I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
p
u
t
er
Science
Vo
l.
22
,
No
.
3
,
J
u
n
e
2
0
2
1
,
p
p
.
1573
~
1
5
7
9
I
SS
N:
2
5
02
-
4
7
5
2
,
DOI
: 1
0
.
1
1
5
9
1
/i
j
ee
cs.v
2
2
.i
3
.
pp
1
5
7
3
-
1
5
7
9
1573
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
c
s
.
ia
esco
r
e.
co
m
I
m
pro
v
ed grey
w
o
lf
a
lg
o
rith
m
f
o
r
o
pti
m
i
z
a
tion pro
ble
m
s
H
a
f
iz
M
a
a
z
Asg
her
1
,
Ya
na
M
a
zw
in
M
o
h
m
a
d H
a
s
s
i
m
2
,
Ro
za
ida
G
ha
za
li
1
,
M
uh
a
m
ma
d Aa
m
ir
3
1
,
2,
3
F
a
c
u
l
ty
o
f
Co
m
p
u
ter S
c
ien
c
e
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
si
a
(UT
HM),
M
a
la
y
sia
3
Sc
h
o
o
l
o
f
El
e
c
tro
n
ics
,
Co
m
p
u
ti
n
g
a
n
d
M
a
th
e
m
a
ti
c
s,
Un
iv
e
rsit
y
o
f
De
rb
y
,
Un
it
e
d
Kin
g
d
o
m
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
1
,
2
0
2
0
R
ev
i
s
ed
A
u
g
1
0
,
2
0
2
0
A
cc
ep
ted
Sep
2
5
,
2
0
2
0
T
h
e
g
re
y
w
o
l
f
o
p
ti
m
iza
ti
o
n
(G
W
O)
is
a
n
a
tu
re
in
s
p
ired
a
n
d
m
e
ta
-
h
e
u
risti
c
a
lg
o
rit
h
m
,
it
h
a
s
su
c
c
e
ss
f
u
ll
y
so
lv
e
d
m
a
n
y
o
p
ti
m
iza
ti
o
n
p
r
o
b
lem
s
a
n
d
g
iv
e
b
e
tt
e
r
so
l
u
ti
o
n
a
s
c
o
m
p
a
re
to
o
t
h
e
r
a
lg
o
rit
h
m
s.
Ho
w
e
v
e
r,
d
u
e
t
o
it
s
p
o
o
r
e
x
p
lo
ra
ti
o
n
c
a
p
a
b
il
it
y
,
it
h
a
s
im
b
a
lan
c
e
r
e
latio
n
b
e
tw
e
e
n
e
x
p
lo
ra
ti
o
n
a
n
d
e
x
p
lo
it
a
ti
o
n
.
T
h
e
re
f
o
re
,
i
n
th
is
re
se
a
rc
h
w
o
rk
,
th
e
p
o
o
r
e
x
p
lo
ra
ti
o
n
p
a
rt
o
f
GW
O
wa
s
i
m
p
ro
v
e
d
th
ro
u
g
h
h
y
b
rid
w
it
h
w
h
a
le
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
(
W
O
A
)
e
x
p
lo
ra
ti
o
n
.
T
h
e
p
r
o
p
o
se
d
g
re
y
w
o
lf
w
h
a
le
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
(GWW
O
A
)
w
a
s
e
v
a
lu
a
ted
o
n
f
iv
e
u
n
im
o
d
a
l
a
n
d
f
iv
e
m
u
lt
i
m
o
d
a
l
b
e
n
c
h
m
a
rk
f
u
n
c
ti
o
n
s.
T
h
e
re
su
lt
s
sh
o
w
s
th
a
t
GWW
O
A
o
ffe
re
d
b
e
tt
e
r
e
x
p
lo
ra
ti
o
n
a
b
il
it
y
a
n
d
a
b
le
t
o
so
lv
e
th
e
o
p
ti
m
iza
ti
o
n
p
r
o
b
lem
a
n
d
g
iv
e
b
e
tt
e
r
so
lu
ti
o
n
in
se
a
rc
h
sp
a
c
e
.
A
d
d
it
io
n
a
ll
y
,
GWW
O
A
r
e
su
lt
s
w
e
re
we
ll
b
a
lan
c
e
d
a
n
d
g
a
v
e
th
e
m
o
st
o
p
ti
m
a
l
in
se
a
rc
h
sp
a
c
e
a
s
c
o
m
p
a
re
to
th
e
sta
n
d
a
rd
G
WO
a
n
d
W
OA
a
lg
o
rit
h
m
s
.
K
ey
w
o
r
d
s
:
E
x
p
lo
itatio
n
E
x
p
lo
r
atio
n
Gr
e
y
w
o
l
f
o
p
ti
m
izatio
n
Op
ti
m
izatio
n
W
h
ale
o
p
ti
m
izatio
n
alg
o
r
it
h
m
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
C
C
BY
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Haf
iz
Ma
az
A
s
g
h
er
Facu
lt
y
o
f
C
o
m
p
u
ter
Scien
ce
an
d
I
n
f
o
r
m
atio
n
T
ec
h
n
o
lo
g
y
Un
i
v
er
s
iti T
u
n
H
u
s
s
ei
n
On
n
Ma
la
y
s
ia
(
UT
HM
)
P
ar
it R
aj
a,
8
6
4
0
0
,
B
atu
P
ah
at,
J
o
h
o
r
,
Ma
lay
s
ia
E
m
ail:
m
aa
za
s
g
h
ar
9
6
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Op
ti
m
izatio
n
tech
n
iq
u
e
s
p
la
y
i
m
p
o
r
tan
t
r
o
le
in
en
g
in
ee
r
i
n
g
d
esig
n
,
in
f
o
r
m
atio
n
s
cien
ce
,
e
co
n
o
m
ic
s
m
an
a
g
e
m
e
n
t,
o
p
er
atio
n
al
r
es
ea
r
ch
,
an
d
r
elate
d
ar
ea
s
[
1
]
,
[
2
]
.
I
n
co
m
p
u
ter
s
cien
ce
a
n
d
m
at
h
e
m
atic
s
,
a
p
r
o
b
lem
is
s
aid
to
b
e
an
o
p
ti
m
izatio
n
p
r
o
b
lem
;
if
,
it
h
as
m
an
y
v
iab
le
s
o
l
u
tio
n
s
an
d
th
e
o
p
tim
al
s
o
lu
tio
n
is
r
eq
u
ir
ed
to
b
e
f
o
u
n
d
am
o
n
g
a
ll
th
e
f
ea
s
ib
le
s
o
l
u
tio
n
s
b
y
ap
p
ly
in
g
th
e
least
p
o
s
s
ib
le
co
s
t
[
3
]
,
[
4
]
.
R
ec
en
tl
y
,
m
eta
h
eu
r
i
s
tics
alg
o
r
it
h
m
s
h
a
v
e
b
ee
n
u
s
ed
to
s
o
l
v
e
m
an
y
o
p
ti
m
izat
io
n
p
r
o
b
le
m
s
.
So
m
e
lat
est
p
o
p
u
lar
l
y
u
s
ed
ar
e
p
ar
ticle
s
w
ar
m
o
p
ti
m
izati
o
n
(
P
SO)
[
5
]
-
[
7
]
,
a
r
tif
icial
b
ee
c
o
lo
n
y
(
A
B
C
)
[
8
]
,
[
9
]
,
cu
ck
o
o
s
ea
r
ch
a
lg
o
r
ith
m
(
C
S
A
)
[
1
0
]
,
[
1
1
]
,
w
h
ale
o
p
ti
m
izatio
n
alg
o
r
it
h
m
(
W
OA
)
[
1
2
]
,
f
ir
ef
l
y
alg
o
r
it
h
m
(
F
A)
[
1
3
]
,
an
t
co
lo
n
y
o
p
tim
izatio
n
(
AC
O)
[
1
4
]
an
d
b
at
a
lg
o
r
ith
m
(
B
A
)
[
1
5
]
.
T
h
is
s
tu
d
y
f
o
cu
s
es
o
n
th
e
n
at
u
r
e
in
s
p
ir
ed
g
r
e
y
w
o
l
f
o
p
ti
m
i
za
tio
n
(
GW
O)
alg
o
r
ith
m
[
1
2
]
.
GW
O
is
a
s
w
ar
m
in
tel
lig
e
n
t
a
lg
o
r
it
h
m
t
h
at
f
o
llo
w
s
th
e
b
asic
p
r
in
cip
le
s
o
f
lead
er
s
h
ip
h
ier
ar
ch
y
a
n
d
h
u
n
t
in
g
b
e
h
av
io
r
o
f
g
r
e
y
w
o
l
v
es
in
n
at
u
r
e.
D
u
e
to
its
s
i
m
p
lic
it
y
,
GW
O
h
a
s
b
ee
n
w
id
el
y
u
tili
ze
d
to
s
o
lv
e
m
an
y
p
r
ac
tical
o
p
tim
izatio
n
p
r
o
b
lem
s
[
1
6
]
-
[
2
0
]
.
Fo
r
a
w
ell
-
es
tab
lis
h
ed
s
o
lu
tio
n
s
ea
r
c
h
s
p
ac
e
g
e
n
er
atio
n
,
a
f
i
n
e
b
ala
n
ce
i
s
r
eq
u
ir
ed
b
et
w
ee
n
t
h
e
e
x
p
lo
r
atio
n
an
d
e
x
p
lo
itatio
n
p
r
o
ce
s
s
es
f
o
r
f
i
n
d
in
g
a
n
o
p
ti
m
i
ze
d
s
o
lu
tio
n
i
n
a
m
eta
h
eu
r
i
s
tic
[
2
1
]
.
