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w
id
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liter
at
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[
3
–
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.
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w
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(
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[
7
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I
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p
ac
e
o
f
s
o
l
u
tio
n
s
;
t
h
is
i
m
p
r
o
v
e
m
e
n
t
e
n
s
u
r
es
t
h
e
f
i
n
al
s
o
lu
tio
n
v
alid
it
y
.
P
r
ac
ticall
y
,
t
h
er
e
ar
e
t
w
o
i
m
p
o
r
tan
t
p
r
o
b
lem
s
w
it
h
ap
p
r
o
ac
h
es
b
ased
o
n
co
n
v
e
n
tio
n
al
n
eu
r
al
n
et
w
o
r
k
ar
c
h
itect
u
r
es
.
T
h
e
f
ir
s
t
p
r
o
b
le
m
i
s
t
h
at
H
NN
p
ar
tia
ll
y
m
it
ig
ate
s
t
h
e
p
r
o
b
le
m
o
f
g
ettin
g
s
tu
c
k
in
lo
ca
l
o
p
tim
u
m
.
T
h
e
s
ec
o
n
d
o
n
e
i
s
d
u
e
to
Ho
p
f
ield
n
e
t
w
o
r
k
d
y
n
a
m
ic
w
h
ic
h
co
n
ti
n
u
o
u
s
l
y
e
x
p
lo
r
es
th
e
s
ea
r
c
h
s
p
ac
e
an
d
w
i
ll
n
o
t
al
w
a
y
s
s
tab
ilize
at
b
o
r
d
e
r
0
o
r
1
.
I
f
th
e
s
a
m
e
ca
s
e
ap
p
ea
r
s
,
w
e
g
et
lo
w
s
o
l
u
tio
n
q
u
a
lit
y
o
r
an
in
co
m
p
lete
as
s
i
g
n
m
e
n
t
o
f
v
ar
iab
les
p
r
o
b
lem
.
I
n
o
r
d
er
to
ta
ck
le
th
e
s
e
p
r
o
b
lem
s
,
w
e
p
r
o
p
o
s
e
to
im
p
r
o
v
e
t
h
e
C
HN
s
o
lu
tio
n
b
y
th
e
Mi
n
-
C
o
n
f
lict
h
eu
r
i
s
tic(
MN
C
)
[
1
3
]
.
T
h
e
MN
C
w
o
r
k
s
as
f
o
llo
w
s
:
it
s
elec
ts
th
e
v
a
lu
e
f
r
o
m
ea
ch
v
ar
iab
le
d
o
m
a
in
f
o
r
w
h
ic
h
th
e
to
tal
er
r
o
r
in
th
e
n
ex
t
co
n
f
i
g
u
r
atio
n
w
ill
b
e
m
i
n
i
m
al.
P
r
ec
is
el
y
,
MN
C
is
a
r
ep
air
-
b
ased
s
to
ch
asti
c
ap
p
r
o
ac
h
,
w
h
ic
h
s
tar
ts
w
it
h
a
co
m
p
lete
b
u
t
in
co
n
s
i
s
t
en
t
ass
i
g
n
m
en
t
a
n
d
th
en
r
ep
air
s
v
ar
iab
les
i
m
p
li
ca
ted
in
co
n
s
tr
ain
t
v
i
o
latio
n
s
r
ep
etitiv
el
y
u
n
til
a
co
n
s
i
s
ten
t
ass
ig
n
m
en
t
is
ac
h
iev
ed
,
t
h
is
ap
p
r
o
ac
h
ca
n
s
o
lv
e
lar
g
e
-
s
ca
le
p
r
o
b
lem
s
in
a
p
r
ac
tical
ti
m
e.
T
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
ll
o
w
s
:
I
n
s
ec
tio
n
1
,
w
e
p
r
esen
t
a
m
o
d
eliza
tio
n
o
f
t
h
e
b
in
ar
y
C
SP
as
0
-
1
q
u
ad
r
atic
p
r
o
g
r
a
m
,
a
n
d
g
e
n
er
alize
s
e
n
er
g
y
f
u
n
c
tio
n
a
s
s
o
cia
ted
w
ith
t
h
e
C
SP
s
.
Sec
tio
n
2
i
s
d
ev
o
ted
to
g
i
v
i
n
g
i
m
p
le
m
en
ta
tio
n
d
etails o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
.
I
n
th
e
last
s
ec
tio
n
,
w
e
s
h
o
w
th
e
co
m
p
lex
it
y
a
n
al
y
s
is
a
n
d
th
e
r
esu
lt
s
o
f
t
h
e
n
u
m
er
ica
l e
x
p
er
i
m
en
ts
a
g
ain
s
t o
th
er
ap
p
r
o
ac
h
es
lik
e
t
h
e
Ge
n
etic
A
l
g
o
r
ith
m
[
1
4
–
1
6
]
.
