
.
2
5
.
T
h
er
e
ar
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
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lec
E
n
g
&
C
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m
p
Sci
I
SS
N:
2502
-
4752
Op
tima
l E
co
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Lo
a
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Dis
p
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in
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Mu
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C
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S
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a
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r
ith
m
(
Z.M.
Y
a
s
in
)
171
th
r
ee
m
ai
n
s
tag
e
s
in
th
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iter
a
tiv
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r
c
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t
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ir
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ated
v
ia
L
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all
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b
ased
o
n
(
2
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an
d
(
3
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.
T
h
e
o
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e
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o
f
t
h
e
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ca
l
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d
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m
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ce
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th
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e
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tio
n
g
e
n
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g
(
1
1
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.
Fig
u
r
e
1
.
Flo
w
c
h
ar
t o
f
M
u
ltio
b
j
ec
tiv
e
C
u
c
k
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Sear
ch
Alg
o
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ith
m
G
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r
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G
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Ev
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=
<
0
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N
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mal
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h
f
i
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ss v
a
l
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s
Ca
l
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M
u
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F
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ss,
F
T
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R
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R
a
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<
P
a
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i
=
n
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No
R
e
p
l
a
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e
w
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s so
l
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y
r
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Y
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p
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st
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v
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?
No
Y
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s
S
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C
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c
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l
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t
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G
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D
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e
P
a
,
n
a
n
d
f
i
t
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e
ss f
u
n
c
t
i
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
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d
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J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
1
6
8
–
17
4
172
(
)
(
)
L
ev
y
(
1
0
)
W
h
er
e
X
d
is
th
e
p
o
p
u
latio
n
a
n
d
α
is
a
s
tep
s
ize
f
o
r
u
p
d
atin
g
n
e
w
s
o
l
u
tio
n
.
{
(
)
(
1
1
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h
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X
r1
an
d
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ar
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r
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it
h
d
r
a
w
n
f
r
o
m
t
h
e
p
o
p
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In
th
e
last
s
ta
g
e,
MO
C
S
A
w
il
l
p
er
f
o
r
m
a
s
elec
tio
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p
r
o
ce
s
s
.
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r
in
g
th
e
s
elec
tio
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p
r
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s
s
,
all
f
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e
s
s
f
u
n
ctio
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w
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n
o
r
m
a
lized
b
ef
o
r
e
ca
lcu
latin
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t
h
e
m
u
l
tio
b
j
ec
tiv
e
f
it
n
es
s
(
F
T
).
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f
n
e
w
f
i
tn
e
s
s
,
F
T
v
al
u
e
i
s
b
etter
th
an
o
ld
F
T
,
th
e
n
e
w
f
it
n
es
s
v
alu
e
w
i
ll
b
e
u
p
d
ated
as
a
n
e
w
n
est.
Ot
h
er
w
is
e,
it
w
ill
g
o
b
ac
k
to
g
en
er
ate
C
u
c
k
o
o
r
an
d
o
m
l
y
.
T
h
e
s
elec
tio
n
p
r
o
ce
s
s
ca
n
b
e
r
ep
r
esen
t
ed
b
y
(
1
2
)
.
T
h
e
v
alu
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o
f
f
it
n
es
s
o
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tain
ed
n
ee
d
s
atis
f
y
w
i
th
t
h
e
v
ar
io
u
s
co
n
s
t
r
ain
ts
i
n
ec
o
n
o
m
ic
d
is
p
atc
h
p
r
o
b
lem
.
T
h
e
n
u
m
b
er
o
f
n
es
t
is
s
et
to
2
0
an
d
th
e
b
est f
it
n
es
s
v
al
u
e
o
b
tain
ed
w
il
l
b
e
co
m
p
ar
e
d
w
it
h
t
h
e
b
est v
a
lu
e
o
f
.
{
(
)
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)
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1
2
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h
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X
d,
ne
w
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a
n
e
w
s
o
lu
tio
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at
th
e
s
a
m
e
n
e
s
t
d
.
