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Algo
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it
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In
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nfo
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ticle
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to
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y:
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h
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a
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p
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p
latf
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th
a
t
p
ro
v
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s
o
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-
d
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m
a
n
d
a
c
c
e
ss
to
a
sh
a
re
d
p
o
o
l
o
f
c
o
n
f
ig
u
ra
b
le
re
so
u
rc
e
s
su
c
h
a
s
n
e
tw
o
rk
s,
se
rv
e
rs
a
n
d
st
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ra
g
e
th
a
t
c
a
n
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e
ra
p
id
ly
p
ro
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isio
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e
d
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n
d
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d
w
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h
m
in
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l
m
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g
e
m
e
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t
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ff
o
rt
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o
m
c
li
e
n
ts.
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t
it
s
c
o
re
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u
d
c
o
m
p
u
ti
n
g
f
o
c
u
se
s
o
n
m
a
x
i
m
izin
g
th
e
e
ffe
c
ti
v
e
n
e
ss
o
f
th
e
sh
a
re
d
re
so
u
rc
e
s.
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h
e
r
e
f
o
re
,
w
o
rk
f
lo
w
sc
h
e
d
u
li
n
g
is
o
n
e
o
f
th
e
c
h
a
ll
e
n
g
e
s
th
a
t
th
e
Clo
u
d
m
u
st
ta
c
k
le
e
sp
e
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iall
y
if
a
lar
g
e
n
u
m
b
e
r
o
f
tas
k
s a
re
e
x
e
c
u
ted
o
n
g
e
o
g
ra
p
h
ica
ll
y
d
istri
b
u
ted
se
rv
e
rs.
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h
is en
tails
th
e
n
e
e
d
to
a
d
o
p
t
a
n
e
f
fe
c
ti
v
e
sc
h
e
d
u
li
n
g
a
lg
o
rit
h
m
in
o
r
d
e
r
to
m
in
i
m
iz
e
tas
k
c
o
m
p
letio
n
ti
m
e
(
m
a
k
e
sp
a
n
).
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l
th
o
u
g
h
w
o
rk
f
lo
w
sc
h
e
d
u
li
n
g
h
a
s
b
e
e
n
th
e
f
o
c
u
s
o
f
m
a
n
y
r
e
se
a
rc
h
e
rs,
a
h
a
n
d
f
u
l
e
ff
ici
e
n
t
so
lu
ti
o
n
s
h
a
v
e
b
e
e
n
p
ro
p
o
se
d
f
o
r
Clo
u
d
c
o
m
p
u
ti
n
g
.
In
t
h
is
p
a
p
e
r
,
we
p
ro
p
o
se
th
e
L
P
S
O,
a
n
o
v
e
l
a
lg
o
rit
h
m
f
o
r
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rk
f
lo
w
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h
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d
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li
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g
p
ro
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l
e
m
th
a
t
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a
se
d
o
n
t
h
e
P
a
rti
c
le
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w
a
r
m
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ti
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iza
ti
o
n
m
e
th
o
d
.
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u
r
p
ro
p
o
se
d
a
lg
o
rit
h
m
n
o
t
o
n
ly
e
n
su
re
s
a
f
a
st
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o
n
v
e
rg
e
n
c
e
b
u
t
a
lso
p
re
v
e
n
ts
g
e
tt
in
g
trap
p
e
d
in
lo
c
a
l
e
x
tre
m
a
.
We
ra
n
re
a
li
stic
sc
e
n
a
rio
s
u
sin
g
C
lo
u
d
S
im
a
n
d
f
o
u
n
d
t
h
a
t
L
P
S
O
is
su
p
e
rio
r
to
p
re
v
io
u
sly
p
ro
p
o
se
d
a
lg
o
ri
th
m
s
a
n
d
n
o
t
ice
d
t
h
a
t
t
h
e
d
e
v
iatio
n
b
e
twe
e
n
th
e
so
lu
ti
o
n
f
o
u
n
d
b
y
L
P
S
O an
d
th
e
o
p
ti
m
a
l
so
lu
ti
o
n
is n
e
g
li
g
ib
le.
K
ey
w
o
r
d
:
Min
i
m
izatio
n
m
a
k
esp
a
n
Op
ti
m
izatio
n
p
r
o
b
lem
P
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
clo
u
d
c
o
m
p
u
ti
n
g
W
o
r
k
f
lo
w
s
ch
ed
u
li
n
g
Co
p
y
rig
h
t
©
2
0
1
8
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
T
o
an
P
h
an
T
h
an
h
,
Han
o
i N
atio
n
al
U
n
i
v
er
s
it
y
o
f
E
d
u
ca
tio
n
,
Ha
No
i,
Viet
Na
m
.
E
m
ail: p
tto
an
@
h
n
u
e.
ed
u
.
v
n
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
C
lo
u
d
is
a
co
m
p
u
t
in
g
p
latf
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r
m
t
h
at
p
r
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-
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e
m
a
n
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ac
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s
to
a
s
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p
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o
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co
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f
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g
u
r
ab
le
co
m
p
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tin
g
r
eso
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s
s
u
ch
as
n
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w
o
r
k
s
,
s
er
v
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s
an
d
s
to
r
ag
e
[
1
]
.
W
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k
f
lo
w
s
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u
li
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g
is
o
n
e
o
f
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T
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p
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f
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alg
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m
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d
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to
attain
a
m
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m
al
co
m
p
let
io
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ti
m
e
(
ca
lled
m
a
k
esp
a
n
)
.
T
h
e
r
est o
f
t
h
e
p
ap
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is
o
r
g
an
i
ze
d
as f
o
llo
w
.
Sectio
n
2
r
ev
ie
w
s
s
o
m
e
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f
t
h
e
r
elate
d
w
o
r
k
s
g
er
m
a
n
e
to
w
o
r
k
f
lo
w
s
c
h
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u
l
in
g
alg
o
r
it
h
m
s
.
Sect
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n
3
d
escr
ib
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th
e
co
m
p
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tatio
n
an
d
co
m
m
u
n
icat
i
o
n
m
o
d
el
o
n
w
h
ic
h
C
lo
u
d
tas
k
s
o
p
er
ate.
B
ased
o
n
th
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s
m
o
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el,
S
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n
4
p
r
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p
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u
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g
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r
ith
m
L
P
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(
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ca
l
-
s
ea
r
ch
P
ar
ticle
S
w
ar
m
Op
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m
izatio
n
)
.