2.
B
I
NARY
CSPS
SO
L
V
E
D
B
Y
CH
N
2
.
1
.
Q
ua
dra
t
ic
m
o
del o
f
Co
ns
t
ra
int
s
a
t
is
f
a
ct
io
n pro
ble
m
A
lar
g
e
n
u
m
b
er
o
f
r
ea
l
p
r
o
b
le
m
s
s
u
c
h
as
ar
ti
f
ic
ial
in
telli
g
en
ce
,
s
ch
ed
u
l
in
g
,
as
s
ig
n
m
e
n
t
p
r
o
b
lem
ca
n
b
e
f
o
r
m
u
lated
as
a
C
o
n
s
tr
ain
t
Sati
s
f
ac
tio
n
P
r
o
b
lem
.
So
lv
in
g
a
C
SP
r
eq
u
ir
es
to
f
in
d
in
g
a
n
ass
i
g
n
m
e
n
t
o
f
all
v
ar
iab
les p
r
o
b
lem
u
n
d
er
co
n
s
t
r
ain
ts
r
estrict
io
n
.
T
h
e
C
SP
ca
n
b
e
f
o
r
m
u
lated
as t
h
r
ee
s
ets
:
1)
Set o
f
N
v
ar
iab
les X=
{
X
i
; 1
≤
i ≤
N
}.
2)
Set
o
f
N
v
ar
iab
les
d
o
m
ai
n
s
:
D
={
D
i
;
1
≤
i
≤
d
i
}
w
h
er
e
ea
ch
Di
co
n
tai
n
s
s
et
o
f
d
i
r
an
g
e
v
alu
e
s
f
o
r
X
i
.
3)
Set o
f
M
co
n
s
tr
ai
n
t
s
: C
={
C
i
; 1
≤
i
≤
M}.
E
ac
h
co
n
s
tr
ain
t
C
i
as
s
o
ciate
s
a
n
o
r
d
er
ed
v
ar
iab
les s
u
b
s
e
t
w
h
i
ch
is
ca
lled
th
e
s
co
p
e
o
f
C
i
.
T
h
e
ar
it
y
o
f
a
co
n
s
tr
ain
t
is
t
h
e
n
u
m
b
er
o
f
in
v
o
l
v
ed
v
ar
iab
les.
W
e
ca
n
ea
s
il
y
r
ef
o
r
m
u
late
C
SP
as
a
Qu
ad
r
atic
P
r
o
b
lem
(
QP
)
,
b
y
in
tr
o
d
u
cin
g
a
b
in
ar
y
v
ar
iab
le
x
ik
f
o
r
ea
ch
C
SP
v
ar
iab
le
x
i
,
w
h
er
e
k
v
ar
ies
o
v
er
t
h
e
r
an
g
e
o
f
x
i
,
g
iv
e
n
as f
o
llo
w
s
:
o
t
h
e
r
w
i
s
e
k
v
a
l
u
e
t
a
k
e
s
i
v
a
r
i
a
b
l
e
if
x
ik
0,
1,
=
(
1
)
Fo
r
ea
ch
b
in
ar
y
co
n
s
tr
ai
n
t
C
ij
,
b
etw
ee
n
t
h
e
v
ar
iab
le
s
y
i
an
d
y
j
,
w
e
a
s
s
o
ciate
a
s
tat
e
f
u
n
ctio
n
d
ef
in
ed
as:
i
r
j
s
js
ir
j
d
s
i
d
r
ij
Q
x
x
x
S
1
=
1
=
=
)
(
(
2
)
W
h
er
e
}
],
[
1
.
.