GW
O
alg
o
r
ith
m
is
a
m
eta
h
e
u
r
is
tic
s
al
g
o
r
ith
m
th
at
is
u
n
f
o
r
tu
n
a
tel
y
n
o
t
ab
le
to
en
s
u
r
e
a
f
i
n
e
b
alan
ce
b
et
w
ee
n
t
h
e
ex
p
l
o
r
atio
n
(
f
in
d
i
n
g
n
e
w
s
ea
r
c
h
s
p
ac
e)
an
d
ex
p
lo
itatio
n
(
f
i
n
d
in
g
s
o
lu
tio
n
i
n
lo
ca
l
s
ea
r
ch
s
p
ac
e)
o
f
th
e
s
o
lu
tio
n
s
ea
r
ch
s
p
ac
e
d
u
r
in
g
th
e
g
r
e
y
w
o
l
f
p
o
s
itio
n
u
p
d
ate
s
tag
e
[
2
2
]
.
T
h
is
im
b
ala
n
ce
d
r
elatio
n
s
h
ip
b
et
w
ee
n
t
h
e
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
i
n
GW
O
r
esu
lts
in
co
n
v
er
g
e
n
ce
to
w
ar
d
s
d
eg
r
ad
ed
s
o
lu
tio
n
q
u
alit
y
[
2
3
]
.
I
t
also
h
as
p
o
o
r
ex
p
lo
r
atio
n
ca
p
ab
ilit
y
at
s
m
all
r
an
d
o
m
izatio
n
w
h
ic
h
m
a
y
lead
to
s
k
ip
th
e
m
o
s
t
o
p
ti
m
al
s
o
lu
t
io
n
an
d
ev
en
th
e
p
r
esen
t
s
o
l
u
tio
n
[
2
4
]
.
I
n
o
r
d
e
r
to
o
v
er
co
m
e
th
is
p
r
o
b
lem
,
w
h
al
e
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.
22
,
No
.
3
,
J
u
n
e
2
0
2
1
:
1
5
7
3
-
1
5
7
9
1574
o
p
tim
izatio
n
a
l
g
o
r
ith
m
(
W
O
A
)
is
in
co
r
p
o
r
ated
w
i
th
GW
O
to
im
p
r
o
v
e
th
e
ex
p
lo
r
atio
n
ca
p
ab
ilit
y
,
w
h
ic
h
in
tu
r
n
i
m
p
r
o
v
es t
h
e
g
lo
b
al
co
n
v
er
g
en
ce
r
es
u
lts
a
n
d
en
s
u
r
es b
e
tter
q
u
alit
y
s
o
lu
tio
n
.
T
h
er
ef
o
r
e,
to
o
v
er
co
m
e
t
h
e
p
r
o
b
lem
o
f
p
o
o
r
ex
p
lo
r
atio
n
in
GW
O,
th
e
i
n
itial
p
o
p
u
lati
o
n
f
o
r
th
e
GW
O
is
in
itial
ized
u
s
i
n
g
t
h
e
s
ea
r
ch
i
n
g
p
r
e
y
m
ec
h
a
n
is
m
o
f
W
OA
.
T
h
is
en
s
u
r
es
a
w
ell
-
b
a
lan
ce
d
r
elati
o
n
s
h
ip
b
et
w
ee
n
e
x
p
lo
r
atio
n
an
d
e
x
p
l
o
itatio
n
p
h
ase
i
n
t
h
e
p
r
o
p
o
s
ed
g
r
e
y
w
o
l
f
w
h
ale
o
p
tim
izatio
n
a
l
g
o
r
ith
m
(
GW
W
O)
alg
o
r
ith
m
.
I
n
t
h
e
i
n
itial
s
ta
g
e
o
f
t
h
e
s
ta
n
d
ar
d
GW
O
it
h
as
t
h
e
lo
w
co
n
v
er
g
e
n
ce
r
ate
in
th
e
s
ea
r
ch
s
p
ac
e
[
2
3
]
.
T
h
e
p
o
s
itio
n
u
p
d
ate
eq
u
atio
n
p
er
f
o
r
m
s
w
el
l
in
e
x
p
lo
itatio
n
a
n
d
is
n
o
t
ab
le
to
p
er
f
o
r
m
w
e
ll
d
u
r
in
g
ex
p
lo
r
atio
n
[
2
1
]
.
I
n
th
e
g
r
ey
w
o
l
f
p
o
s
itio
n
ch
an
g
e
s
ta
g
e,
th
e
s
o
lu
ti
o
n
s
et
g
e
n
er
ates
th
e
p
o
s
itio
n
b
ased
o
n
th
e
p
r
ev
io
u
s
b
est
p
o
s
itio
n
o
f
alp
h
a
(
α
)
,
b
eta
(
β),
an
d
d
elta
(
δ)
w
o
lv
es
[
2
5
]
.
A
t
th
is
s
ta
g
e,
t
h
e
r
atio
o
f
ex
p
lo
itatio
n
an
d
ex
p
lo
r
atio
n
r
an
d
o
m
l
y
ch
o
s
e
in
d
i
v
id
u
al
s
f
r
o
m
th
e
p
o
p
u
la
tio
n
b
y
a
co
m
p
o
n
e
n
t
ca
lled
r
an
d
o
m
izatio
n
f
ac
to
r
th
at
p
la
y
s
t
h
e
m
o
s
t
i
m
p
o
r
tan
t
r
o
le
in
ch
a
n
g
i
n
g
th
e
Gr
e
y
w
o
l
f
p
o
s
itio
n
it
is
u
s
ed
in
tu
r
n
to
co
n
tr
o
l
th
e
s
o
l
u
tio
n
s
ea
r
ch
o
f
t
h
e
o
p
ti
m
iza
tio
n
p
r
o
b
le
m
to
b
e
s
o
l
v
ed
.
T
h
e
m
aj
o
r
r
o
le
o
f
t
h
is
f
ac
to
r
is
to
ch
ec
k
th
e
a
v
ailab
ili
t
y
o
f
th
e
m
o
s
t
o
p
ti
m
al
s
o
lu
t
io
n
in
v
icin
i
ties
o
f
t
h
e
cu
r
r
en
t
s
o
l
u
tio
n
(
ex
p
lo
itatio
n
)
.
A
l
s
o
,
it
h
as
to
ex
p
lo
r
e
th
e
s
o
lu
tio
n
s
ea
r
ch
s
p
ac
e
to
f
in
d
n
e
w
s
o
l
u
tio
n
(
ex
p
lo
r
atio
n
)
is
s
e
ar
ch
o
f
th
e
m
o
s
t o
p
ti
m
al
s
o
lu
ti
o
n
[
2
1
]
.
2.
G
R
E
Y
WO
L
F
O
P
T
I
M
I
Z
AT
I
O
N
(
G
WO
)
GW
O
is
a
s
w
ar
m
in
te
lli
g
en
t
a
lg
o
r
ith
m
t
h
at
f
o
llo
w
s
t
h
e
b
asi
c
p
r
in
cip
les
o
f
lead
er
s
h
ip
h
ier
ar
ch
y
a
n
d
h
u
n
ti
n
g
b
eh
a
v
io
r
o
f
g
r
e
y
w
o
l
v
es
i
n
n
at
u
r
e.
Du
e
to
its
s
i
m
p
licit
y
,
GW
O
h
as
b
ee
n
w
id
el
y
u
tili
ze
d
to
s
o
l
v
e
m
an
y
p
r
ac
tical
o
p
ti
m
izatio
n
p
r
o
b
lem
s
[
1
6
]
.
GW
O
f
ac
es
m
a
n
y
c
h
alle
n
g
i
n
g
p
r
o
b
lem
s
,
it
ca
n
ea
s
il
y
g
et
tr
ap
p
ed
in
lo
ca
l
o
p
ti
m
a
b
ec
au
s
e
o
f
i
m
b
alan
ce
d
r
elatio
n
s
h
ip
b
et
w
ee
n
ex
p
lo
itatio
n
an
d
ex
p
lo
r
atio
n
.
A
l
s
o
,
th
e
p
o
s
itio
n
-
u
p
d
ate
eq
u
atio
n
in
GW
O
m
o
s
t
l
y
r
elies
o
n
t
h
e
in
f
o
r
m
atio
n
p
r
o
v
id
ed
b
y
th
e
p
r
ev
io
u
s
s
o
l
u
tio
n
s
to
g
e
n
er
ate
n
e
w
ca
n
d
id
ate
s
o
lu
tio
n
s
w
h
ic
h
r
es
u
lt
in
p
o
o
r
ex
p
lo
r
atio
n
ac
tiv
ity
[
1
8
]
.
T
h
er
ef
o
r
e,
to
o
v
er
co
m
e
th
e
p
r
o
b
lem
o
f
p
o
o
r
ex
p
lo
r
atio
n
in
t
h
e
G
W
O
th
e
e
x
p
lo
r
atio
n
p
ar
t
o
f
t
h
e
w
h
ale
o
p
ti
m
izatio
n
alg
o
r
it
h
m
(
W
OA
)
i
s
i
n
te
g
r
ated
in
it
[
2
6
]
.
T
h
e
r
esu
ltan
t
g
r
e
y
w
o
l
f
w
h
ale
o
p
ti
m
izatio
n
a
l
g
o
r
ith
m
(
GW
W
O
A
)
o
f
f
er
s
b
etter
ex
p
lo
r
atio
n
ab
ilit
y
an
d
ca
n
s
o
lv
e
t
h
e
o
p
ti
m
iza
tio
n
p
r
o
b
lem
s
to
f
i
n
d
th
e
m
o
s
t
o
p
ti
m
al
s
o
l
u
tio
n
i
n
s
ea
r
c
h
s
p
ac
e.
T
h
e
p
e
r
f
o
r
m
a
n
c
e
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
i
s
test
ed
an
d
e
v
alu
a
ted
o
n
f
iv
e
b
en
ch
m
ar
k
ed
u
n
i
m
o
d
al
a
n
d
f
iv
e
m
u
lti
m
o
d
al
f
u
n
ctio
n
s
a
g
ai
n
s
t
GW
O
an
d
W
OA
al
g
o
r
ith
m
s
.