Sectio
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5
d
escr
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th
e
ex
p
er
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m
e
n
ts
w
e
co
n
d
u
cted
u
s
in
g
t
h
e
C
lo
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d
Si
m
s
i
m
u
lat
io
n
to
o
l
[
2
]
in
o
r
d
er
to
ev
alu
ate
t
h
e
p
r
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p
o
s
ed
alg
o
r
ith
m
.
Sec
tio
n
6
co
n
cl
u
d
es
o
u
r
p
ap
er
an
d
s
k
etc
h
es
f
u
tu
r
e
w
o
r
k
.
2.
RE
L
AT
E
D
WO
RK
2
.
1
.
Appro
a
ches f
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r
w
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rk
f
lo
w
s
c
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pro
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m
s
A
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s
eq
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ce
o
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co
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n
ec
ted
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s
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s
.
W
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lo
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li
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g
i
n
C
lo
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d
s
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h
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b
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s
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k
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I
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e
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c
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li
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p
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,
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is
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n
NP
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co
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p
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p
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[
3
]
.
Hen
ce
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p
ast
w
o
r
k
s
b
an
k
ed
m
o
s
tl
y
o
n
h
eu
r
i
s
tic
-
b
ased
s
o
lu
tio
n
s
f
o
r
s
ch
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u
lin
g
w
o
r
k
f
lo
w
s
.
Fo
r
ex
a
m
p
le,
S.
P
ar
s
a
[
4
]
p
r
o
p
o
s
ed
a
s
ch
ed
u
li
n
g
alg
o
r
it
h
m
t
h
at
m
i
n
i
m
izes
th
e
m
a
k
es
p
an
o
f
t
h
e
w
o
r
k
f
lo
w
in
th
e
Gr
id
en
v
ir
o
n
m
e
n
t.
A
.
A
g
ar
w
al
[
5
]
s
t
u
d
ied
th
e
g
r
ee
d
y
al
g
o
r
ith
m
w
h
ic
h
as
s
i
g
n
ed
a
n
ap
p
r
o
p
r
iate
p
r
io
r
ity
s
eq
u
e
n
ce
n
u
m
b
er
s
to
task
s
.
J
.
Hu
an
g
[
6
]
p
r
o
p
o
s
ed
a
w
o
r
k
f
lo
w
tas
k
s
c
h
ed
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li
n
g
alg
o
r
ith
m
b
ased
o
n
g
en
etic
alg
o
r
ith
m
.
S.
P
an
d
ey
[
7
]
p
r
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ted
an
ef
f
e
ctiv
e
s
c
h
ed
u
li
n
g
alg
o
r
ith
m
(
P
SO_
H)
to
m
i
n
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m
ize
th
e
co
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t
o
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th
e
ex
ec
u
tio
n
.
R
.
B
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y
y
a
[
2
]
p
r
esen
ted
a
b
r
ief
d
escr
ip
tio
n
o
f
C
lo
u
d
Si
m
,
t
h
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u
s
e
f
u
l
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m
u
latio
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it th
at
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to
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late
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th
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li
n
g
p
o
lic
y
.
J
.
J
in
tao
[
8
]
p
r
o
p
o
s
ed
a
task
s
ch
ed
u
li
n
g
alg
o
r
it
h
m
b
ased
o
n
s
er
v
ice
q
u
alit
y
an
d
t
h
e
ad
v
an
t
ag
es
o
f
t
h
e
Min
-
m
in
al
g
o
r
ith
m
.
Gu
o
-
Nin
g
an
d
T
in
g
-
L
ei
[
9
]
p
r
esen
ted
an
o
p
ti
m
ized
alg
o
r
ith
m
f
o
r
task
s
c
h
ed
u
li
n
g
b
ased
o
n
H
y
b
r
id
Gen
etic
Alg
o
r
it
h
m
s
.
T
h
e
au
t
h
o
r
s
co
v
er
ed
in
t
h
eir
s
t
u
d
y
t
h
e
Qo
S
r
eq
u
ir
e
m
en
ts
li
k
e
co
m
p
le
tio
n
ti
m
e,
b
an
d
w
id
t
h
,
co
s
t,
d
is
ta
n
c
e,
r
eliab
ilit
y
o
f
d
i
f
f
er
en
t
t
y
p
es
o
f
tas
k
s
.
L
.
G
u
o
[
1
0
]
p
r
esen
t
ed
a
m
o
d
el
f
o
r
task
s
ch
ed
u
lin
g
in
C
lo
u
d
to
m
i
n
i
m
ize
th
e
o
v
er
all
ti
m
e
o
f
ex
ec
u
t
io
n
an
d
tr
an
s
m
is
s
io
n
.
L
.
G
u
o
p
r
o
p
o
s
ed
th
e
P
SO
alg
o
r
ith
m
(
P
ar
ticle
S
w
ar
m
Op
ti
m
izat
io
n
)
w
h
ic
h
is
b
ased
o
n
th
e
s
m
all
p
o
s
itio
n
v
alu
e
r
u
le.
R
.
R
aj
k
u
m
ar
[
1
1
]
p
r
o
p
o
s
ed
a
h
ier
ar
ch
ical
s
ch
ed
u
li
n
g
a
lg
o
r
it
h
m
th
a
t
h
e
lp
s
s
ati
s
f
y
s
er
v
ice
le
v
el
a
g
r
ee
m
e
n
t
with
q
u
ick
r
e
s
p
o
n
s
e
f
r
o
m
th
e
s
er
v
ice
p
r
o
v
id
er
.
S.
J
.
Xu
e
[
1
2
]
p
r
o
p
o
s
ed
th
e
h
y
b
r
id
P
SO
alg
o
r
ith
m
to
m
i
n
i
m
ize
th
e
co
s
t
ex
ec
u
tio
n
o
f
th
e
w
o
r
k
f
lo
w
.
C
r
o
s
s
o
v
er
an
d
m
u
tatio
n
o
f
g
en
et
ic
alg
o
r
ith
m
ar
e
e
m
b
ed
d
ed
in
to
th
e
P
SO
alg
o
r
ith
m
to
i
m
p
r
o
v
e
t
h
e
g
lo
b
al
s
ea
r
c
h
.
J
.
L
i
u
I
n
et
a
l
[
1
3
]
p
r
esen
ted
th
e
co
m
p
o
n
e
n
ts
o
f
an
in
te
lli
g
en
t
j
o
b
s
ch
ed
u
li
n
g
s
y
s
te
m
i
n
clo
u
d
co
m
p
u
ti
n
g
.