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
Mu
ltio
b
j
ec
tiv
e
C
u
c
k
o
o
Sear
ch
A
l
g
o
r
ith
m
(
MO
C
S
A
)
was
d
ev
elo
p
ed
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y
u
s
in
g
M
AT
L
A
B
i
n
d
eter
m
in
i
n
g
th
e
o
p
ti
m
al
E
L
D
.
T
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
w
as
test
ed
o
n
th
r
ee
g
e
n
er
at
in
g
u
n
its
s
y
s
te
m
a
n
d
th
e
o
u
tp
u
t
r
es
u
lts
w
er
e
co
m
p
ar
ed
to
Mu
ltio
b
j
ec
tiv
e
Gen
etic
A
l
g
o
r
ith
m
(
MO
G
A
)
an
d
Mu
ltio
b
j
ec
tiv
e
P
ar
ticle
S
w
ar
m
Op
ti
m
izatio
n
(
MO
P
SO)
tech
n
iq
u
e
at
v
ar
io
u
s
lo
ad
d
em
a
n
d
.
Fo
r
co
m
p
ar
is
o
n
p
u
r
p
o
s
es,
th
e
an
al
y
s
i
s
w
er
e
also
ca
r
r
ied
o
u
t
u
s
i
n
g
C
S
A
,
G
A
a
n
d
P
SO
f
o
r
s
in
g
l
e
o
b
j
ec
tiv
e
o
p
ti
m
izatio
n
.
T
h
e
in
p
u
t
d
ata
f
o
r
th
r
ee
g
en
er
ati
n
g
u
n
it
in
ter
m
o
f
th
e
f
u
e
l
co
s
t
an
d
e
m
is
s
io
n
f
u
n
ctio
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w
a
s
g
i
v
e
n
in
T
ab
le
1
an
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ab
le
2
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tiv
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.
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e
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ir
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f
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[
7
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1
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T
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Gen
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I
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co
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4
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t
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b
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f
o
r
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i
n
g
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b
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f
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n
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o
f
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h
e
co
s
t
m
i
n
i
m
izatio
n
u
s
i
n
g
C
S
A
tech
n
iq
u
e
.
Th
e
r
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s
w
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co
m
p
ar
ed
to
th
e
P
SO
an
d
G
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te
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iq
u
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T
ab
le
3
.
L
o
s
s
C
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ef
f
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n
t
f
o
r
T
h
r
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Gen
er
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g
U
n
it
S
y
s
te
m
[
]
Evaluation Warning : The document was created with Spire.PDF for Python.
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d
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J
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&
C
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N:
2502
-
4752
Op
tima
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Lo
a
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Dis
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m
(
Z.M.
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173
T
ab
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4
.
Fu
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C
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s
t
Min
i
m
izati
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n
a
s
th
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4.
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RE
F
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R
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NC
E
S
[1
]
P
.
Ne
m
a
a
n
d
S
.
G
a
jb
h
iy
e
,
“
A
p
p
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ti
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icia
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rm
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v
o
l.
3
,
n
o
.
5
,
p
p
.
1
5
–
2
0
,
2
0
1
4
.
[2
]
S
.
Bisw
a
l,
A
.
K.
B
a
risa
l,
A
.
B
e
h
e
ra
,
a
n
d
T
.
P
ra
k
a
sh
,
“
Op
ti
m
a
l
p
o
w
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isp
a
tch
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sin
g
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a
l
g
o
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h
m
,
”
2
0
1
3
In
ter
n
a
t
io
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a
l
C
o
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fer
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E
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ien
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T
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y
,
p
p
.
1
0
1
8
–
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2
3
,
2
0
1
3
.
[3
]
S
.
S
a
h
o
o
,
K.
M
a
h
e
sh
Da
sh
,
R.
C.
P
r
u
sty
,
a
n
d
A
.
K.
B
a
risa
l,
“
Co
m
p
a
ra
ti
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tch
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ro
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s,”
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n
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h
a
ms
En
g
i
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rin
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J
o
u
rn
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l
,
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n
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.
1
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p
p
.
1
0
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–
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2
0
,
2
0
1
5
.