T
h
e
d
r
a
w
b
ac
k
o
f
th
e
s
ta
n
d
ar
d
GW
O
o
p
er
atio
n
is
i
ts
p
o
o
r
ex
p
lo
r
atio
n
ca
p
ab
ilit
y
at
s
m
al
l
r
an
d
o
m
izatio
n
[
2
4
]
.
T
h
e
p
o
o
r
ex
p
lo
r
atio
n
m
a
y
lead
to
s
k
ip
th
e
m
o
s
t
o
p
ti
m
a
l
s
o
lu
tio
n
a
n
d
ev
e
n
t
h
e
p
r
ese
n
t
s
o
lu
tio
n
[
2
4
]
.
T
h
e
s
tan
d
a
r
d
GW
O
is
al
s
o
p
o
o
r
in
co
n
v
er
g
en
ce
r
ate
an
d
u
lti
m
atel
y
d
eg
r
ad
es
th
e
g
lo
b
al
s
o
lu
tio
n
q
u
al
it
y
[
2
7
]
.
I
n
o
r
d
er
to
o
v
er
co
m
e
th
i
s
p
r
o
b
lem
,
W
OA
i
s
in
co
r
p
o
r
ated
w
it
h
GW
O
t
h
at
i
m
p
r
o
v
e
t
h
e
ex
p
lo
r
atio
n
ca
p
ab
ilit
y
,
w
h
ic
h
in
t
u
r
n
i
m
p
r
o
v
es
t
h
e
g
lo
b
al
co
n
v
er
g
e
n
ce
r
es
u
lt
s
an
d
en
s
u
r
es
b
etter
q
u
alit
y
s
o
l
u
tio
n
.
Ma
n
y
r
esear
ch
er
s
s
o
lv
ed
th
is
p
r
o
b
le
m
u
s
i
n
g
d
i
f
f
er
en
t
wa
y
s
to
i
m
p
r
o
v
e
t
h
e
g
lo
b
al
s
ea
r
ch
o
f
t
h
e
GW
O.
W
OA
is
r
ic
h
i
n
t
h
e
ex
p
lo
r
atio
n
s
o
its
ex
p
lo
r
atio
n
p
ar
t
i
s
u
s
ed
i
n
GW
O
to
i
m
p
r
o
v
e
th
e
ex
p
lo
r
atio
n
.
A
s
co
m
p
ar
e
d
to
th
e
o
th
er
o
p
tim
iza
tio
n
tech
n
iq
u
e
s
,
W
OA
is
ea
s
y
to
u
n
d
er
s
tan
d
an
d
i
m
p
le
m
en
t.
T
h
e
f
lo
w
d
ia
g
r
a
m
s
h
o
w
s
i
n
F
i
g
u
r
e
1
.
Fig
u
r
e
1
.
T
h
e
GW
O
alg
o
r
ith
m
f
lo
w
d
ia
g
r
a
m
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
o
m
p
Sci
I
SS
N:
2502
-
4752
I
mp
r
o
ve
d
g
r
ey
w
o
lf a
lg
o
r
ith
m
fo
r
o
p
timiz
a
tio
n
p
r
o
b
lems
(
Ha
fiz
Ma
a
z
A
s
g
h
er
)
1575
3.
T
H
E
P
RO
P
O
SE
D
G
WWO
A
AL
G
O
RI
T
H
M
T
h
e
aim
o
f
t
h
e
s
tu
d
y
i
s
to
d
ev
elo
p
an
in
te
g
r
ated
m
o
d
el
o
f
th
e
GW
O
an
d
W
OA
to
b
alan
ce
th
e
ex
p
lo
itatio
n
a
n
d
e
x
p
lo
r
atio
n
in
t
h
e
GW
O.
T
h
i
s
b
alan
c
in
g
i
m
p
r
o
v
es
t
h
e
q
u
al
it
y
o
f
th
e
GW
O
to
s
o
lv
e
t
h
e
o
p
tim
izatio
n
p
r
o
b
le
m
.
T
h
is
s
ec
tio
n
is
d
ed
icate
d
to
d
ev
elo
p
in
g
t
h
e
m
o
d
el
to
r
eso
lv
e
t
h
e
p
r
o
b
lem
in
th
e
s
tan
d
ar
d
m
o
d
el
o
f
GW
O.
T
h
e
g
r
e
y
w
o
l
f
w
h
a
le
o
p
ti
m
izati
o
n
a
lg
o
r
ith
m
(
GW
W
O
A
)
is
d
ev
elo
p
ed
to
s
o
lv
e
d
if
f
er
e
n
t
t
y
p
es
o
f
o
p
ti
m
izati
o
n
p
r
o
b
lem
s
.
I
n
th
is
r
esear
ch
,
th
e
tar
g
et
is
to
i
m
p
r
o
v
e
th
e
ab
ilit
y
o
f
t
h
e
ex
p
lo
r
atio
n
in
Gr
e
y
w
o
l
f
o
p
tim
izatio
n
alg
o
r
ith
m
b
ec
au
s
e
t
h
e
GW
O
alg
o
r
ith
m
p
er
f
o
r
m
s
w
ell
i
n
ex
p
lo
itatio
n
b
u
t
d
o
es
n
o
t
p
er
f
o
r
m
w
el
l
i
n
ex
p
lo
r
atio
n
.
T
h
e
o
b
j
ec
tiv
es
an
d
s
co
p
e
o
f
th
e
r
esear
ch
ar
e
m
en
tio
n
ed
i
n
s
o
lv
i
n
g
th
is
p
r
o
b
lem
.
T
h
e
p
r
o
p
o
s
ed
GW
W
OA
al
g
o
r
ith
m
p
er
f
o
r
m
s
w
ell
in
b
o
t
h
(
ex
p
lo
itatio
n
a
n
d
ex
p
lo
r
atio
n
)
.
I
n
GW
W
OA
,
th
e
e
x
p
lo
r
atio
n
p
r
o
ce
s
s
o
f
W
O
A
is
i
n
teg
r
a
ted
i
n
th
e
GW
O
to
i
m
p
r
o
v
e
ex
p
lo
r
atio
n
p
er
f
o
r
m
a
n
ce
i
n
it
b
ec
au
s
e
W
OA
p
er
f
o
r
m
w
el
l
in
ex
p
lo
r
atio
n
.
T
h
e
f
lo
w
d
i
ag
r
a
m
o
f
t
h
e
p
r
o
p
o
s
ed
alg
o
r
i
th
m
i
s
s
h
o
w
s
as
i
n
Fig
u
r
e
2
.
I
n
t
h
is
p
r
o
p
o
s
ed
alg
o
r
it
h
m
,
th
e
W
OA
is
an
in
teg
r
al
p
ar
t
o
f
s
tan
d
ar
d
GW
O,
an
d
u
s
ed
to
i
m
p
r
o
v
e
t
h
e
ex
p
lo
r
atio
n
p
ar
t
(
eq
u
atio
n
)
o
f
GW
O.
T
h
e
im
p
r
o
v
e
m
e
n
t
i
n
ex
p
lo
r
atio
n
p
ar
t
o
f
GW
O
is
d
u
e
to
its
p
o
o
r
p
er
f
o
r
m
a
n
ce
an
d
i
m
b
ala
n
ce
d
r
elatio
n
s
h
ip
w
it
h
ex
p
lo
itatio
n
p
ar
t
o
f
GW
O.
Fu
r
th
er
,
s
tan
d
ar
d
GW
O
co
n
s
is
ti
n
g
o
f
m
ai
n
l
y
th
r
ee
p
ar
a
m
eter
s
wh
ich
ar
e
“
a”
,
“A
”
an
d
“
c”
.
I
n
th
i
s
r
esear
ch
w
o
r
k
,
“
a”
w
a
s
ch
o
s
e
n
as
d
ef
a
u
lt
v
alu
e
o
f
0
.
5
as
d
escr
ib
ed
in
W
OA
an
d
v
ar
ied
to
an
al
y
ze
th
e
ex
p
lo
r
atio
n
p
ar
t
o
f
GW
O.
T
h
u
s
,
th
e
p
r
o
p
o
s
ed
GW
W
OA
al
g
o
r
ith
m
h
av
e
c
h
a
n
g
e
th
e
p
ar
a
m
eter
v
al
u
e
f
r
o
m
1
to
0
.
5
f
o
r
“
a”
as
s
h
o
w
n
in
v
ec
to
r
“
a”
i
n
p
s
eu
d
o
co
d
e.
On
u
tili
zi
n
g
t
h
ese
v
ar
iab
le
v
alu
e
s
f
o
r
p
ar
am
e
ter
,
th
e
p
r
o
p
o
s
ed
G
W
W
OA
alg
o
r
ith
m
w
as
u
s
ed
to
i
m
p
r
o
v
e
ex
p
lo
r
atio
n
p
ar
t
o
f
GW
O.
T
h
e
s
i
m
u
latio
n
r
es
u
lt
s
r
ev
ea
led
th
at
th
e
p
r
o
p
o
s
ed
GW
W
OA
alg
o
r
it
h
m
d
eliv
er
ed
b
alan
ce
d
r
elatio
n
s
h
ip
b
et
w
ee
n
ex
p
lo
itat
io
n
a
n
d
ex
p
lo
r
atio
n
p
ar
ts
.
Mo
r
eo
v
er
,
th
e
p
ar
a
m
eter
,
“A”
u
s
ed
to
ex
p
r
ess
t
h
e
d
ec
is
io
n
f
o
r
ex
p
lo
r
atio
n
an
d
o
r
ex
p
lo
itat
io
n
p
ar
ts
o
f
th
e
GW
O.
A
n
d
t
h
r
o
u
g
h
t
h
e
p
r
o
p
o
s
ed
GW
W
OA
al
g
o
r
ith
m
,
if
th
e
v
a
lu
e
“A
”
o
b
tain
ed
g
r
ea
ter
t
h
an
1
,
it
w
ill
b
e
ex
p
lo
r
atio
n
v
al
u
e.