2
.
2
.
T
he
pa
rt
icle
s
w
a
r
m
o
pti
m
iz
a
t
io
n
m
et
ho
d
T
h
e
P
ar
ticle
S
w
ar
m
Op
ti
m
iza
tio
n
(
P
SO)
is
o
n
e
o
f
t
h
e
late
s
t
ev
o
l
u
tio
n
ar
y
o
p
ti
m
iza
tio
n
t
ec
h
n
iq
u
e
s
in
tr
o
d
u
ce
d
i
n
1
9
9
5
b
y
Ken
n
e
d
y
an
d
E
b
er
h
ar
t
[
1
4
]
.
T
h
er
e
ar
e
m
a
n
y
s
t
u
d
ies
w
h
ic
h
s
u
cc
ee
d
P
SO
s
tr
ateg
y
s
u
c
h
as [
1
5
]
,
[
1
6
]
.
T
h
e
y
p
r
o
p
o
s
ed
t
h
e
f
o
r
m
u
la
o
f
u
p
d
atin
g
t
h
e
p
o
s
itio
n
v
ec
to
r
as f
o
llo
w
s
:
v
i
k+
1
=
v
i
k
+ c
1
r
a
n
d
1
×(
p
b
est
i
-
x
i
k
)
+
c
2
r
a
n
d
2
×(
g
b
est
-
x
i
k
)
(
1
)
x
i
k+
1
= x
i
k
+ v
i
k
(
2
)
w
h
er
e
a.
v
i
k
, v
i
k+
1
:
v
elo
cit
y
o
f
p
ar
ticle
i
at
iter
atio
n
k
an
d
k+1
b.
x
i
k
, x
i
k+
1
:
p
o
s
itio
n
o
f
th
e
p
ar
tic
le
i
at
iter
atio
n
k
an
d
k+1
c.
ω
:
in
er
tia
w
eig
h
t;
c
1
,
c
2
:
ac
ce
ler
atio
n
co
ef
f
icien
ts
d.
r
a
n
d
1
,
r
a
n
d
2
:
r
an
d
o
m
n
u
m
b
er
b
et
w
ee
n
0
an
d
1
e.
p
b
est
i
:
b
est p
o
s
itio
n
o
f
p
ar
ticle
i
;
g
b
est
:
p
o
s
itio
n
o
f
b
est p
ar
ticle
in
a
p
o
p
u
latio
n
T
h
e
g
o
al
o
f
P
SO is
to
f
in
d
t
h
e
p
o
s
itio
n
th
at
m
i
n
i
m
izes t
h
e
f
it
n
es
s
f
u
n
c
tio
n
d
en
o
ted
b
y
:
F
itn
ess
(
g
b
est)
→
Min
2
.
3
.
T
o
po
lo
g
ica
l n
eig
hb
o
rho
o
d f
o
r
t
he
P
SO
T
h
e
s
tan
d
ar
d
P
SO
h
as
n
o
n
ei
g
h
b
o
r
h
o
o
d
r
elatio
n
s
h
ip
,
all
o
f
p
ar
ticles
ar
e
d
ir
ec
tl
y
co
n
n
ec
t
ed
to
ea
ch
o
th
er
s
o
th
er
e
ar
e
n
o
n
eig
h
b
o
r
h
o
o
d
r
elatio
n
s
h
ip
s
b
et
w
ee
n
th
e
m
.
T
h
e
p
o
s
itio
n
o
f
ea
c
h
p
ar
ticle
is
u
p
d
ated
ac
co
r
d
in
g
to
its
p
er
s
o
n
al
b
est
p
o
s
itio
n
(
p
b
est
)
an
d
th
e
g
lo
b
al
b
est
p
o
s
itio
n
a
m
o
n
g
all
t
h
e
p
ar
ticles
(
g
b
est
)
.
Ho
w
e
v
er
,
v
ar
io
u
s
p
er
s
o
n
al
r
elatio
n
s
h
ip
s
,
s
u
c
h
as
p
ar
en
t
-
ch
ild
r
elatio
n
s
h
ip
s
,
in
r
ea
l
wo
r
ld
d
o
ex
is
t.
T
h
is
co
m
p
elled
s
o
m
e
r
esear
ch
er
s
[
1
7
]
to
p
r
o
p
o
s
e
to
p
o
lo
g
ical
n
eig
h
b
o
r
h
o
o
d
b
etw
ee
n
p
ar
ticles
in
P
SO’
s
.
R
esear
ch
e
s
[
17
]
h
av
e
ap
p
lied
v
ar
io
u
s
to
p
o
lo
g
ical
n
ei
g
h
b
o
r
h
o
o
d
s
s
u
c
h
as
th
e
R
i
n
g
n
ei
g
h
b
o
r
h
o
o
d
an
d
Vo
n
Neu
m
an
n
eig
h
b
o
u
r
h
o
o
d
w
h
er
e
ea
ch
p
ar
ticle
s
h
ar
es
its
lo
ca
l
b
est
p
o
s
itio
n
am
o
n
g
n
eig
h
b
o
r
i
n
g
p
ar
ticles
i
n
th
e
to
p
o
lo
g
ical
s
p
ac
e.
Fo
r
th
is
r
ea
s
o
n
ea
ch
p
ar
ticle
is
a
f
f
ec
ted
b
y
th
e
lo
ca
l
b
est
(
lb
est
)
in
it
s
l
o
ca
l
n
eig
h
b
o
r
h
o
o
d
in
s
tead
o
f
p
b
est
.
I
n
P
SOs
t
h
at
u
s
e
a
lo
ca
l b
est p
o
s
itio
n
,
t
h
e
f
o
r
m
u
la
f
o
r
u
p
d
atin
g
th
e
p
o
s
iti
o
n
v
ec
to
r
is
v
i
k+
1
=
×v
i
k
+
c
1
r
a
n
d
1
× (
p
b
e
s
t
i
-
x
i
k
)
+ c
2
r
a
n
d
2
× (
lb
es
t
i
-
x
i
k
)
(
3
)
w
h
er
e
lb
est
i
is
t
h
e
lo
ca
l b
est p
o
s
itio
n
o
f
p
ar
ticle
i
w
it
h
t
h
e
b
est f
it
n
e
s
s
v
alu
e
a
m
o
n
g
it
s
n
ei
g
h
b
o
r
s
.