,
{
=
i
ik
d
k
N
i
x
x
a
v
ec
to
r
o
f
QP
s
o
lu
tio
n
an
d
th
e
q
u
ad
r
atic
ter
m
s
i
r
j
s
Q
d
ef
i
n
ed
as:
o
t
h
e
r
w
is
e
C
s
r
if
Q
ij
i
r
j
s
0
)
,
(
1
=
(
3
)
Fro
m
all
t
h
e
eq
u
a
tio
n
s
d
ef
in
ed
in
(
2
)
,
w
h
ic
h
co
r
r
esp
o
n
d
to
p
r
o
b
lem
co
n
s
tr
ai
n
t
s
,
w
e
d
ed
u
ce
t
h
e
o
b
j
ec
tiv
e
f
u
n
c
tio
n
o
f
it
s
eq
u
i
v
alen
t Q
P
:
i
r
j
s
js
ir
j
d
s
N
j
i
d
r
N
i
Q
x
x
x
f
1
=
1
=
1
=
1
=
=
)
(
(
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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n
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&
C
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p
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4752
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eu
r
a
l Net
w
o
r
k
a
n
d
Lo
ca
l
S
e
a
r
ch
to
S
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lve
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in
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r
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C
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A
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i
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h
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ch
)
1321
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r
t
h
er
m
o
r
e,
s
o
m
e
s
tr
ict
li
n
ea
r
co
n
s
tr
ain
t
s
eq
u
at
io
n
s
m
u
s
t b
e
s
atis
f
ied
b
y
t
h
e
s
o
l
u
tio
n
:
1
=
=1
ir
i
d
r
x
,
f
o
r
N
i
1
.
.
=
w
h
ic
h
ca
n
b
e
w
r
itte
n
als
o
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Ax
=
(
A
is
a
M
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m
atr
i
x
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d
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io
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f
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ialized
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w
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t
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x
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n
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{
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(
)
(
(
5
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S
y
s
te
m
a
ticall
y
,
to
s
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l
v
e
t
h
e
la
s
t
Q
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ad
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atic
Op
ti
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izatio
n
P
r
o
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le
m
w
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h
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p
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ield
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o
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ee
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u
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e
n
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g
y
f
u
n
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n
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ch
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e
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ea
s
ib
le
s
o
lu
t
io
n
s
o
f
t
h
e
p
r
o
b
lem
co
r
r
esp
o
n
d
in
g
to
th
e
m
i
n
i
m
al
o
f
C
HN
en
er
g
y
f
u
n
ctio
n
.
2
.
2
.
H
o
pfie
ld neura
l net
w
o
r
k
Ho
p
f
ield
n
e
u
r
al
n
et
w
o
r
k
w
as
in
tr
o
d
u
ce
d
b
y
Ho
p
f
ield
an
d
T
an
k
[
1
7
]
[
1
8
]
[
1
9
]
.
A
t
t
h
e
b
e
g
in
n
i
n
g
,
i
t
w
a
s
d
ev
elo
p
ed
to
s
o
l
v
e
co
m
b
i
n
ato
r
ial
o
p
ti
m
izatio
n
p
r
o
b
lem
s
.
T
h
en
i
t
w
a
s
e
x
ten
d
ed
e
x
te
n
s
iv
el
y
to
o
t
h
er
ar
ea
s
o
f
ap
p
licatio
n
,
s
u
c
h
as
r
ec
o
g
n
itio
n
an
d
o
p
ti
m
izatio
n
.
Ho
p
f
ield
n
eu
r
al
n
et
w
o
r
k
is
cla
s
s
i
f
ied
as
a
n
e
f
f
icien
t
lo
ca
l
s
ea
r
ch
,
w
h
ic
h
g
i
v
es
a
n
ac
ce
p
tab
le
s
o
l
u
tio
n
to
h
ar
d
o
p
ti
m
izatio
n
p
r
o
b
lem
s
at
a
r
ea
s
o
n
ab
le
ti
m
e.
B
asicall
y
,
t
h
i
s
n
eu
r
al
n
e
t
w
o
r
k
m
o
d
el
i
s
a
f
u
ll
y
co
n
n
ec
ted
n
eu
r
al
n
et
w
o
r
k
a
n
d
it
s
d
y
n
a
m
i
c
s
tat
s
ar
e
g
o
v
er
n
ed
b
y
t
h
e
f
o
llo
w
in
g
d
if
f
er
en
tial e
q
u
atio
n
:
b
i
x
T
x
dt
dy
=
(
6
)
W
h
er
e:
x
: v
e
cto
r
o
f
n
e
u
r
o
n
s
in
p
u
t
y
: v
ec
to
r
o
f
o
u
tp
u
t
T
: m
atr
ix
o
f
w
e
ig
h
t b
et
w
ee
n
ea
ch
n
e
u
r
o
n
es p
air
s
i
b
: T
h
e
b
iases
w
h
ich
d
escr
ib
e
th
e
n
e
u
r
o
n
s
s
el
f
-
i
n
ter
n
al
n
o
i
s
es.
Ho
p
f
ield
p
r
o
v
ed
th
at
if
T
is
s
y
m
m
etr
ic
th
e
n
t
h
e
n
et
w
o
r
k
en
er
g
ies i
s
a
L
y
ap
u
n
o
v
f
u
n
ctio
n
:
(
7
)
Fo
r
ea
ch
n
eu
r
o
n
s
,
t
h
e
in
p
u
t
is
g
o
v
er
n
ed
b
y
a
n
ac
ti
v
atio
n
f
u
n
ctio
n
x
=
g
(
y
)
w
h
ic
h
v
ar
ies
b
et
w
ee
n
0
an
d
1
.