On
th
e
o
t
h
er
h
a
n
d
,
it
w
ill
b
e
ex
p
lo
itatio
n
v
alu
e,
w
h
e
n
“A
”
is
less
th
a
n
1
.
T
h
u
s
,
th
e
p
ar
a
m
eter
“A
”
v
a
lu
e
w
a
s
v
ar
ied
i
n
t
h
i
s
p
r
o
p
o
s
ed
GW
W
OA
,
as th
e
ex
p
lo
r
atio
n
p
ar
t o
f
W
OA
p
er
f
o
r
m
ed
b
etter
as c
o
m
p
ar
ed
to
G
W
O
.
Fig
u
r
e
2
.
T
h
e
p
r
o
p
o
s
ed
G
W
W
OA
al
g
o
r
ith
m
f
lo
w
d
iag
r
a
m
4.
P
E
RF
O
RM
ANCE O
F
T
H
E
G
W
WO
A
AL
G
O
RI
T
H
M
T
h
e
d
if
f
er
en
t
t
y
p
es
o
f
u
n
i
m
o
d
al
a
n
d
m
u
lti
m
o
d
al
b
en
c
h
m
ar
k
f
u
n
ctio
n
s
ar
e
ap
p
lied
,
an
d
th
e
p
er
f
o
r
m
a
n
ce
i
s
ev
al
u
ated
an
d
co
m
p
ar
ed
to
th
e
s
ta
n
d
ar
d
GW
O
an
d
W
OA
.
T
h
e
p
er
f
o
r
m
a
n
ce
ev
a
lu
at
io
n
p
ar
am
eter
o
f
t
h
e
p
r
o
p
o
s
ed
m
o
d
els
f
o
r
o
p
ti
m
izat
io
n
f
u
n
ctio
n
w
er
e
b
est
-
ca
s
e
s
o
l
u
tio
n
s
,
an
d
s
tan
d
ar
d
d
ev
iatio
n
o
f
s
o
l
u
tio
n
s
.
A
ll
m
o
d
els
a
n
d
n
e
w
l
y
d
ev
elo
p
m
o
d
els
ar
e
r
u
n
3
0
ti
m
e
s
f
o
r
all
th
e
m
i
n
i
m
izatio
n
an
d
m
ax
i
m
izatio
n
f
u
n
ct
io
n
s
a
n
d
t
h
e
o
p
ti
m
al
v
al
u
es
o
b
tain
ed
i
n
ea
ch
r
u
n
ar
e
r
ec
o
r
d
ed
.
T
h
e
b
es
t
-
ca
s
e
s
o
l
u
tio
n
f
o
r
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.
22
,
No
.
3
,
J
u
n
e
2
0
2
1
:
1
5
7
3
-
1
5
7
9
1576
m
i
n
i
m
izatio
n
f
u
n
ctio
n
is
t
h
e
m
i
n
i
m
u
m
v
al
u
e
a
m
o
n
g
th
e
s
e
3
0
r
ec
o
r
d
ed
v
alu
es.
T
h
e
s
tan
d
ar
d
d
ev
iatio
n
s
h
o
ws
th
e
v
ar
iatio
n
s
in
t
h
ese
s
o
lu
tio
n
s
.
T
o
co
n
d
u
ct
a
f
air
co
m
p
ar
is
o
n
,
b
o
th
co
m
p
ar
ati
v
e
an
d
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
ar
e
i
m
p
le
m
en
ted
u
s
i
n
g
M
A
T
L
A
B
2
0
1
7
a
s
o
f
t
w
ar
e
w
h
ic
h
a
r
e
ex
ec
u
ted
s
a
m
e
h
ar
d
w
ar
e
a
n
d
s
o
f
t
w
ar
e
v
er
s
io
n
r
u
n
n
i
n
g
o
n
co
r
e
i7
C
P
U.
Un
i
m
o
d
al
an
d
m
u
lti
m
o
d
al
f
u
n
ctio
n
s
ar
e
g
iv
e
n
in
t
h
e
T
ab
l
e
1
an
d
T
ab
le
2
,
r
esp
ec
tiv
el
y
.
T
ab
le
1
.
Un
i
m
o
d
al
f
u
n
ctio
n
F
u
n
c
t
i
o
n
(
s)
D
i
me
n
si
o
n
R
a
n
g
e
1
(
)
=
∑
2
=
1
30
[
−
5
.
12
,
5
.
12
]
0
2
(
)
=
−
e
x
p
(
−
0
.
5
∑
2
=
1
)
30
[
−
1
,
1
]
0
3
(
)
=
∑
|
|
+
1
=
1
30
[
−
10
,
10
]
0
4
(
)
=
0
.
5
+
2
(
2
+
2
)
−
0
.
5
[
1
+
0
.
0
0
1
(
2
+
2
)
]
2
30
[
−
100
,
100
]
0
5
(
)
=
∑
[
100
(
+
1
−
2
)
2
+
(
−
1
)
2
]
−
1
=
1
30
[
−
30
,
30
]
0
T
ab
le
2
.
Mu
lti
m
o
d
al
f
u
n
c
tio
n
F
u
n
c
t
i
o
n
(
s)
D
i
me
n
si
o
n
R
a
n
g
e
6
(
)
=
418
.
9829
−
∑
=
1
(
√
|
|
)
30
[
−
100
,
100
]
0
7
(
)
=
10
+
∑
[
2
−
10
c
o
s
(
2
)
]
=
1
30
[
−
5
.
12
,
5
.
12
]
0
8
(
)
=
−
20
(
√
1
∑
2
=
1
−
0
.
2
)
−
e
x
p
(
1
∑
cos
(
2
)
=
1
)
+
20
+
30
[
−
32
,
32
]
0
9
(
)
=
1
4000
∑
2
−
∏
(
√
)
=
1
=
1
+
1
30
[
−
600
,
600
]
0
10
(
)
=
∑
(
e
−
0
.
2
√
2
+
+
1
2
+
3
(
c
o
c
(
2x
i
)
+
si
n
(
2
+
1
)
)
=
1
30
[
−
35
,
35
]
0
5.
RE
SU
L
T
S
A
ND
D
IS
CU
SS
I
O
N
B
ased
o
n
th
e
p
er
f
o
r
m
an
ce
e
v
alu
a
tio
n
p
ar
a
m
eter
s
,
th
e
d
e
v
elo
p
ed
m
o
d
el’
s
s
i
m
u
latio
n
r
esu
lt
s
ar
e
co
m
p
ar
ed
w
it
h
t
h
e
s
ta
n
d
ar
d
GW
O
an
d
W
OA
.
T
h
e
r
es
u
lts
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
ar
e
b
etter
th
a
n
s
ta
n
d
ar
d
GW
O
b
ec
au
s
e
GW
O
is
g
o
o
d
in
attac
k
in
g
th
e
p
r
e
y
an
d
w
ea
k
in
s
ea
r
ch
in
g
th
e
n
e
w
p
r
e
y
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
s
o
lv
es t
h
i
s
is
s
u
e
b
y
b
ala
n
cin
g
th
e
e
x
p
lo
itatio
n
an
d
ex
p
lo
r
atio
n
.
5
.1
.
Uni
m
o
da
l
B
ench
m
a
r
k
F
un
ct
io
ns
First,
th
e
f
i
v
e
u
n
i
m
o
d
al
b
en
ch
m
ar
k
f
u
n
ctio
n
al
d
ataset
is
ap
p
lied
to
th
e
p
r
o
p
o
s
ed
G
W
W
OA
to
an
al
y
s
is
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
n
e
w
l
y
d
ev
e
lo
p
ed
alg
o
r
ith
m
an
d
co
m
p
ar
ed
th
e
p
r
o
p
o
s
ed
m
o
d
el’
s
r
es
u
lts
w
it
h
th
e
s
ta
n
d
ar
d
GW
O
an
d
W
OA
alg
o
r
ith
m
s
.
Fiv
e
u
n
i
m
o
d
al
b
en
ch
m
ar
k
f
u
n
ctio
n
s
ar
e
ap
p
lied
to
th
e
p
r
o
p
o
s
ed
m
o
d
el.
T
ab
le
3
.
Sh
o
w
s
th
e
r
esu
lt
s
an
d
its
co
m
p
ar
is
o
n
.
A
ll
t
h
e
f
u
n
ctio
n
s
in
T
ab
le
3
.
Hav
e
s
in
g
le
lo
ca
l
o
p
tim
u
m
i
n
th
e
tr
aj
ec
to
r
y
o
f
th
eir
s
ea
r
c
h
s
p
ac
e.
T
h
e
m
a
x
i
m
u
m
iter
atio
n
s
f
o
r
all
t
h
e
al
g
o
r
ith
m
s
w
er
e
s
et
to
5
0
0
w
it
h
a
to
tal
o
f
3
0
tr
ials
o
n
ea
ch
f
u
n
ctio
n
.
T
ab
le
3
.
S
h
o
w
s
t
h
e
b
est
-
ca
s
e
v
al
u
e,
an
d
s
tan
d
ar
d
d
ev
iatio
n
v
alu
e.
Fro
m
T
ab
le
3
.
T
h
e
p
r
o
p
o
s
ed
GW
W
OA
alg
o
r
ith
m
s
h
o
w
s
b
etter
r
esu
lt
s
as
co
m
p
ar
ed
to
th
e
o
th
er
alg
o
r
ith
m
u
s
ed
f
o
r
co
m
p
ar
is
o
n
.
T
h
e
GW
W
OA
al
g
o
r
ith
m
p
er
f
o
r
m
ed
b
etter
o
n
all
f
i
v
e
f
u
n
ctio
n
s
(
i.e
.
ƒ
1
,
ƒ
2
,
ƒ
3
,
ƒ
4
,
an
d
ƒ
5
)
.