As
s
h
o
w
n
in
Fi
g
u
r
e
1
,
th
e
n
ei
g
h
b
o
r
h
o
o
d
r
elatio
n
s
h
ip
s
ar
e
d
eter
m
i
n
ed
b
ased
o
n
ea
c
h
to
p
o
lo
g
y
.
Fo
r
ex
a
m
p
le,
i
n
t
h
e
R
in
g
to
p
o
lo
g
y
,
ea
ch
p
ar
ticle
h
as
k
n
ei
g
h
b
o
r
s
.
I
n
th
i
s
p
ap
er
w
e
s
et
k
=2
s
o
ea
ch
p
ar
ticle
x
i
co
n
n
ec
t
s
d
ir
ec
tl
y
to
its
lef
t
-
n
ei
g
h
b
o
r
(
L
e
f
t(
x
i
)
)
an
d
its
r
ig
h
t
-
n
eig
h
b
o
r
(
R
ig
h
t(
x
i
)
)
.
B
ased
o
n
th
e
R
i
n
g
to
p
o
lo
g
y
,
w
e
b
u
ild
a
s
ea
r
ch
i
n
g
f
u
n
ct
io
n
d
escr
ib
ed
as f
o
llo
w
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
8
5
2
–
3
8
5
9
3854
F
u
n
c
t
i
o
n
R
i
n
g
(
x
i
)
I
n
p
u
t
:
c
u
r
r
e
n
t
p
o
si
t
i
o
n
x
i
O
u
t
p
u
t
:
x
w
h
e
r
e
F
i
t
n
e
ss(
x
)
=
m
i
n
{F
i
t
n
e
ss(
x
i
)
,
F
i
t
n
e
ss(
L
e
f
t
(
x
i
)
)
,
F
i
t
n
e
ss(
R
i
g
h
t
(
x
i
))}
3.
P
RO
B
L
E
M
F
O
R
M
UL
AT
I
O
N
W
e
d
en
o
te
th
e
w
o
r
k
f
lo
w
as a
Dir
ec
ted
A
c
y
clic
Gr
ap
h
(
D
AG)
r
ep
r
esen
ted
b
y
G=
(
V,
E
)
,
w
h
er
e:
a.
V
is
a
s
et
o
f
v
er
tex
,
ea
ch
v
er
te
x
r
ep
r
esen
ts
a
ta
s
k
b.
T
={
T
1
, T
2
,…,T
M
}
is
th
e
s
et
o
f
task
s
,
M
is
t
h
e
n
u
m
b
er
o
f
tas
k
s
c.
E
r
ep
r
esen
ts
th
e
d
ata
d
ep
en
d
en
cies
b
et
w
ee
n
th
e
s
e
tas
k
s
.
T
h
e
ed
g
e
(
T
i
,
T
j
)
E
m
ea
n
s
t
h
e
tas
k
T
i
is
t
h
e
f
at
h
er
o
f
th
e
ta
s
k
T
j
,
th
e
d
ata
p
r
o
d
u
ce
d
b
y
T
i
w
ill b
e
co
n
s
u
m
e
d
b
y
t
h
e
tas
k
T
j
.
d.
Th
e
C
lo
u
d
’
s
co
m
p
u
tatio
n
r
eso
u
r
ce
,
s
et
o
f
s
er
v
er
s
S =
{
S
1
, S
2
,….,S
N
}.
N
is
th
e
n
u
m
b
er
o
f
s
er
v
er
s
.
e.
E
ac
h
tas
k
T
i
ca
n
b
e
ex
ec
u
ted
b
y
an
y
s
er
v
er
S
j
S,
an
d
S
i
h
as
t
o
h
an
d
le
w
h
o
le
t
h
e
w
o
r
k
lo
ad
o
f
T
i
f.
T
h
e
co
m
p
u
ta
tio
n
o
f
ta
s
k
T
i
d
en
o
ted
b
y
W
i
(
f
lo
p
-
f
lo
ati
n
g
p
o
i
n
t o
p
er
atio
n
s
)
g.
P(
S
i
)
: th
e
co
m
p
u
tatio
n
p
o
w
er
o
f
th
e
s
er
v
er
S
i
(
MI
/s
:
m
illi
o
n
in
s
tr
u
ctio
n
s
/s
ec
o
n
d
)
h.
T
h
e
b
an
d
w
id
th
B
(
S
i
,S
j
)
b
et
w
e
en
s
er
v
er
S
i
an
d
s
er
v
er
S
j
r
ep
r
esen
ts
b
y
t
h
e
f
u
n
ctio
n
B
(
)
:
S×S
→
R
+
.
W
e
ass
u
m
e
t
h
at
B(
S
i
,S
i
)
=
∞
an
d
B
(
S
i
,S
j
)
=
B
(
S
j
,S
i
)
i.
D
ij
: d
ata
p
r
o
d
u
ce
d
b
y
task
T
i
a
n
d
co
n
s
u
m
ed
b
y
ta
s
k
T
j
.
E
ac
h
s
ch
ed
u
li
n
g
p
la
n
ca
n
b
e
r
ep
r
esen
ted
b
y
t
h
e
f
u
n
ctio
n
f
(
)
: T
→S w
h
er
e
f
(
T
i
)
is
t
h
e
s
er
v
er
w
h
ic
h
h
a
n
d
le
s
t
h
e
task
T
i
.
Un
d
er
th
e
ab
o
v
e
ass
u
m
p
tio
n
s
,
w
e
m
a
y
co
m
p
u
te
:
a.
T
h
e
ex
ec
u
tio
n
t
i
m
e
o
f
th
e
ta
s
k
T
i
is
i
i
T
f
P
W
(
4
)
b.
T
h
e
co
m
m
u
n
ica
tio
n
ti
m
e
b
et
wee
n
th
e
ta
s
k
T
i
an
d
T
j
is
j
i
ij
T
f
T
f
B
D
,
(
5
)
Fo
r
m
all
y
,
w
e
s
ee
k
to
m
in
i
m
iz
e
th
e
e
x
ec
u
tio
n
t
i
m
e
o
f
th
e
w
o
r
k
f
lo
w
:
ma
ke
s
p
a
n
→
m
i
n
w
h
er
e
th
e
e
x
ec
u
tio
n
ti
m
e,
ca
ll
ed
ma
ke
s
p
a
n
,
is
th
e
ti
m
e
d
if
f
e
r
en
ce
b
et
w
ee
n
t
h
e
s
tar
t
an
d
f
i
n
is
h
o
f
a
s
eq
u
en
ce
o
f
w
o
r
k
f
lo
w
's
tas
k
s
.