T
h
is
f
u
n
ctio
n
i
s
g
i
v
e
n
b
y
:
))
(
t
a
n
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(1
2
1
=
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(
0
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d
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h
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s
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x
x
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n
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m
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1
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ad
ap
ted
to
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o
lv
e
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e
q
u
ad
r
atic
f
o
r
m
u
latio
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o
f
th
e
b
i
n
ar
y
C
SP
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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ed
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[
8
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T
h
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P
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ce
d
u
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et
w
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n
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d
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tio
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f
.
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h
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ce
s
s
s
p
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s
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p
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e
n
e
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et
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n
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ce
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ig
n
i
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y
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Fu
r
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m
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to
as
s
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th
e
s
tab
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w
e
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la
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s
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m
et
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w
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lc
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lates
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h
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L
e
t
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e
f
o
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s
m
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s
t
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m
p
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ed
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en
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>
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to
h
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a
m
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n
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m
izatio
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ir
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e
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ce
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av
o
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t
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ed
to
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s
a
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w
it
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d
th
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d
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n
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n
s
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(
x
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m
u
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y
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1
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en
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t
h
at
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m
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a
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e
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E
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x
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m
u
s
t v
er
i
f
y
also
:
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(
x
)
d
+
+
(
1
3
)
W
ith
:
E
m
p
ir
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,
t
h
e
b
est v
a
lu
e
o
f
is
1
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ar
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ett
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[
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=
=
2
0
2
0
>
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d
(
1
4
)
T
h
e
C
HN
p
ar
a
m
eter
s
s
et
tin
g
an
d
t
h
e
s
tar
ti
n
g
p
o
in
t
u
s
ed
in
th
i
s
p
ap
er
ar
e
s
i
m
ila
r
to
[
1
0
]
.
Fu
r
t
h
er
m
o
r
e,
a
p
r
o
ce
d
u
r
e
w
h
i
ch
allo
w
s
ea
ch
n
e
u
r
o
n
o
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th
e
s
a
m
e
v
ar
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to
tak
e
0
,
if
o
n
e
co
n
v
er
g
es
to
th
e
b
o
ar
d
e
r
1
,
is
ad
d
ed
.
T
h
is
p
r
o
c
ed
u
r
e
is
ca
lled
Mo
d
er
ato
r
(
Fig
u
r
e
1
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
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&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
N
eu
r
a
l Net
w
o
r
k
a
n
d
Lo
ca
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S
e
a
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ch
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in
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C
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(
A
d
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B
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ch
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1323
Fig
u
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e
1
.
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r
T
h
is
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p
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w
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p
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w
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(
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,
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v
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ied
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n
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o
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C
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n
s
tr
ain
t
s
s
at
is
f
ac
tio
n
p
r
o
b
lem
[
8
]
,
b
u
t
s
i
m
u
latio
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p
r
o
v
ed
th
at
t
h
i
s
ap
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Fo
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y
t
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e
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est
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T
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attr
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ai
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et
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et
w
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t.
A
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r
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ield
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et
w
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s
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m
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e,
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ch
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u
tp
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t
ca
n
ta
k
e
an
y
v
alu
e
b
et
w
ee
n
0
an
d
1
.
So
,
th
e
n
et
w
o
r
k
ca
n
b
e
s
tr
a
n
d
ed
at
a
l
o
ca
l
m
i
n
i
m
u
m
w
h
ic
h
co
n
tain
s
s
o
m
e
u
n
i
ts
th
at
s
t
ill
tak
e
r
ea
l
v
alu
e
s
.
I
n
t
h
i
s
ca
s
e,
w
e
o
b
tai
n
an
in
v
alid
s
o
l
u
tio
n
f
o
r
C
SP
.
T
o
o
v
er
co
m
e
t
h
i
s
n
e
u
r
al
n
et
w
o
r
k
w
ea
k
n
es
s
,
th
e
m
ai
n
id
ea
o
f
t
h
i
s
w
o
r
k
is
to
r
ep
air
th
e
s
o
lu
tio
n
g
i
v
en
b
y
t
h
e
C
HN
an
d
i
m
p
r
o
v
e
it b
y
a
k
n
o
w
n
M
in
-
co
n
f
lic
t a
lg
o
r
it
h
m
.
3.