T
h
e
co
m
p
ar
is
o
n
o
f
th
e
av
er
a
g
e
co
n
v
er
g
e
n
ce
v
a
lu
e
s
o
b
tain
ed
b
y
th
e
p
r
o
p
o
s
ed
GW
W
OA
,
GW
O,
I
n
ter
m
s
o
f
co
n
v
er
g
e
n
c
e
s
tab
ilit
y
,
t
h
e
p
r
o
p
o
s
e
d
G
W
W
OA
al
g
o
r
ith
m
w
as
t
h
e
m
o
s
t
s
tab
le
d
u
r
in
g
all
t
h
e
3
0
r
u
n
tr
ials
.
I
ts
s
tab
le
p
er
f
o
r
m
an
ce
ca
n
b
e
s
ee
n
t
h
r
o
u
g
h
t
h
e
s
tan
d
ar
d
d
ev
iatio
n
v
alu
e
s
g
i
v
en
i
n
t
h
e
T
ab
le
3.
Ov
er
all,
th
e
p
r
o
p
o
s
ed
G
W
W
OA
is
f
o
u
n
d
to
b
e
a
h
ig
h
l
y
s
tab
le
alg
o
r
ith
m
d
u
r
i
n
g
all
th
e
tr
ial
r
u
n
s
w
h
e
n
co
m
p
ar
ed
w
ith
t
h
e
s
ta
n
d
ar
d
GW
O,
an
d
W
OA
an
d
it
g
iv
e
s
b
etter
s
o
lu
tio
n
s
o
n
th
e
u
n
i
m
o
d
al
b
en
c
h
m
ar
k
f
u
n
ctio
n
s
i
n
th
e
s
ea
r
ch
s
p
ac
e.
5
.
2
.
M
ultim
o
da
l
B
ench
m
a
r
k
F
u
nct
io
ns
T
h
e
s
ec
o
n
d
s
tep
is
th
e
ap
p
lic
atio
n
o
f
m
u
lti
m
o
d
al
b
en
ch
m
a
r
k
f
u
n
ctio
n
s
.
T
h
e
r
esu
lt
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
is
co
m
p
ar
ed
to
t
h
e
s
t
an
d
ar
d
GW
O
an
d
W
O
A
al
g
o
r
ith
m
s
.
Fi
v
e
d
i
f
f
er
e
n
t
m
u
lti
m
o
d
al
b
e
n
ch
m
ar
k
f
u
n
ctio
n
s
ar
e
u
s
ed
to
ev
al
u
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
d
ev
e
lo
p
ed
m
o
d
el.
T
h
e
T
ab
le
4
s
h
o
w
s
th
e
r
es
u
lt
s
a
n
d
th
e
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
o
m
p
Sci
I
SS
N:
2502
-
4752
I
mp
r
o
ve
d
g
r
ey
w
o
lf a
lg
o
r
ith
m
fo
r
o
p
timiz
a
tio
n
p
r
o
b
lems
(
Ha
fiz
Ma
a
z
A
s
g
h
er
)
1577
co
m
p
ar
is
o
n
.
Du
r
i
n
g
all
t
h
e
it
er
atio
n
s
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
g
o
t
th
e
b
est
v
al
u
es
i
n
th
e
s
e
ar
ch
s
p
ac
e
T
a
b
le
4
s
h
o
w
s
t
h
e
b
est
-
ca
s
e
s
o
lu
tio
n
,
an
d
s
tan
d
ar
d
d
ev
iatio
n
.
T
ab
le
4
s
h
o
w
s
t
h
e
co
m
p
ar
is
o
n
o
f
p
r
o
p
o
s
ed
alg
o
r
ith
m
GW
W
OA
w
it
h
t
h
e
s
ta
n
d
ar
d
GW
O
an
d
W
OA
.
It
s
h
o
w
s
th
e
b
es
t
r
es
u
lts
,
a
n
d
th
e
s
ta
n
d
ar
d
d
ev
i
atio
n
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
GW
W
OA
p
er
f
o
r
m
ed
w
ell
d
u
r
in
g
co
n
v
er
g
e
n
ce
to
f
i
n
d
o
u
t
t
h
e
m
o
s
t
o
p
ti
m
al
s
o
l
u
tio
n
in
s
ea
r
c
h
s
p
ac
e
ag
ai
n
s
t
t
h
e
s
t
an
d
ar
d
Gr
ey
W
o
lf
Op
ti
m
izatio
n
an
d
Sta
n
d
ar
d
W
h
ale
Op
ti
m
izatio
n
al
g
o
r
ith
m
s
.
T
h
e
p
r
o
p
o
s
ed
G
W
W
OA
alg
o
r
ith
m
co
n
v
er
g
ed
to
av
er
ag
e
g
lo
b
al
o
p
ti
m
al
v
al
u
es
o
f
3
.
0
1
E
-
0
2
,
1
.
0
2
E
-
0
9
,
6
.
1
9
E
-
1
6
,
1
.
4
5
E
-
0
1
,
an
d
3
.
0
1
E
-
0
1
f
o
r
f
u
n
ct
io
n
s
ƒ
6
,
ƒ
7
,
ƒ
8
,
ƒ
9
,
an
d
ƒ
1
0
,
r
esp
ec
tiv
el
y
.
T
h
e
r
esu
lts
s
h
o
w
t
h
e
s
tab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
G
W
W
OA
alg
o
r
ith
m
t
h
r
o
u
g
h
its
s
tan
d
ar
d
d
ev
iatio
n
r
es
u
lt
s
o
n
m
u
lti
m
o
d
al
b
en
c
h
m
ar
k
f
u
n
ct
i
o
n
s
ƒ
6
to
ƒ
1
0
.
T
ab
le
3
.
P
er
f
o
r
m
a
n
ce
ev
al
u
ati
o
n
f
o
r
u
n
i
m
o
d
al
F
u
n
c
t
i
o
n
(
ƒ
)
A
l
g
o
r
i
t
h
m
B
e
st
C
a
se
V
a
l
u
e
S
t
a
n
d
a
r
d
D
e
v
i
a
t
i
o
n
V
a
l
u
e
ƒ1
G
W
O
1
.
0
5
E
-
17
6
.
2
4
E
-
09
W
O
A
2
.
2
6
E
-
27
2
.
0
9
E
-
24
G
W
W
O
A
3
.
6
8
E
-
28
9
.
4
1
E
-
24
ƒ2
G
W
O
5
.
2
0
E
-
19
3
.
2
0
E
-
18
W
O
A
1
.
3
3
E
-
27
3
.
7
1E
-
22
G
W
W
O
A
5
.
9
0
E
-
33
2
.
0
3
E
-
24
ƒ3
G
W
O
4
.
0
8
E
-
04
2
.
8
3
E
-
02
W
O
A
5
.
3
6
E+
0
1
3
.
8
2
E+
0
2
G
W
W
O
A
3
.
8
1
E
-
06
2
.
3
3
E
-
03
ƒ4
G
W
O
1
.
9
1
E
-
04
1
.
3
2
E
-
03
W
O
A
1
.
7
4
E
-
04
1
.
4
0
E
-
03
G
W
W
O
A
1
.
4
1
E
-
04
7
.
7
1
E
-
04
ƒ5
G
W
O
2
.
6
1
E
-
02
7
.
8
8
E
-
02
W
O
A
4
.
8
1
E
-
02
2
.
4
0
E
-
02
G
W
W
O
A
4
.
9
E
-
02
2
.
4
6
E
-
02
*
*
T
h
e
h
i
g
h
l
i
g
h
t
e
d
c
e
l
l
s c
o
n
t
a
i
n
t
h
e
p
r
o
p
o
se
d
a
l
g
o
r
i
t
h
m’
s re
su
l
t
s
T
ab
le
4
.
P
er
f
o
r
m
a
n
ce
e
v
al
u
ati
o
n
f
o
r
m
u
lti
m
o
d
al
F
u
n
c
t
i
o
n
(
ƒ
)
A
l
g
o
r
i
t
h
m
B
e
st
C
a
se
S
o
l
u
t
i
o
n
S
t
a
n
d
a
r
d
D
e
v
i
a
t
i
o
n
ƒ6
G
W
O
4
.
2
4
E
-
02
1
.
4
4
E
-
01
W
O
A
4
.
2
3
E
-
02
1
.
0
5
E
-
02
G
W
W
O
A
4
.
3
5
E
-
02
6
.
7
1
E
-
0
3
ƒ7
G
W
O
9
.
9
6
E
-
01
4
.
7
0
E
-
01
W
O
A
8
.
0
0
E
-
09
6
.
1
2
E
-
09
G
W
W
O
A
7
.
8
0
E
-
09
3
.
4
9
E
-
09
ƒ8
G
W
O
4
.
2
8
E
-
16
2
.
4
8
E
-
14
W
O
A
6
.
0
9
E
-
15
6
.
4
0
E
-
16
G
W
W
O
A
5
.
1
9
E
-
16
9
.
8
0
E
-
17
ƒ9
G
W
O
9
.
3
8
E
-
01
2
.
9
7
E
-
01
W
O
A
9
.
7
8
E
-
01
2
.
8
8
E
-
01
G
W
W
O
A
5
.
8
4
E
-
01
3
.
2
3
E
-
01
ƒ
1
0
G
W
O
3
.
4
8
E
-
02
1
.
3
6
E
-
01
W
O
A
4
.
6
9
E
-
03
1
.
7
3
E
-
01
G
W
W
O
A
4
.
8
9
E
-
03
1
.
7
2
E
-
01
*
*
T
h
e
h
i
g
h
l
i
g
h
t
e
d
c
e
l
l
s c
o
n
t
a
i
n
t
h
e
p
r
o
p
o
se
d
a
l
g
o
r
i
t
h
m’
s re
su
l
t
s
6.