4.
P
RO
P
O
SE
D
AL
G
O
R
I
T
H
M
4
.
1
.
E
s
ca
pi
ng
lo
ca
l e
x
t
re
m
u
m
Du
r
in
g
th
eir
e
x
ec
u
tio
n
,
P
SO
-
b
ased
alg
o
r
ith
m
s
m
a
y
g
e
t
tr
ap
p
ed
in
lo
ca
l
e
x
tr
e
m
a.
O
u
r
p
r
o
p
o
s
ed
id
ea
to
escap
e
s
u
c
h
lo
ca
l
ex
tr
e
m
a
i
s
as
f
o
llo
w
s
:
w
h
e
n
th
e
s
w
ar
m
f
alls
in
to
t
h
e
ar
ea
ar
o
u
n
d
t
h
e
lo
ca
l
ex
tr
e
m
a,
w
e
co
m
b
i
n
e
th
e
P
SOs
i
n
o
r
d
er
to
h
a
v
e
a
to
p
o
lo
g
ical
n
eig
h
b
o
r
h
o
o
d
w
it
h
a
n
ei
g
h
b
o
r
h
o
o
d
s
ea
r
ch
in
g
f
u
n
ctio
n
[
1
8
]
th
at
m
o
v
es
p
ar
ticle
s
to
a
n
e
w
ar
ea
.
Var
iab
le
Neig
h
b
o
r
h
o
o
d
Sear
ch
in
g
Fu
n
ctio
n
i
n
o
r
d
er
to
h
elp
th
e
s
w
ar
m
escap
e
f
r
o
m
th
e
ar
ea
ar
o
u
n
d
th
e
lo
ca
l
ex
tr
e
m
a,
w
e
d
ev
is
ed
t
w
o
o
p
er
ato
r
s
n
a
m
ed
E
x
c
h
a
n
g
e
an
d
R
o
tateR
ig
h
t,
a
s
i
llu
s
tr
at
ed
in
Fi
g
u
r
e
2
,
a
n
d
b
u
ilt a
Var
iab
le_
Neig
h
b
o
r
h
o
o
d
_
Sear
ch
in
g
f
u
n
ctio
n
b
ased
o
n
th
e
s
e
o
p
er
ato
r
s
.
Fig
u
r
e
1
.
N
eig
h
b
o
r
h
o
o
d
to
p
o
l
o
g
ies
(
a)
Star
to
p
o
l
o
g
y
(
b
)
R
in
g
to
p
o
lo
g
y
(
c)
Vo
n
n
eu
m
a
n
n
to
p
o
lo
g
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
E
ffective
P
S
O
-
in
s
p
ir
ed
A
lg
o
r
ith
m
fo
r
W
o
r
kflo
w
S
ch
ed
u
lin
g
(
To
a
n
P
h
a
n
Th
a
n
h
)
3855
Fig
u
r
e
2
.
Op
er
ato
r
r
o
tater
ig
h
t
(
a)
an
d
Op
er
ato
r
e
x
ch
an
g
e
(
b
)
F
u
n
c
t
i
o
n
V
a
r
i
a
b
l
e
_
N
e
i
g
h
b
o
r
h
o
o
d
_
S
e
a
r
c
h
i
n
g
(
)
I
n
p
u
t
:
p
o
si
t
i
o
n
v
e
c
t
o
r
xi
O
u
t
p
u
t
:
p
o
si
t
i
o
n
v
e
c
t
o
r
xk
:
F
i
t
n
e
ss(
xk
)
<
F
i
t
n
e
ss(
xi
)
B
e
g
i
n
1
.
t
:
=
0
;
2
.
w
h
i
l
e
(
F
i
t
n
e
ss(
xk
)
>
F
i
t
n
e
ss (
xi
)
a
n
d
(
t
<
Ma
x
_
I
t
e
r
a
t
i
o
n
)
3
.
r
:
=
r
a
n
d
o
m
[
1
,
M
];
4
.
xi
:
=
R
o
t
a
t
e
R
i
g
h
t
(
xi
,
r
);
5
.
r
a
n
d
1
:
=
[
1
,
M
]
;
r
a
n
d
2
:
=
[
1
,
M
];
6
.
xk
:
=
Ex
c
h
a
n
g
e
(
xi
,
ra
n
d
1
,
r
a
n
d
2
);
7
.
i
f
F
i
t
n
e
ss(
xk
)
<
F
i
t
n
e
ss(
xi
)
t
h
e
n
r
e
t
u
r
n
xk
e
l
se
r
e
t
u
r
n
xi
;
8
.
t
:
=
t
+
1
;
9
.
e
n
d
w
h
i
l
e
En
d
.
N
o
t
e
:
I
f
t
h
e
f
u
n
c
t
i
o
n
c
a
n
n
o
t
f
i
n
d
a
b
e
t
t
e
r
p
o
si
t
i
o
n
t
h
a
n
t
h
e
c
u
r
r
e
n
t
p
o
si
t
i
o
n
(
x
i
)
w
i
t
h
i
n
t
h
e
Ma
x
_
I
t
e
r
a
t
i
o
n
l
i
mi
t
,
x
i
i
s
r
e
t
u
r
n
e
d
.
4
.
2
.
T
he
L
P
SO
a
lg
o
rit
h
m
T
h
e
L
P
SO a
lg
o
r
ith
m
ca
n
b
e
d
escr
ib
ed
as f
o
llo
w
s
:
A
l
g
o
r
i
t
h
m
L
P
S
O
(
)
I
n
p
u
t
:
T
,
S
,
si
z
e
o
f
w
o
r
k
l
o
a
d
W
[
1
×
M
]
,
P
[
1
×
N
]
,
B
[
N
×
N
]
,
D
[
M
×
M
]
,
t
h
e
c
o
n
st
a
n
t
K
,
t
h
e
d
e
v
i
a
t
i
o
n
,
t
h
e
n
u
mb
e
r
o
f
p
a
r
t
i
c
l
e
N
o
P
O
u
t
p
u
t
:
t
h
e
b
e
st
p
o
si
t
i
o
n
g
b
e
st
B
e
g
i
n
1
.
F
o
r
i
:
=
1
t
o
N
o
P
d
o
2
.
x
i
:
=
r
a
n
d
o
m v
e
c
t
o
r
s;
v
i
:
=
r
a
n
d
o
m
v
e
c
t
o
r
s;
3
.
e
n
d
f
o
r
4
.
t
:
=
0
;
5
.