CH
N
AND
M
I
N
CO
NF
L
I
C
T
H
E
UR
I
S
T
I
C
T
O
SO
L
V
E
C
SPS
T
h
er
e
ar
e
m
a
n
y
m
e
th
o
d
s
wh
ich
co
m
b
i
n
e
t
w
o
o
r
m
o
r
e
n
o
ex
ac
t
s
ap
p
r
o
ac
h
es
to
s
o
l
v
e
a
g
iv
e
n
o
p
tim
izatio
n
p
r
o
b
le
m
[
1
1
,
2
0
–
2
3
]
.
I
n
th
e
s
a
m
e
d
ir
ec
tio
n
w
e
i
n
tr
o
d
u
ce
a
h
y
b
r
id
ap
p
r
o
ac
h
b
ased
C
HN
a
n
d
MN
C
.
T
h
e
MN
C
al
g
o
r
ith
m
[
1
3
]
is
a
v
er
y
s
i
m
p
le
a
n
d
f
ast
lo
ca
l r
ep
air
in
g
m
e
th
o
d
to
r
eso
l
v
e
C
SP
s
,
w
h
ic
h
ai
m
s
at
ass
i
g
n
i
n
g
all
th
e
v
ar
iab
les
r
an
d
o
m
l
y
.
Ne
x
t,
it
iter
ati
v
el
y
s
elec
ts
o
n
e
v
ar
iab
le
f
r
o
m
th
e
s
et
o
f
th
e
v
ar
iab
le
s
w
it
h
co
n
f
lict
s
w
h
ic
h
v
io
lates
o
n
e
o
r
m
o
r
e
co
n
s
tr
ain
t
s
o
f
t
h
e
C
SP
.
T
h
en
,
it
ass
ig
n
s
a
v
alu
e
to
th
e
s
elec
ted
v
ar
iab
le,
s
o
th
at
it
ca
n
m
in
i
m
ize
th
e
n
u
m
b
er
o
f
co
n
f
lict
s
.
MN
C
h
as
d
e
m
o
n
s
tr
ated
to
b
e
ab
le
to
s
o
lv
e
th
e
q
u
ee
n
s
p
r
o
b
le
m
i
n
m
i
n
u
tes
[
2
4
]
.
MN
C
i
s
w
id
el
y
u
s
ed
to
co
n
s
tr
u
ct
h
y
b
r
id
al
g
o
r
it
h
m
s
w
i
th
o
th
er
o
p
ti
m
izatio
n
s
[
2
5
–
2
8
]
.
I
n
th
is
w
a
y
,
t
h
e
b
asic
id
ea
o
f
o
u
r
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
is
to
u
s
e
MN
C
to
i
m
p
r
o
v
e
t
h
e
s
o
lu
t
io
n
r
ea
ch
ed
b
y
C
HN.
T
h
is
w
i
ll b
e
d
o
n
e
in
t
w
o
s
tep
s
(
s
ee
Fi
g
u
r
e
2
)
.
First,
MN
C
v
i
s
it
s
all
as
s
ig
n
ed
v
ar
i
ab
les;
f
o
r
ea
ch
o
n
e
,
w
e
ap
p
l
y
Min
-
C
o
n
f
lict
d
ir
ec
t
l
y
to
th
e
n
eu
r
al
n
et
w
o
r
k
s
tr
u
c
tu
r
e,
th
en
,
it
r
etu
r
n
s
th
e
b
est
ass
i
g
n
m
e
n
t
f
o
r
th
e
cu
r
r
en
t
v
ar
iab
le
(
s
ee
Fi
g
u
r
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o
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ir
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2
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4
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
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n
esia
n
J
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lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
1
0
,
No
.
3
,
J
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n
e
2
0
1
8
: 1
3
1
9
–
1
3
3
0
1326
4.
NUM
E
RICAL
R
E
SU
L
T
S
4
.
1
.
G
ener
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t
ed
ra
nd
o
m
ly
in
s
t
a
nces
T
o
ev
alu
ate
t
h
e
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er
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o
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m
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ce
o
f
p
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p
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ed
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in
g
m
et
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o
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s
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w
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r
u
n
s
o
m
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r
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m
in
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x
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n
t
s
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th
e
r
an
d
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l
y
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e
n
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ated
p
r
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lem
s
an
d
w
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m
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t
h
e
s
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tio
n
q
u
a
lit
y
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y
t
h
e
n
u
m
b
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n
s
.