CO
NCLU
SI
O
N
GW
O
is
an
in
n
o
v
a
tiv
e
o
p
ti
m
i
za
tio
n
alg
o
r
ith
m
an
d
is
in
s
p
ir
ed
f
r
o
m
h
u
n
ti
n
g
an
d
h
ier
ar
ch
y
b
eh
a
v
io
r
o
f
g
r
e
y
w
o
l
v
es
’
g
r
o
u
p
.
GW
O
is
m
ai
n
l
y
co
m
p
o
s
ed
o
f
th
r
ee
m
ai
n
p
o
s
itio
n
s
i
n
cl
u
d
in
g
alp
h
a,
b
eta
an
d
o
m
e
g
a,
w
h
ic
h
f
o
llo
w
ed
ea
c
h
o
t
h
er
to
s
o
lv
e
o
p
ti
m
izatio
n
p
r
o
b
lem
s
.
I
n
th
is
r
esear
c
h
w
o
r
k
,
th
e
GW
W
OA
p
er
f
o
r
m
an
ce
w
a
s
co
m
p
ar
ed
w
i
th
s
ta
n
d
ar
d
GW
O
an
d
W
OA
th
r
o
u
g
h
to
tal
ten
b
en
ch
m
ar
k
f
u
n
ctio
n
s
.
T
h
e
ce
n
tr
al
id
ea
o
f
th
is
r
esear
ch
w
o
r
k
w
a
s
to
s
o
l
v
e
t
h
e
p
r
o
b
le
m
s
r
elate
d
to
ex
p
lo
r
atio
n
i
n
GW
O
t
h
r
o
u
g
h
t
h
e
h
e
lp
f
r
o
m
e
x
p
lo
r
atio
n
p
ar
t
o
f
th
e
W
OA
.
T
h
e
GW
W
OA
r
es
u
lt
s
r
ev
ea
led
b
etter
ac
cu
r
ac
y
a
n
d
w
er
e
i
n
g
o
o
d
ag
r
ee
m
en
t
w
it
h
s
ta
n
d
ar
d
GW
O
an
d
W
OA
.
Fu
r
t
h
er
,
t
h
e
G
W
W
O
A
alg
o
r
it
h
m
s
o
l
v
ed
o
p
tim
izat
io
n
p
r
o
b
le
m
an
d
f
o
u
n
d
o
p
ti
m
ized
s
o
lu
tio
n
in
s
ea
r
c
h
s
p
ac
e.
A
d
d
itio
n
all
y
,
t
h
e
GW
W
OA
al
g
o
r
ith
m
s
h
o
w
ed
b
etter
co
n
v
er
g
en
ce
s
o
l
u
tio
n
as
co
m
p
ar
ed
to
s
tan
d
ar
d
GW
O
a
n
d
W
O
A
a
n
d
h
ad
w
e
ll
b
alan
c
e
in
e
x
p
lo
itatio
n
an
d
e
x
p
lo
r
ati
o
n
.
Mo
r
eo
v
er
,
th
e
GW
W
OA
al
g
o
r
ith
m
i
m
p
r
o
v
e
d
th
e
s
o
lu
t
io
n
ti
m
e
as
co
m
p
ar
ed
to
th
e
co
n
s
u
m
ed
ti
m
e
o
f
GW
O
an
d
W
O
A
alg
o
r
ith
m
s
.
Fi
n
all
y
,
as b
ased
o
n
t
h
e
o
b
tain
ed
r
es
u
lt
s
,
th
e
GW
W
OA
i
s
s
u
itab
le
al
g
o
r
it
h
m
to
s
o
lv
e
o
p
ti
m
izatio
n
is
s
u
es
.
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.
22
,
No
.
3
,
J
u
n
e
2
0
2
1
:
1
5
7
3
-
1
5
7
9
1578
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
is
r
esear
ch
w
as
s
u
p
p
o
r
ted
b
y
m
i
n
is
tr
y
o
f
h
ig
h
er
e
d
u
ca
tio
n
(
MO
HE
)
th
r
o
u
g
h
f
u
n
d
a
m
en
ta
l
r
esear
ch
g
r
an
t
s
ch
e
m
e
(
FR
GS/1
/2
0
2
0
/I
C
T
0
2
/U
T
HM
/0
2
/1
)
Vo
t K
2
7
0
.
RE
F
E
R
E
NC
E
S
[1
]
B.
M
a
h
d
a
d
a
n
d
K.
S
ra
iri
,
“
Blac
k
o
u
t
risk
p
re
v
e
n
ti
o
n
in
a
sm
a
rt
g
rid
b
a
se
d
f
lex
ib
le
o
p
ti
m
a
l
stra
teg
y
u
sin
g
G
re
y
W
o
lf
-
pa
tt
e
rn
se
a
rc
h
a
lg
o
rit
h
m
s,”
En
e
rg
y
Co
n
v
e
rs
.
M
a
n
a
g
.
,
v
o
l.
9
8
,
p
p
.
4
1
1
–
4
2
9
,
2
0
1
5
,
d
o
i:
1
0
.
1
0
1
6
/j
.
e
n
c
o
n
m
a
n
.
2
0
1
5
.
0
4
.
0
0
5
.
[2
]
J.
A
.
Ko
u
p
a
e
i,
S
.
M
.
M
.
Ho
ss
e
in
i
,
a
n
d
F
.
M
.
M
.
G
h
a
in
i,
“
A
n
e
w
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
b
a
se
d
o
n
c
h
a
o
ti
c
m
a
p
s
a
n
d
g
o
ld
e
n
se
c
ti
o
n
se
a
rc
h
m
e
th
o
d
,
”
E
n
g
.
Ap
p
l.
Arti
f.
In
tell
.
,
v
o
l.
5
0
,
p
p
.
2
0
1
–
2
1
4
,
2
0
1
6
,
d
o
i:
1
0
.
1
0
1
6
/j
.
e
n
g
a
p
p
a
i.
2
0
1
6
.
0
1
.
0
3
4
.
[3
]
F
.
W
a
h
id
e
t
a
l.
,
“
A
n
En
h
a
n
c
e
d
F
ir
e
f
l
y
A
l
g
o
rit
h
m
Us
in
g
P
a
tt
e
rn
S
e
a
rc
h
f
o
r
S
o
lv
in
g
Op
ti
m
iza
ti
o
n
P
ro
b
lem
s,”
IEE
E
Acc
e
ss
,
v
o
l.
8
,
p
p
.
1
4
8
2
6
4
–
1
4
8
2
8
8
,
2
0
2
0
,
d
o
i:
1
0
.
1
1
0
9
/A
CCES
S
.
2
0
2
0
.
3
0
1
5
2
0
6
.
[4
]
P
.
Oliv
e
ll
a
-
Ro
se
ll
e
t
a
l.
,
“
Op
ti
m
iz
a
ti
o
n
p
r
o
b
lem
f
o
r
m
e
e
ti
n
g
d
istri
b
u
ti
o
n
sy
ste
m
o
p
e
ra
to
r
re
q
u
e
sts
i
n
l
o
c
a
l
f
le
x
ib
il
it
y
m
a
r
k
e
ts
w
it
h
d
istri
b
u
ted
e
n
e
rg
y
r
e
so
u
rc
e
s,”
Ap
p
l.
En
e
rg
y
,
v
o
l.
2
1
0
,
p
p
.
8
8
1
–
8
9
5
,
A
u
g
.
2
0
1
8
,
d
o
i:
1
0
.
1
0
1
6
/j
.
a
p
e
n
e
rg
y
.
2
0
1
7
.
0
8
.
1
3
6
.
[5
]
T
.
Ke
z
o
n
g
,
Z.
L
i,
L
.
L
u
o
,
a
n
d
B.
L
iu
,
“
M
u
lt
i
-
S
trate
g
y
A
d
a
p
ti
v
e
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
f
o
r
Nu
m
e
rica
l
Op
ti
m
iza
ti
o
n
,
”
En
g
in
e
e
rin
g
Ap
p
li
c
a
ti
o
n
s
o
f
Arti
fi
c
ia
l
I
n
telli
g
e
n
c
e
,
v
o
l.
37
,
p
p
.
9
–
1
9
.
2
0
1
5
,
d
o
i:
1
0
.
1
0
1
6
/j
.
e
n
g
a
p
p
a
i.
2
0
1
4
.
0
8
.
0
0
2
.
[6
]
R.
Je
n
si,
a
n
d
G
.
W
.
Jiji
,
"
A
n
e
n
h
a
n
c
e
d
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iz
a
ti
o
n
w
it
h
lev
y
f
li
g
h
t
f
o
r
g
lo
b
a
l
o
p
ti
m
iza
ti
o
n
,
"
Ap
p
li
e
d
S
o
ft
Co
mp
u
t
i
n
g
,
v
o
l
.
4
3
,
p
p
2
4
8
-
2
6
1
,
2
0
1
6
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
a
so
c
.
2
0
1
6
.
0
2
.
0
1
8
.
[7
]
Z.
Z
h
a
n
,
J.
Z
h
a
n
g
,
Y.
L
i
a
n
d
Y.
S
h
i,
"
Orth
o
g
o
n
a
l
L
e
a
rn
in
g
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iz
a
ti
o
n
,
"
in
IEE
E
T
ra
n
sa
c
ti
o
n
s o
n
Evo
lu
ti
o
n
a
ry
Co
mp
u
t
a
ti
o
n
,
v
o
l.
1
5
,
n
o
.
6
,
p
p
.
8
3
2
-
8
4
7
,
De
c
.
2
0
1
1
,
d
o
i:
1
0
.
1
1
0
9
/T
EV
C.
2
0
1
0
.
2
0
5
2
0
5
4
.
[8
]
F
.
Zh
o
n
g
,
L
.
Hu
i,
a
n
d
S
.