W
h
i
l
e
(
t
h
e
d
e
v
i
a
t
i
o
n
o
f
g
b
e
s
t
>
)
D
o
6
.
f
o
r
i
:
=
1
t
o
N
o
P
do
7
.
C
o
mp
u
t
e
n
e
w
p
o
si
t
i
o
n
x
i
8
.
e
n
d
f
o
r
9
.
f
o
r
i
:
=
1
t
o
N
o
P
do
1
0
.
U
p
d
a
t
e
p
b
e
st
i
;
1
1
.
e
n
d
f
o
r
1
2
.
U
p
d
a
t
e
g
b
e
st
;
13
.
f
o
r
i
:
=
1
t
o
N
o
P
do
14
.
l
b
e
st
i
:=
R
i
n
g
(
x
i
)
;
15
.
e
n
d
f
o
r
1
6
.
f
o
r
i
:
=
1
t
o
N
o
P
do
1
7
.
U
p
d
a
t
e
v
i
k
a
n
d
c
o
mp
u
t
e
x
i
;
1
9
.
e
n
d
f
o
r
2
0
.
t
++
;
2
1
.
i
f
(
t
h
e
d
e
v
i
a
t
i
o
n
o
f
g
b
e
s
t
≤
a
f
t
e
r
K
g
e
n
e
r
a
t
i
o
n
s)
t
h
e
n
g
b
e
s
t
:
=
V
a
r
i
a
b
l
e
_
N
e
i
g
h
b
o
r
h
o
o
d
_
S
e
a
r
c
h
i
n
g
(
g
b
e
st
)
;
2
3
.
E
n
d
w
h
i
l
e
;
2
4
.
R
e
t
u
r
n
g
b
e
st
;
En
d
.
In
ea
ch
iter
atio
n
,
th
e
L
P
SO
u
p
d
ates
t
h
e
p
o
s
itio
n
v
ec
to
r
s
o
f
p
ar
ticles
b
a
s
ed
o
n
g
b
est
a
n
d
lb
est
u
s
in
g
f
o
r
m
u
las
(
2
)
an
d
(
3
)
.
I
f
t
h
e
d
e
v
iatio
n
o
f
g
b
est
le
s
s
t
h
an
d
u
r
in
g
K
co
n
tin
u
o
u
s
g
e
n
er
atio
n
s
,
th
is
m
ea
n
s
t
h
at
t
h
e
s
w
ar
m
i
s
tr
ap
p
ed
in
a
lo
ca
l
e
x
tr
e
m
u
m
ar
ea
,
an
d
h
en
ce
t
h
e
f
u
n
ctio
n
V
a
r
ia
b
le_
N
eig
h
b
o
u
r
h
o
o
d
_
S
ea
r
ch
in
g
(
)
s
h
o
u
ld
b
e
ca
lled
.
T
h
is
f
u
n
ct
io
n
m
o
v
es (
m
ig
r
ate
s
)
th
e
s
w
ar
m
to
a
n
e
w
ar
ea
an
d
p
r
o
d
u
ce
s
a
n
e
w
g
e
n
er
atio
n
.
I
f
g
b
est
is
n
o
t
i
m
p
r
o
v
ed
s
ig
n
i
f
ican
tl
y
,
i.e
.
th
e
d
ev
iatio
n
o
f
g
b
est
is
s
t
ill
les
s
th
a
n
af
ter
K
c
o
n
tin
u
o
u
s
m
i
g
r
atio
n
s
u
p
o
n
ca
lli
n
g
th
e
f
u
n
ctio
n
V
a
r
ia
b
le
_
N
eig
h
b
o
u
r
h
o
o
d
_
S
e
a
r
ch
in
g
(
)
,
L
P
SO h
al
ts
.
I
n
o
u
r
ex
p
er
i
m
e
n
t
s
,
w
e
s
et
K
=1
0
0
an
d
=
0
.
2
1
.
I
n
t
h
e
b
e
s
t
ca
s
e,
L
P
SO
ca
n
f
i
n
d
th
e
ab
s
o
lu
te
p
o
s
itio
n
u
p
o
n
ca
llin
g
t
h
e
f
u
n
ctio
n
V
a
r
ia
b
le
N
eig
h
b
o
u
r
h
o
o
d
S
ea
r
ch
in
g
(
)
K
ti
m
es,
lead
i
n
g
to
s
p
a
w
n
i
n
g
K
2
g
e
n
er
atio
n
s
.
(
b
)
(
a)
3
1
2
3
1
3
1
2
3
1
3
3
2
1
1
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
8
5
2
–
3
8
5
9
3856
I
n
th
e
w
o
r
s
t
ca
s
e,
L
P
SO
al
w
a
y
s
f
i
n
d
s
a
b
etter
p
o
s
itio
n
af
ter
t
h
e
f
u
n
ctio
n
V
a
r
ia
b
le_
N
eig
h
b
o
u
r
h
o
o
d
_
S
ea
r
ch
in
g
(
)
is
ex
ec
u
ted
w
i
th
o
u
t
g
ettin
g
tr
ap
p
ed
in
a
lo
ca
l
ex
t
r
u
m
u
m
,
r
en
d
er
in
g
L
P
SO
an
ex
h
au
s
ti
v
e
s
ea
r
ch
.
Ou
r
d
ef
au
lt
t
h
r
es
h
o
ld
f
o
r
n
u
m
b
er
o
f
g
e
n
er
atio
n
s
is
3
0
0
.
T
h
e
L
P
SO
s
to
p
s
u
p
o
n
r
ea
ch
in
g
t
h
is
t
h
r
es
h
o
ld
.
5.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
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1
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k
[
t
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t
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as
s
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g
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m
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n
t
o
f
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er
v
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s
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th
e
P
SO_
H
alg
o
r
ith
m
[
7
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,
a
n
d
h
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n
ce
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t
ca
n
n
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t
m
ai
n
tai
n
t
h
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d
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f
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s
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m
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n
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est
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(
x
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k
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k
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x
i
k+
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t
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if
│P
(
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j
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-
(
x
i
k
[
t
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+
v
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k
[
t
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r
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k
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t
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M
I
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w
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s
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w
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th
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s
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g
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er
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er
t
h
at
h
as t
h
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s
e
s
t c
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m
p
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n
p
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w
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r
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l v
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p
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r
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t
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h
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m
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t
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d
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th
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b
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t
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an
t
h
o
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e
o
f
P
SO
_
H
[
7
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an
d
R
a
n
d
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m
[
1
9
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in
m
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s
t
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f
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s
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s
.