Si
m
u
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n
h
a
s
b
ee
n
d
o
n
e
w
i
t
h
th
e
f
o
llo
w
in
g
m
ac
h
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ch
ar
ac
ter
is
tics
:
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5
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r
e(
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p
r
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s
s
o
r
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d
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m
e
m
o
r
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to
2
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r
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p
ar
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m
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s
,
th
e
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t v
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s
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f
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n
d
ed
e
m
p
ir
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ll
y
:
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g
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er
ate
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an
d
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m
p
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lem
s
,
w
e
u
s
e
a
r
an
d
o
m
g
en
er
ato
r
b
ased
ex
ten
d
ed
m
o
d
el
B
as
it
is
d
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in
[
2
9
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3
1]
.
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h
is
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te
n
d
ed
m
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d
el
w
h
ich
is
ca
lled
Mo
d
el
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is
ab
le
to
g
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ate
a
h
ar
d
s
ati
s
f
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n
s
tan
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o
s
u
m
m
ar
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s
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e
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m
e
th
o
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,
a
r
an
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i
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ed
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y
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h
e
f
o
llo
w
i
n
g
f
i
v
e
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n
p
u
t p
a
r
a
m
eter
s
:
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k
: d
en
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tes th
e
ar
it
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o
f
ea
ch
co
n
s
tr
ai
n
t,
f
ix
ed
at
k
=2
,
s
o
all
co
n
s
tr
ai
n
ts
ar
e
b
in
ar
y
•
n
: d
en
o
tes th
e
n
u
m
b
er
o
f
v
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i
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les,
•
α
: d
eter
m
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es t
h
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o
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ai
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=n
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f
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ar
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le,
•
r
>0
: d
eter
m
in
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s
th
e
n
u
m
b
er
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=
r
n
l
n
(
n
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f
co
n
s
tr
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n
ts
,
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1>
p
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: d
eter
m
in
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s
th
e
n
u
m
b
e
r
t
=
p
d
k
o
f
d
is
allo
w
ed
tu
p
le
s
o
f
ea
ch
r
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.
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n
th
e
f
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llo
w
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g
,
a
class
o
f
C
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s
w
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e
d
en
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b
y
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tu
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o
f
th
e
f
o
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m
<
n
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m
,
k
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r
s
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,
w
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ate
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d
n
in
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0
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0
},
a
n
d
w
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ca
lcu
late
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o
f
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n
s
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t
s
o
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n
s
ta
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in
t
h
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s
a
m
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o
f
p
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e.
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r
th
e
m
o
d
el
p
ar
am
eter
u
s
ed
,
w
e
g
et
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0
,
1
1
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p
>,
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5
,
3
0
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d
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0
,
1
9
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4
4
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,
p
>.
T
h
e
co
r
r
esp
o
n
d
in
g
lo
ad
cu
r
v
e
i
s
g
i
v
e
n
i
n
F
ig
u
r
es
5
,
6
an
d
7
r
esp
ec
tiv
el
y
.
C
o
m
p
ar
is
o
n
is
m
ad
e
b
et
w
ee
n
th
e
s
o
lu
tio
n
o
b
tai
n
ed
b
y
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
an
d
t
h
e
s
o
lu
tio
n
g
iv
e
n
b
y
t
h
e
o
r
i
g
i
n
al
ap
p
r
o
ac
h
[
1
0
]
.
As
w
e
ca
n
s
ee
Min
-
C
o
n
f
lict i
m
p
r
o
v
es
t
h
e
q
u
alit
y
o
f
th
e
s
o
lu
tio
n
co
n
s
id
er
ab
l
y
ar
o
u
n
d
m
ea
n
5
0
%,
an
d
th
e
s
u
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e
s
s
o
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n
et
w
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is
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p
to
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b
u
t
f
o
r
C
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ap
p
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h
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f
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e
m
p
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m
ea
n
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e
7
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% o
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er
all
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u
n
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.
Fig
u
r
e
5
.
nu
m
b
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o
f
v
io
latio
n
o
v
er
class
e
N=
2
0
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
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m
p
Sci
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N:
2502
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4752
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r
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S
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a
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r
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i
B
o
u
h
o
u
ch
)
1327
Fig
u
r
e
6
.
n
u
m
b
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o
f
v
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latio
n
o
v
er
class
e
N=
3
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Fig
u
r
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7
.
n
u
m
b
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f
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n
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v
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class
e
N=
4
0
4
.
2
T
y
pica
l ins
t
a
nces
Fo
r
s
h
o
w
in
g
t
h
e
p
r
ac
tical
in
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r
est
o
f
o
u
r
ap
p
r
o
ac
h
,
w
e
also
s
tu
d
y
its
p
er
f
o
r
m
a
n
ce
o
v
er
p
r
o
b
lem
s
o
f
d
if
f
er
e
n
t
n
at
u
r
es
(
r
an
d
o
m
,
ac
a
d
em
ic
an
d
r
ea
l
-
w
o
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ld
p
r
o
b
lem
s
)
[
3
2
]
.