[4
]
N.
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rth
ik
,
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.
K.
P
a
rv
a
th
y
,
a
n
d
R.
A
ru
l,
“
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on
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c
o
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v
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Eco
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d
Disp
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tch
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sin
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k
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S
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a
rc
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A
l
g
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rit
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m
,
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In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
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trica
l
En
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ter
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v
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l.
5
,
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o
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1
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p
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4
8
,
2
0
1
7
.
[5
]
S
.
F
a
ra
ji
a
n
p
o
u
r,
A
.
M
o
h
a
m
m
a
d
i,
S
.
T
a
v
a
k
o
li
,
a
n
d
S
.
M
.
Ba
ra
k
a
ti
,
“
Im
p
ro
v
e
d
Ba
c
teria
l
F
o
ra
g
in
g
A
l
g
o
rit
h
m
f
o
r
Op
ti
m
u
m
Eco
n
o
m
ic
Em
is
sio
n
Disp
a
tch
w
it
h
W
in
d
P
o
w
e
r,
”
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
ti
o
n
,
C
o
mp
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l)
,
v
o
l.
1
0
,
n
o
.
4
,
2
0
1
2
.
[6
]
I.
Zi
a
n
e
,
F
.
Be
n
h
a
m
id
a
,
a
n
d
Y.
S
a
lh
i,
“
A
F
a
st
S
o
lv
e
r
f
o
r
D
y
n
a
m
ic
Eco
n
o
m
ic
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o
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d
Disp
a
tch
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h
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in
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m
Em
issio
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in
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Qu
a
d
ra
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P
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ra
m
m
in
g
,
”
n
o
.
2
,
p
p
.
2
9
0
–
2
9
4
,
2
0
1
5
.
[7
]
W
.
A
.
A
u
g
u
ste
e
n
,
R.
Ku
m
a
ri,
a
n
d
R.
Re
n
g
a
ra
j,
“
Eco
n
o
m
ic
a
n
d
v
a
rio
u
s
e
m
issio
n
d
isp
a
tch
u
si
n
g
d
if
fe
re
n
ti
a
l
e
v
o
lu
ti
o
n
a
lg
o
rit
h
m
,
”
in
2
0
1
6
3
r
d
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
E
l
e
c
trica
l
En
e
rg
y
S
y
ste
ms
,
ICEE
S
2
0
1
6
,
2
0
1
6
,
p
p
.
74
–
7
8
.
[8
]
V
.
K.
Ja
d
o
u
n
,
N.
G
u
p
ta,
K.
R.
Nia
z
i,
a
n
d
A
.
S
wa
rn
k
a
r,
“
M
o
d
u
l
a
ted
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
f
o
r
e
c
o
n
o
m
ic
e
m
issio
n
d
is
p
a
tch
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
Po
we
r a
n
d
En
e
rg
y
S
y
ste
ms
,
v
o
l.
7
3
,
p
p
.
8
0
–
8
8
,
2
0
1
5
.
[9
]
I.
A
.
F
a
rh
a
t
a
n
d
M
.
E.
El
-
Ha
wa
r
y
,
“
M
u
lt
i
-
o
b
jec
ti
v
e
e
c
o
n
o
m
ic
-
e
m
issio
n
o
p
ti
m
a
l
lo
a
d
d
isp
a
tch
u
sin
g
b
a
c
teria
l
f
o
ra
g
in
g
a
l
g
o
rit
h
m
,
”
2
0
1
2
2
5
th
I
EE
E
Ca
n
a
d
ia
n
Co
n
fer
e
n
c
e
o
n
E
lec
trica
l
a
n
d
Co
m
p
u
ter
E
n
g
i
n
e
e
rin
g
(
CCECE
)
,
p
p
.
1
–
5
,
2
0
1
2
.
[1
0
]
A
.
Ku
m
a
r
a
n
d
S
.