T
h
e
s
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lu
tio
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d
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n
g
f
r
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1
%
t
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1
2
%.
T
h
e
L
P
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s
s
tan
d
ar
d
d
ev
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s
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e
s
m
all
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t
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'
s
w
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v
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d
if
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ce
v
ar
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in
g
f
r
o
m
5
3
%
to
8
4
%.
T
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[1
]
L
a
o
Zh
ih
o
n
g
,
L
a
risa
Iv
a
s
c
u
,
“
Clo
u
d
Co
m
p
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ti
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g
Res
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u
rc
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Dy
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a
mic
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la
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Co
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NIKA
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n
,
Co
mp
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t
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g
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6
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[2
]
R.
Bu
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a
,
R.
C
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“
M
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li
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n
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HPCS
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IS
BN:
9
7
8
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2
4
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A
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1
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0
0
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.
[3
]
J.
D.
Ullma
n
,
“
NP
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1
0
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no.
3
,
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3
8
4
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393
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[4
]
S
.
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a
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M
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“
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ASA
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]
A
.
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[6
]
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n
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9
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[7
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S
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P
a
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[8
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Jia
o
Jin
tao
,
e
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N:
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3
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9
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0
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2
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[9
]
G
.
G
u
o
-
Nin
g
a
n
d
H.
T
in
g
-
L
e
i,
“
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C
o
mp
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ti
n
g
a
n
d
In
te
g
ra
te
d
S
y
ste
ms
,
pp.
60
-
6
3
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2
0
1
0
.
[1
0
]
L
.
G
u
o
,
e
t
a
l
.,
“
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a
sk
S
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h
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d
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li
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m
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S
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9
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2
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[1
1
]
R.
Ra
jk
u
m
a
r
a
n
d
T
.
M
a
la,
“
A
c
h
iev
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g
S
e
rv
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re
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t
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b
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n
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g
”
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Pro
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in
g
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In
ter
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r V
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1
3
2
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p
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5
4
7
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5
5
4
,
IS
BN 9
7
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4
2
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2
7
4
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2
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0
1
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[1
2
]
S
.
J.
Xu
e
a
n
d
W
.
W
u
,
“
S
c
h
e
d
u
l
in
g
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o
rk
f
lo
w
in
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u
d
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o
m
p
u
ti
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g
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se
d
o
n
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y
b
rid
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a
rti
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le
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w
a
r
m
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l
g
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m
”
,
In
d
o
n
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sia
n
J
o
u
rn
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l
o
f
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rin
g
,
v
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l.
1
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p
p
.
1
5
6
0
-
1
5
6
6
,
2
0
1
2
.
[1
3
]
J.
L
iu
,
e
t
a
l
.,
“
A
n
In
telli
g
e
n
t
Jo
b
S
c
h
e
d
u
l
in
g
S
y
ste
m
f
o
r
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e
b
S
e
rv
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in
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u
d
Co
m
p
u
ti
n
g
”
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
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trica
l
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g
in
e
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rin
g
,
v
o
l
.
1
1
,
p
p
.
2
9
5
6
-
2
9
6
1
,
2
0
1
3
.
[1
4
]
J.
Ke
n
n
e
d
y
a
n
d
R.
C.
Eb
e
rh
a
rt,
“
P
a
rti
c
le
sw
a
r
m
o
p
ti
m
i
z
a
ti
o
n
”
,
P
ro
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d
in
g
o
f
IEE
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In
ter
n
a
ti
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a
l
Co
n
fer
e
n
c
e
on
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u
ra
l
Ne
two
rk
s
,
p
p
.
1
9
4
2
-
1
9
4
8
,
1
9
9
5
.
[1
5
]
K.
L
e
n
in
,
“
E
m
b
e
ll
ish
e
d
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
A
l
g
o
rit
h
m
fo
r
S
o
lv
in
g
Re
a
c
ti
v
e
P
o
w
e
r
P
ro
b
l
e
m
”
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
I
n
fo
rm
a
t
ics
(
IJ
EE
I)
, v
o
l.
5
,
n
o
.
3
,
p
p
.
1
9
2
-
1
9
8
,
2
0
1
7
.
[1
6
]
S
a
lam
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le
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n
,
e
t
a
l
.,
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A
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v
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n
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d
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ti
m
a
l
P
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F
u
z
z
y
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n
d
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tr
o
ll
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r
w
it
h
P
M
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a
n
d
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S
a
t
5
Hz
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id
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f
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e
n
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ra
ti
o
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a
n
d
5
0
Hz
S
id
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f
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rid
”
,
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Po
we
r
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e
c
tro
n
ics
a
n
d
Dr
ive
S
y
ste
m
(
IJ
PE
DS
)
,
v
o
l.
7
,
n
o
.
1
,
pp.
1
7
3
-
1
9
2
,
IS
S
N:
2
0
8
8
-
8
6
9
4
,
2
0
1
6
.
[1
7
]
A.
E.
M
.
Zav
a
la,
“
EV
OL
V
E
-
A
Brid
g
e
b
e
tw
e
e
n
P
ro
b
a
b
il
it
y
,
S
e
t
Orie
n
ted
Nu
m
e
rics
,
a
n
d
Ev
o
lu
t
io
n
a
ry
Co
m
p
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tatio
n
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Co
m
p
a
riso
n
,
A
Co
m
p
a
riso
n
S
tu
d
y
o
f
P
S
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Ne
ig
h
b
o
rh
o
o
d
s”
,
S
p
rin
g
e
r
Ver
la
g
Ber
li
n
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id
e
b
e
rg
,
pp.
2
5
1
-
2
9
5
,
IS
BN
9
7
8
-
3
-
6
4
2
-
3
2
7
2
5
-
4
,
2
0
1
3
.
[1
8
]
H.
L
iu
,
e
t
a
l
.
,
“
A
No
v
e
l
V
a
riab
le
Ne
ig
h
b
o
rh
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o
d
P
a
rti
c
le
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w
a
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m
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ti
m
iza
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n
f
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r
M
u
lt
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o
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ti
v
e
F
lex
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le
Jo
b
-
S
h
o
p
S
c
h
e
d
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li
n
g
P
ro
b
lem
s”
,
P
ro
c
.
o
f
2
n
d
In
ter
n
a
ti
o
n
a
l
Co
n
fer
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n
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e
o
n
Dig
it
a
l
In
f
o
rm
a
ti
o
n
M
a
n
a
g
e
me
n
t
(
ICDI
M
'
0
7
)
,
v
o
l
.