Z
h
o
n
g
,
“
A
n
i
m
p
ro
v
e
d
a
rti
f
i
c
ial
b
e
e
c
o
lo
n
y
a
l
g
o
rit
h
m
w
it
h
m
o
d
if
ied
-
n
e
ig
h
b
o
rh
o
o
d
-
b
a
se
d
u
p
d
a
te
o
p
e
ra
to
r
a
n
d
in
d
e
p
e
n
d
e
n
t
-
in
h
e
rit
i
n
g
-
se
a
rc
h
stra
teg
y
f
o
r
g
lo
b
a
l
o
p
ti
m
iza
ti
o
n
,
”
En
g
i
n
e
e
rin
g
Ap
p
li
c
a
ti
o
n
s
o
f
Arti
fi
c
ia
l
I
n
telli
g
e
n
c
e
,
v
o
l.
58
,
p
p
1
3
4
–
5
6
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/j
.
e
n
g
a
p
p
a
i.
2
0
1
6
.
1
1
.
0
0
5
.
[9
]
A
.
Yu
rtk
u
ra
n
,
a
n
d
E.
Em
e
l,
“
An
A
d
a
p
ti
v
e
A
rti
f
icia
l
Be
e
Co
lo
n
y
A
lg
o
rit
h
m
f
o
r
G
lo
b
a
l
Op
ti
m
iz
a
ti
o
n
,
”
Ap
p
li
e
d
M
a
th
e
ma
ti
c
s a
n
d
Co
mp
u
ta
ti
o
n
,
v
o
l.
2
7
1
,
p
p
.
1
0
0
4
–
10
23
,
2
0
1
5
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
a
m
c
.
2
0
1
5
.
0
9
.
0
6
4
.
[1
0
]
H.
Ra
k
h
sh
a
n
i,
a
n
d
A
.
Ra
h
a
ti
,
“
S
n
a
p
-
Drif
t
Cu
c
k
o
o
S
e
a
rc
h
:
A
No
v
e
l
Cu
c
k
o
o
S
e
a
rc
h
Op
ti
m
iza
t
io
n
A
lg
o
rit
h
m
,
”
Ap
p
li
e
d
S
o
ft
Co
mp
u
ti
n
g
J
o
u
rn
a
l
,
v
o
l.
52
,
p
p
.
7
7
1
–
9
4
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/
j.
a
so
c
.
2
0
1
6
.
0
9
.
0
4
8
.
[1
1
]
U.
M
lak
a
r,
I.
F
ister
Jr,
a
n
d
I.
F
ister
,
“
H
y
b
rid
se
lf
-
a
d
a
p
ti
v
e
c
u
c
k
o
o
se
a
rc
h
f
o
r
g
lo
b
a
l
o
p
ti
m
iza
ti
o
n
”
S
w
a
r
m
a
n
d
Ev
o
lu
ti
o
n
a
ry
Co
m
p
u
tatio
n
.
v
o
l
2
9
,
p
p
4
7
-
7
2
,
2
0
1
6
.
[1
2
]
S
.
M
irj
a
li
li
,
S
.
M
o
h
a
m
m
a
d
,
a
n
d
A
.
L
e
w
is,
“
G
re
y
W
o
lf
Op
ti
m
ize
r,
”
Ad
v
.
E
n
g
.
S
o
f
tw
.
,
v
o
l.
6
9
,
p
p
.
4
6
–
6
1
,
2
0
1
4
,
d
o
i:
1
0
.
1
0
1
6
/j
.
a
d
v
e
n
g
so
f
t.
2
0
1
3
.
1
2
.
0
0
7
.
[1
3
]
A
.
H.
G
a
n
d
o
m
i,
X
.
S
.
Ya
n
g
,
S
T
a
lata
h
a
ri,
a
n
d
A
H
A
l
a
v
i
,
“
F
iref
l
y
a
lg
o
rit
h
m
w
it
h
c
h
a
o
s
,
”
Co
mm
u
n
ica
ti
o
n
s
in
No
n
li
n
e
a
r
S
c
ien
c
e
a
n
d
Nu
me
ric
a
l
S
imu
l
a
ti
o
n
,
v
o
l.
18
,
n
o
.
1
,
p
p
.
89
–
9
8
,
2
0
1
2
,
d
o
i:
1
0
.
1
0
1
6
/
j.
c
n
sn
s.
2
0
1
2
.
0
6
.
0
0
9
.
[1
4
]
Z.
Ch
e
n
,
,
S
.
Zh
o
u
,
a
n
d
J.
L
u
o
,
“
A
ro
b
u
st
a
n
t
c
o
lo
n
y
o
p
ti
m
i
z
a
ti
o
n
f
o
r
c
o
n
ti
n
u
o
u
s
f
u
n
c
ti
o
n
s,”
Exp
e
rt
S
y
ste
ms
wit
h
Ap
p
li
c
a
ti
o
n
s
,
v
o
l
.
8
1
,
p
p
.
3
0
9
-
3
2
0
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/
j.
e
sw
a
.
2
0
1
7
.
0
3
.
0
3
6
.
[1
5
]
A
.
Ch
a
k
ri,
R.
Kh
e
li
f
,
M.
Be
n
o
u
a
re
t,
&
X
.
S
.
Ya
n
g
,
“
Ne
w
d
irec
ti
o
n
a
l
b
a
t
a
lg
o
ri
th
m
f
o
r
c
o
n
ti
n
u
o
u
s
o
p
ti
m
iza
ti
o
n
p
ro
b
lem
s,”
Exp
e
rt S
y
ste
ms
wit
h
Ap
p
l
ic
a
ti
o
n
s
,
v
o
l
.
6
9
,
p
p
.
1
5
9
-
1
7
5
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/
j.
e
sw
a
.
2
0
1
6
.
1
0
.
0
5
0
.
[1
6
]
İ.
G
ö
lcü
k
a
n
d
F
.
B.
Oz
so
y
d
a
n
,
“
Ev
o
lu
ti
o
n
a
ry
a
n
d
a
d
a
p
ti
v
e
in
h
e
rit
a
n
c
e
e
n
h
a
n
c
e
d
G
re
y
W
o
lf
Op
ti
m
iz
a
ti
o
n
a
lg
o
rit
h
m
f
o
r
b
in
a
ry
d
o
m
a
in
s,”
Kn
o
wled
g
e
-
Ba
se
d
S
y
ste
ms
,
V
o
l
.
1
9
4
,
2
0
2
0
,
d
o
i:
1
0
.
1
0
1
6
/
j.
k
n
o
sy
s.2
0
2
0
.
1
0
5
5
8
6
.
[1
7
]
N.
Ja
y
a
k
u
m
a
r,
S
S
u
b
ra
m
a
n
ian
,
S
Ga
n
e
sa
n
,
a
n
d
E
B
El
a
n
c
h
e
z
h
ian
,
“
G
r
e
y
W
o
lf
Op
ti
m
iza
ti
o
n
f
o
r
C
o
m
b
in
e
d
He
a
t
a
n
d
P
o
w
e
r
Dis
p
a
tch
w
it
h
Co
g
e
n
e
ra
ti
o
n
S
y
ste
m
s,
”
In
t
.
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
P
o
we
r
a
n
d
En
e
rg
y
S
y
ste
ms
,
v
o
l.
74
,
pp.
2
5
2
–
6
4
,
2
0
1
6
,
d
o
i:
1
0
.
1
0
1
6
/
j.
i
jep
e
s.2
0
1
5
.
0
7
.
0
3
1
.
[1
8
]
M
.
F
a
h
a
d
,
F
.
A
a
d
il
,
Z.
Re
h
m
a
n
,
S
.
Kh
a
n
,
P
A
S
h
a
h
,
M
.
Kh
a
n
,
J.
L
lo
r
e
t,
H.
Wan
g
,
J
W
L
e
e
,
I
M
e
h
m
o
o
d
,
“
G
re
y
W
o
lf
Op
ti
m
iza
ti
o
n
Ba
se
d
Clu
ste
rin
g
A
l
g
o
rit
h
m
f
o
r
V
e
h
icu
lar
A
d
-
Ho
c
Ne
t
w
o
rk
s
,
”
Co
mp
u
ter
s
a
n
d
El
e
c
trica
l
En
g
i
n
e
e
rin
g
,
v
o
l.
7
0
,
p
p
.
8
5
3
–
7
0
,
2
0
1
8
,
d
o
i:
0
.
1
0
1
6
/j
.
c
o
m
p
e
lec
e
n
g
.
2
0
1
8
.
0
1
.
0
0
2
.
[1
9
]
X
.
S
o
n
g
,
L
T
a
n
g
,
S
Zh
a
o
,
X
Z
h
a
n
g
,
L
L
i,
J
Hu
a
n
g
,
a
n
d
W
Ca
i
,
“
G
re
y
W
o
lf
Op
ti
m
ize
r
f
o
r
P
a
ra
m
e
ter
Esti
m
a
ti
o
n
in
S
u
rf
a
c
e
W
a
v
e
s,
”
S
o
il
Dy
n
a
mic
s
a
n
d
Ea
rt
h
q
u
a
k
e
En
g
in
e
e
rin
g
,
v
o
l.
7
5
,
p
p
.
1
4
7
–
5
7
,
2
0
1
5
,
d
o
i:
1
0
.
1
0
1
6
/j
.
s
o
il
d
y
n
.
2
0
1
5
.
0
4
.
0
0
4
.
[2
0
]
A
.
A
.
He
id
a
ri
a
n
d
P
.
P
a
h
lav
a
n
i
,
“
A
n
e
ff
icie
n
t
m
o
d
if
ied
g
re
y
wo
lf
o
p
ti
m
ize
r
w
it
h
L
é
v
y
f
li
g
h
t
fo
r
o
p
ti
m
iza
ti
o
n
tas
k
s,”
Ap
p
l.
S
o
ft
Co
mp
u
t.
J
.
,
v
o
l.
6
0
,
p
p
.
1
1
5
–
1
3
4
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/j
.
a
so
c
.
2
0
1
7
.
0
6
.
0
4
4
.
[2
1
]
W
.
L
o
n
g
,
J.
Jia
o
,
X
.
L
ian
g
,
a
n
d
M
.