T
h
e
g
o
al
is
to
e
v
al
u
ate
its
p
er
f
o
r
m
an
ce
w
it
h
o
t
h
er
ev
o
lu
tio
n
ar
y
a
lg
o
r
ith
m
s
[
3
3
,
3
4
]
.
T
h
u
s
,
w
e
co
m
p
ar
e
it
s
e
f
f
icien
c
y
w
it
h
t
h
e
Gen
e
tic
A
l
g
o
r
ith
m
(
GA
)
[
3
5
]
an
d
th
e
P
ar
ticu
lar
Sw
ar
m
Op
ti
m
izatio
n
(
P
SO)
[
3
6
,
3
7
]
.
I
n
p
r
ac
tice,
r
ath
er
th
an
a
u
th
o
r
s
s
etti
n
g
s
w
e
h
av
e
e
m
p
ir
icall
y
s
ea
r
ch
in
g
t
h
e
Gen
etic
A
l
g
o
r
ith
m
a
n
d
P
SO
g
o
o
d
s
o
n
es,
ad
o
p
ted
to
C
SP
p
r
o
b
lem
.
So
,
f
o
r
G
A
th
e
p
o
p
u
latio
n
w
as
2
0
0
in
d
iv
i
d
u
als,
m
u
tatio
n
r
ate
eq
u
al
s
to
5
%
an
d
cr
o
s
s
i
n
g
r
ate
eq
u
al
s
t
o
7
2
%,
as
f
o
r
P
SO
[
3
6
,
3
7
]
w
e
ch
o
s
e
an
d
p
o
p
u
latio
n
s
ize
f
ix
ed
at
1
0
0
.
W
e
also
r
u
n
ea
ch
o
n
e
2
0
0
tim
e
s
.
T
h
e
C
HN
p
ar
a
m
eter
s
s
etti
n
g
an
d
th
e
s
tar
ti
n
g
p
o
i
n
t
u
s
ed
in
th
is
p
ap
er
ar
e
s
i
m
ilar
to
[
1
0
]
,
w
e
h
a
v
e
u
s
ed
th
e
Var
iab
le
Up
d
atin
g
S
t
ep
(
VUS)
tech
n
iq
u
e
p
r
o
p
o
s
ed
b
y
T
alav
án
an
d
Yáñ
e
z
i
n
[
8
]
.
T
ab
le
1
s
h
o
w
s
th
e
co
m
p
ar
is
o
n
b
et
w
ee
n
o
u
r
ap
p
r
o
ac
h
C
H
N
-
M
NC
a
n
d
o
r
ig
i
n
a
l
C
HN.
T
h
e
d
escr
ip
tio
n
o
f
ta
b
le
co
lu
m
n
s
is
th
e
f
o
llo
w
in
g
:
•
V:
th
e
n
u
m
b
er
o
f
v
ar
iab
les.
•
C
: th
e
n
u
m
b
er
o
f
co
n
s
tr
ain
ts
.
•
R
atio
m
ea
n
: t
h
e
av
er
a
g
e
o
f
th
e
o
p
tim
al
v
al
u
e
in
a
2
0
0
r
u
n
.
•
T
im
e:
t
h
e
av
er
a
g
e
o
f
th
e
t
i
m
e
o
f
all
r
u
n
s
.
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2
NB
:
c
o
m
pos
e
d
*
i
s
th
e
in
s
ta
n
c
e
c
o
m
pos
e
d
-
25
-
10
-
20
-
5
T
ab
le
1
w
e
lear
n
th
at
G
A
an
d
C
HN
-
MN
C
ar
e
clo
s
e
an
d
b
o
th
b
etter
th
an
P
SO,
b
u
t
r
e
g
ar
d
in
g
th
e
m
ea
n
t
i
m
e
ta
k
e
n
b
y
all
co
m
p
ar
ed
alg
o
r
ith
m
s
C
H
N
-
M
NC
i
s
th
e
s
h
o
r
t
o
n
e.
G
A
[
3
5
]
h
av
e
th
e
s
a
m
e
p
r
in
cip
le
w
it
h
o
u
r
ap
p
r
o
ac
h
w
h
ile
th
e
y
u
s
e
G
A
a
n
d
Mi
n
i
m
izatio
n
o
f
co
n
f
lict
s
,
b
u
t
t
h
e
y
i
m
p
r
o
v
e
t
h
e
b
est
i
n
d
iv
id
u
al.