1
,
p
p
.
1
3
8
-
1
4
5
,
2
0
0
7
.
[1
9
]
M
.
M
i
tze
n
m
a
c
h
e
r
a
n
d
E
.
Up
f
a
l,
“
P
r
o
b
a
b
il
it
y
a
n
d
C
o
m
p
u
ti
n
g
:
Ra
n
d
o
m
ize
d
A
lg
o
rit
h
m
s
a
n
d
P
ro
b
a
b
il
i
stic
A
n
a
l
y
sis
”
,
Ca
m
b
rid
g
e
Un
iv
e
rsit
y
P
re
ss
,
IS
B
N
-
13:
8
5
8
-
1
0
0
0
0
5
3
5
5
2
,
2
0
0
5
.
[2
0
]
R.
N.
Ca
lh
e
iro
s
e
t
a
l
.
,
“
Clo
u
d
S
i
m
:
A
T
o
o
lk
it
f
o
r
M
o
d
e
li
n
g
a
n
d
S
im
u
latio
n
o
f
Clo
u
d
Co
m
p
u
ti
n
g
E
n
v
iro
n
m
e
n
ts
a
n
d
E
v
a
lu
a
ti
o
n
o
f
Re
so
u
rc
e
P
r
o
v
isio
n
in
g
A
lg
o
rit
h
m
s
”
,
S
o
ft
wa
re
-
Pra
c
ti
c
e
a
n
d
Exp
e
rie
n
c
e
,
v
o
l
.
4
1
,
n
o
.
1
,
p
p
.
2
3
-
5
0
,
Jo
h
n
W
il
e
y
&
S
o
n
s,
I
n
c
.
USA
,
2
0
1
1
.
[2
1
]
J.
V
.
Vliet
a
n
d
F
.
P
a
g
a
n
e
ll
i,
“
P
r
o
g
ra
m
m
in
g
Am
a
z
o
n
EC2
”
,
O'
Re
il
ly
M
e
d
ia,
IS
BN:
1
4
4
9
3
9
3
6
8
3
,
2
0
1
1
.
[2
2
]
h
tt
p
:
//
m
o
n
tag
e
.
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tere
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m
p
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Ne
tw
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rk
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d
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m
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ra
p
h
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.
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.
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a
ff
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h
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h
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in
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2
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g
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M
.
S
c
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in
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p
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in
1
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9
9
.
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w
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d
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c
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r
2
0
0
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).
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ro
m
2
0
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u
n
t
i
l
2
0
0
4
,
Dr.
El
n
a
f
fa
r
w
o
rk
e
d
a
s
a
Re
se
a
rc
h
A
ss
o
c
iate
a
t
th
e
IBM
Ce
n
tre
o
f
A
d
v
a
n
c
e
d
S
tu
d
ies
(CAS)
in
Ca
n
a
d
a
.
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w
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rk
e
d
a
s
a
n
A
s
so
c
iate
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)
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th
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ll
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m
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ti
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T
e
c
h
n
o
l
o
g
y
,
U
A
E
Un
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e
r
sit
y
(U
A
E
).
P
re
se
n
tl
y
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h
e
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a
n
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o
c
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P
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h
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rin
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t
th
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Am
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rica
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Un
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f
R
A
K
(UA
E).
Dr.
El
n
a
ff
a
r
is
a
lso
a
n
e
n
trep
re
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e
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r
a
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lp
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m
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n
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m
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in
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str
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p
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su
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ti
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n
.
Dr
.
El
n
a
f
f
a
r’s
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se
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c
h
h
a
s
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e
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n
f
u
n
d
e
d
b
y
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u
m
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ro
u
s
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v
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rn
m
e
n
tal
o
rg
a
n
iza
ti
o
n
s,
a
n
d
in
d
u
strial
a
g
e
n
c
ies
.
His
w
o
rk
is
p
u
b
li
sh
e
d
i
n
s
e
v
e
ra
l
in
tern
a
ti
o
n
a
l
jo
u
rn
a
ls
a
n
d
IEE
E/
A
CM
Co
n
f
e
re
n
c
e
s
o
f
W
e
b
se
rv
ice
s,
G
rid
c
o
m
p
u
ti
n
g
,
S
y
ste
m
s
p
e
rf
o
r
m
a
n
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e
a
n
d
Ev
a
lu
a
ti
o
n
,
a
n
d
a
p
p
li
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d
A
I.
He
is
th
e
w
in
n
e
r
o
f
th
e
T
e
a
c
h
in
g
Aw
a
rd
in
2
0
0
8
(UA
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Un
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e
rsit
y
)
a
n
d
th
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st
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se
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p
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p
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in
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p
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d
u
c
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(
2
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)
.
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iet
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re
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d
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a
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-
P
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terb
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rg
El
e
c
tro
tec
h
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ica
l
Un
iv
e
rsity
,
Ru
ss
ia
in
2
0
0
6
.
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w
o
rk
e
d
a
t
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stit
u
te
o
f
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f
o
rm
a
ti
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n
T
e
c
h
n
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l
o
g
y
M
il
it
a
r
y
In
stit
u
te
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
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a
No
i,
V
iet
Na
m
f
ro
m
2
0
0
7
.
His
re
se
a
rc
h
in
tere
sts in
c
lu
d
e
Da
ta m
in
in
g
a
n
d
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o
f
twa
re
En
g
in
e
e
rin
g
.
H
u
u
Da
n
g
Q
u
o
c
re
c
e
iv
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d
Ba
c
h
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n
d
M
.
S
.
d
e
g
re
e
in
S
c
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f
In
f
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rm
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ti
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T
e
c
h
n
o
lo
g
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V
ietn
a
m
Na
ti
o
n
a
l
Un
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,
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a
No
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iet
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m
,
in
2
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0
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a
n
d
2
0
1
5
.
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w
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d
a
t
T
h
u
o
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g
M
a
i
Un
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e
rsit
y
,
Ha
No
i,
V
iet
Na
m
f
r
o
m
2
0
0
6
.
His
re
se
a
rc
h
in
tere
sts
i
n
c
lu
d
e
Co
m
p
u
ter
Ne
tw
o
rk
a
n
d
S
o
f
tw
a
r
e
En
g
in
e
e
rin
g
.
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