C
h
a
k
a
r
v
e
rt
y
,
“
De
sig
n
o
p
ti
m
iz
a
ti
o
n
u
sin
g
G
e
n
e
ti
c
A
lg
o
rit
h
m
a
n
d
Cu
c
k
o
o
S
e
a
rc
h
,
”
2
0
1
1
Ie
e
e
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
E
lec
tro
/In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
,
p
p
.
1
–
5
,
2
0
1
1
.
[1
1
]
N.
A
ti
q
a
h
,
A
.
Ra
h
m
a
n
,
a
n
d
U.
T
.
M
a
ra
,
“
A
c
ti
v
e
L
o
a
d
a
n
d
L
o
ss
A
l
lo
c
a
ti
o
n
i
n
T
ra
n
s
m
issio
n
L
i
n
e
w
it
h
L
in
e
Ou
tag
e
Co
n
d
it
io
n
v
ia Cu
c
k
o
o
S
e
a
rc
h
Op
t
im
iz
a
ti
o
n
T
e
c
h
n
iq
u
e
,
”
n
o
.
De
c
e
m
b
e
r,
p
p
.
1
6
–
1
7
,
2
0
1
3
.
[1
2
]
M
.
Ju
n
e
ja
a
n
d
S
.
K.
Na
g
a
r,
“
P
a
rti
c
le
s
w
a
r
m
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
a
n
d
it
s
p
a
ra
m
e
ter
s:
A
r
e
v
ie
w
,
”
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
n
tro
l
,
Co
m
p
u
ti
n
g
,
C
o
mm
u
n
ica
ti
o
n
a
n
d
M
a
ter
i
a
l
s
,
n
o
.
Ic
c
c
c
m
,
2
0
1
6
.
[1
3
]
“
Co
n
stra
in
e
d
Dy
n
a
m
ic E
c
o
n
o
m
ic
Disp
a
tch
S
o
lu
t
io
n
Us
in
g
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
.
”
[1
4
]
A
.
S
.
Jo
sh
i,
O.
Ku
lk
a
rn
i,
G
.
M
.
Ka
k
a
n
d
ik
a
r,
a
n
d
V
.
M
.
Na
n
d
e
d
k
a
r,
“
Cu
c
k
o
o
S
e
a
rc
h
Op
ti
m
iza
ti
o
n
-
A
R
e
v
ie
w
,
”
in
M
a
ter
ia
ls
T
o
d
a
y
:
Pro
c
e
e
d
in
g
s
,
2
0
1
7
,
v
o
l.
4
,
n
o
.
8
.
[1
5
]
W
.
S
.
T
a
n
,
M
.
Y.
Ha
ss
a
n
,
M
.
S
.
M
a
ji
d
,
a
n
d
H.
A
.
Ra
h
m
a
n
,
“
A
ll
o
c
a
ti
o
n
a
n
d
siz
in
g
o
f
DG
u
sin
g
Cu
c
k
o
o
S
e
a
rc
h
a
lg
o
rit
h
m
,
”
in
PE
Co
n
2
0
1
2
-
2
0
1
2
IEE
E
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
P
o
we
r a
n
d
En
e
rg
y
,
2
0
1
2
,
p
p
.
1
3
3
–
1
3
8
.
[
1
6
]
M
.
Ba
su
a
n
d
A
.
Ch
o
w
d
h
u
ry
,
“
Cu
c
k
o
o
se
a
rc
h
a
lg
o
rit
h
m
f
o
r
e
c
o
n
o
m
ic d
isp
a
tch
,
”
En
e
rg
y
,
v
o
l.
6
0
,
p
p
.
9
9
–
1
0
8
,
2
0
1
3
.
[1
7
]
Y.
Cu
i,
Z
.
G
e
n
g
,
Q.
Zh
u
,
a
n
d
Y.
Ha
n
,
“
Re
v
ie
w
:
M
u
lt
i
-
o
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
m
e
th
o
d
s
a
n
d
a
p
p
li
c
a
ti
o
n
i
n
e
n
e
rg
y
sa
v
in
g
,
”
En
e
rg
y
,
v
o
l.
1
2
5
.
p
p
.
6
8
1
–
7
0
4
,
2
0
1
7
.
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