I
t’
s
n
o
t
s
u
r
e
t
h
at
i
m
p
r
o
v
in
g
a
b
est
s
o
lu
tio
n
w
ill
g
iv
e
t
h
e
g
o
o
d
o
n
e,
it
ca
n
b
e
n
ea
r
th
e
w
o
r
s
t
o
n
e
i
n
t
h
e
p
o
p
u
latio
n
.
Fo
r
th
i
s
r
ea
s
o
n
,
t
h
e
m
ea
n
s
v
alu
e
o
f
t
h
e
m
u
lti
p
le
r
u
n
s
o
f
o
u
r
ap
p
r
o
ac
h
w
as
th
e
b
est
b
ec
a
u
s
e
i
t
i
m
p
r
o
v
es e
ac
h
f
o
u
n
d
ed
s
o
lu
t
i
o
n
.
5.
CO
NCLU
SI
O
N
I
n
th
i
s
p
ap
er
,
w
e
h
a
v
e
p
r
o
p
o
s
ed
a
n
e
w
ap
p
r
o
ac
h
f
o
r
s
o
l
v
in
g
b
in
ar
y
co
n
s
tr
ain
t
s
atis
f
ac
tio
n
p
r
o
b
lem
s
.
Ou
r
h
y
b
r
id
alg
o
r
it
h
m
g
i
v
es
a
g
o
o
d
s
o
lu
tio
n
q
u
alit
y
r
ath
er
t
h
an
u
s
in
g
C
HN
alo
n
e,
th
i
s
i
m
p
r
o
v
e
m
en
t
is
d
o
n
e
b
y
ad
d
in
g
a
n
o
i
m
p
o
r
tu
n
ed
ti
m
e
co
m
p
u
ta
tio
n
.
Fu
r
t
h
er
m
o
r
e
th
e
r
ate
o
f
n
et
w
o
r
k
s
u
cc
e
s
s
to
g
i
v
e
a
v
al
id
s
o
lu
tio
n
is
u
p
to
1
0
0
%
b
y
r
ep
air
in
g
.
A
ls
o
,
th
e
r
es
u
lts
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Me
eti
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ch
e
d
u
lin
g
.
RE
F
E
R
E
NC
E
S
[1
]
K.
L
e
n
in
,
B.
R.
Re
d
d
y
,
a
n
d
M
.
S
.
Ka
lav
a
th
i,
“
W
o
l
f
se
a
rc
h
a
lg
o
rit
h
m
f
o
r
so
lv
in
g
o
p
ti
m
a
l
re
a
c
ti
v
e
(IJEEI
),
v
o
l.
3
,
n
o
.
1
,
p
p
.
7
–
1
5
,
2
0
1
5
.
[2
]
A
.
C
a
rv
a
lh
o
a
n
d
C.
S
a
n
to
s,
“
A
g
e
n
e
ra
to
r
o
f
h
e
a
v
y
-
tailed
se
a
rc
h
tree
s,”
in
Re
c
e
n
t
D
e
v
e
l
o
p
m
e
n
ts
in
d
e
li
n
g
a
n
d
A
p
p
li
c
a
ti
o
n
s i
n
S
tatisti
c
s.
S
p
ri
n
g
e
r,
2
0
1
3
,
p
p
.
1
0
7
–
1
1
3
.
[3
]
B.
A
.
N
a
d
e
l,
“
T
r
e
e
s
e
a
rc
h
a
n
d
a
rc
c
o
n
siste
n
c
y
in
c
o
n
stra
in
t
sa
ti
sf
a
c
ti
o
n
a
lg
o
rit
h
m
s,”
in
S
e
a
rc
h
in
A
rti
f
icia
l
In
telli
g
e
n
c
e
.
S
p
ri
n
g
e
r,
1
9
8
8
,
p
p
.
2
8
7
–
3
4
2
.
[4
]
C.
Be
s
siè
re
,
P
.
M
e
se
g
u
e
r
,
E.
C.
F
re
u
d
e
r,
a
n
d
J.
L
a
rro
sa
,
“
On
f
o
rwa
rd
c
h
e
c
k
in
g
f
o
r
n
o
n
b
in
a
ry
c
o
n
stra
in
t
sa
ti
sfa
c
ti
o
n
,
”
in
P
rin
c
i
p
les
a
n
d
P
r
a
c
ti
c
e
o
f
Co
n
stra
in
t
P
r
o
g
ra
m
m
in
g
–
C
P
9
9
.
S
p
ri
n
g
e
r,
1
9
9
9
,
p
p
.
8
8
–
1
0
2
.
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