T
a
n
g
,
“
A
n
e
x
p
lo
ra
ti
o
n
-
e
n
h
a
n
c
e
d
g
re
y
w
o
l
f
o
p
ti
m
ize
r
to
so
lv
e
h
ig
h
-
d
im
e
n
sio
n
a
l
n
u
m
e
rica
l
o
p
ti
m
iza
ti
o
n
,
”
En
g
.
A
p
p
l
.
Arti
f.
I
n
tell.
,
v
o
l.
6
8
,
p
p
.
6
3
–
8
0
,
A
p
r.
2018
,
d
o
i:
1
0
.
1
0
1
6
/j
.
e
n
g
a
p
p
a
i.
2
0
1
7
.
1
0
.
0
2
4
.
[2
2
]
Z.
T
e
n
g
,
J
L
v
,
a
n
d
L
G
u
o
,
“
A
n
Im
p
ro
v
e
d
H
y
b
rid
G
r
e
y
W
o
lf
Op
ti
m
iza
ti
o
n
A
l
g
o
rit
h
m
,
”
S
o
ft
Co
m
p
u
ti
n
g
,
v
o
l.
2
3
,
n
o
.
1
5
,
p
p
.
6
6
1
7
–
3
1
,
2
0
1
9
,
d
o
i:
1
0
.
1
0
0
7
/s0
0
5
0
0
-
0
1
8
-
3
3
1
0
-
y
.
[2
3
]
S
.
Dh
a
rg
u
p
ta,
M
.
G
h
o
sh
,
S
.
M
irj
a
li
li
,
a
n
d
R.
S
a
rk
a
r,
“
S
e
lec
ti
v
e
O
p
p
o
si
ti
o
n
b
a
se
d
G
re
y
W
o
l
f
Op
ti
m
iz
a
ti
o
n
,
”
Exp
e
rt
S
y
st.
Ap
p
l
.
,
v
o
l.
1
5
1
,
p
p
.
1
1
3
3
8
9
,
2
0
2
0
,
d
o
i:
1
0
.
1
0
1
6
/
j.
e
sw
a
.
2
0
2
0
.
1
1
3
3
8
9
.
[2
4
]
M
.
A
.
M
.
M
a
je
e
d
,
a
n
d
S
.
R.
P
a
tri
.
“
A
n
En
h
a
n
c
e
d
G
re
y
W
o
lf
Op
ti
m
iz
a
ti
o
n
A
lg
o
rit
h
m
w
it
h
I
m
p
ro
v
e
d
Ex
p
lo
ra
ti
o
n
A
b
il
it
y
f
o
r
A
n
a
lo
g
Circu
it
De
sig
n
A
u
to
m
a
ti
o
n
,
”
T
u
rk
ish
J
o
u
rn
a
l
o
f
El
e
c
trica
l
E
n
g
i
n
e
e
rin
g
a
n
d
C
o
m
p
u
ter
S
c
ien
c
e
s
,
v
o
l.
26
,
n
o
.
5
,
p
p
.
2
6
0
5
–
26
1
7
,
2
0
1
8
,
d
o
i:
1
0
.
3
9
0
6
/e
lk
-
1
8
0
2
-
1
1
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
o
m
p
Sci
I
SS
N:
2502
-
4752
I
mp
r
o
ve
d
g
r
ey
w
o
lf a
lg
o
r
ith
m
fo
r
o
p
timiz
a
tio
n
p
r
o
b
lems
(
Ha
fiz
Ma
a
z
A
s
g
h
er
)
1579
[2
5
]
S
.
M
irj
a
li
li
a
n
d
A
.
L
e
w
is,
“
T
h
e
W
h
a
le
Op
ti
m
iza
ti
o
n
A
lg
o
rit
h
m
,
”
Ad
v
a
n
c
e
s
in
e
n
g
in
e
e
rin
g
s
o
ft
wa
re
,
v
o
l.
9
5
,
p
p
.
5
1
–
6
7
,
2
0
1
6
,
d
o
i:
1
0
.
1
0
1
6
/j
.
a
d
v
e
n
g
so
f
t.
2
0
1
6
.
0
1
.
0
0
8
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
H
a
fiz
M
a
a
z
A
sg
h
e
r
b
e
lo
n
g
s
to
Ra
h
im
Y
a
r
Kh
a
n
,
P
u
n
jab
,
P
a
k
i
sta
n
.
He
h
o
l
d
s
a
Ba
c
h
e
lo
r
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
f
ro
m
T
h
e
Isla
m
ia
Un
iv
e
rsit
y
o
f
Ba
h
a
wa
lp
u
r
(IUB)
in
2
0
1
5
.
C
u
rre
n
tl
y
h
e
is
p
u
rsu
in
g
M
a
ste
r
’s
d
e
g
re
e
f
ro
m
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
I
n
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
Un
i
v
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
si
a
(UT
HM).
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
s
o
p
ti
m
iza
ti
o
n
,
m
a
c
h
in
e
lea
rn
in
g
a
n
d
a
rti
f
icia
l
in
telli
g
e
n
c
e
Ya
n
a
M
a
z
w
in
M
o
h
m
a
d
H
a
ss
i
m
i
s
a
s
e
n
io
r
lec
tu
re
r
a
t
th
e
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
Un
iv
e
r
s
it
i
T
u
n
Hu
ss
e
in
O
n
n
M
a
lay
sia
(U
T
HM).
S
h
e
g
ra
d
u
a
ted
w
it
h
a
P
h
D
d
e
g
re
e
f
ro
m
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
sia
(U
T
H
M
)
in
2
0
1
6
.
Earlier,
i
n
2
0
0
6
sh
e
c
o
m
p
lete
d
h
e
r
M
a
s
ter'
s
d
e
g
re
e
in
C
o
m
p
u
ter
S
c
ien
c
e
f
ro
m
Un
iv
e
rsiti
o
f
M
a
lay
a
(UM)
.
S
h
e
re
c
e
iv
e
d
h
e
r
Ba
c
h
e
lo
r
o
f
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
(Ho
n
s)
d
e
g
re
e
m
a
jo
rin
g
in
In
d
u
str
ial
Co
m
p
u
ti
n
g
f
ro
m
Un
iv
e
rsiti
Ke
b
a
n
g
sa
a
n
M
a
la
y
sia
(U
KM)
in
2
0
0
1
.
I
n
2
0
0
3
,
Ya
n
a
M
a
z
w
in
jo
in
e
d
th
e
a
c
a
d
e
m
ic
sta
ff
in
UT
HM.
He
r
re
se
a
r
c
h
a
re
a
in
c
lu
d
e
s
n
e
u
ra
l
n
e
tw
o
rk
s,
sw
a
r
m
in
telli
g
e
n
c
e
,
o
p
ti
m
iza
ti
o
n
a
n
d
c
las
sif
ic
a
ti
o
n
.
Ro
z
a
i
d
a
G
h
a
z
a
li
is
c
u
rre
n
tl
y
a
P
ro
f
e
ss
o
r
a
t
th
e
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
I
n
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
,
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
s
ia
(U
T
HM).
S
h
e
g
ra
d
u
a
ted
w
it
h
a
P
h
.
D
.
d
e
g
re
e
in
Hig
h
e
r
Ord
e
r
Ne
u
ra
l
Ne
tw
o
rk
s
f
ro
m
th
e
S
c
h
o
o
l
o
f
Co
m
p
u
ti
n
g
a
n
d
M
a
t
h
e
m
a
ti
c
a
l
S
c
ien
c
e
s
a
t
L
iv
e
rp
o
o
l
Jo
h
n
M
o
o
r
e
s
Un
iv
e
rsit
y
,
Un
it
e
d
Kin
g
d
o
m
i
n
2
0
0
7
.
Earlier,
in
2
0
0
3
sh
e
c
o
m
p
lete
d
h
e
r
M
.
S
c
.
d
e
g
re
e
in
Co
m
p
u
ter S
c
i
e
n
c
e
f
ro
m
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
a
lay
sia
(U
T
M
).
S
h
e
re
c
e
iv
e
d
h
e
r
B.
S
c
.
(H
o
n
s)
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Un
iv
e
rsiti
S
a
in
s
M
a
lay
si
a
(USM
)
i
n
1
9
9
7
.
In
2
0
0
1
,
Ro
z
a
i
d
a
jo
in
e
d
th
e
a
c
a
d
e
m
i
c
sta
ff
in
UT
HM.
He
r
re
se
a
rc
h
a
re
a
in
c
lu
d
e
s
n
e
u
ra
l
n
e
tw
o
rk
s,
sw
a
r
m
in
te
ll
ig
e
n
c
e
,
o
p
ti
m
iza
ti
o
n
,
d
a
ta
m
in
in
g
,
a
n
d
ti
m
e
se
ries
p
re
d
ictio
n
.
M
u
h
a
m
m
a
d
A
a
m
ir
h
a
s rec
e
n
tl
y
re
c
e
iv
e
d
h
is P
h
D i
n
I
n
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
f
ro
m
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
sia
.
H
e
d
id
h
is
M
a
ste
r’s
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Cit
y
Un
iv
e
rsit
y
o
f
S
c
ien
c
e
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
P
a
k
istan
.
He
h
a
d
w
o
rk
e
d
f
o
r
tw
o
y
e
a
rs
in
X
u
l
u
lab
s
L
L
C
a
s
d
a
ta
sc
ien
ti
st
.
Cu
rre
n
tl
y
h
e
is
w
o
rk
in
g
o
n
re
se
a
rc
h
re
late
d
t
o
b
ig
d
a
ta
p
ro
c
e
ss
in
g
a
n
d
d
a
ta
a
n
a
ly
sis.
H
is
f
ield
s
o
f
In
tere
st
a
re
Da
ta
S
c
ien
c
e
,
De
e
p
L
e
a
rn
in
g
,
a
n
d
Co
m
p
u
ter
P
ro
g
ra
m
m
in
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.