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[
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b
ase
d
o
n
m
ak
e
s
p
an
m
etr
ic
w
it
h
p
er
ce
n
tag
e
i
m
p
r
o
v
e
m
en
t.
T
h
e
r
est
o
f
th
i
s
ar
ticle
is
o
r
g
a
n
ized
as
f
o
llo
w
ed
:
R
elate
d
w
o
r
k
s
o
n
ta
s
k
s
c
h
ed
u
li
n
g
b
ased
o
n
T
ag
u
c
h
i
a
p
p
r
o
ac
h
in
Sectio
n
2
.
Sectio
n
3
d
is
cu
s
s
ed
m
o
d
elli
n
g
o
f
th
e
s
ch
ed
u
li
n
g
p
r
o
b
lem
.
C
at
s
war
m
o
p
ti
m
izatio
n
is
d
is
cu
s
s
ed
in
s
ec
tio
n
4
.
Sectio
n
5
d
is
cu
s
s
ed
t
h
e
p
r
o
p
o
s
ed
tr
ac
in
g
m
o
d
e
p
r
o
ce
s
s
o
f
C
SO
alg
o
r
ith
m
.
T
ag
u
ch
i
o
p
tim
izatio
n
w
i
th
p
r
o
p
o
s
ed
OT
B
-
C
SO
i
s
d
is
c
u
s
s
ed
i
n
Secti
o
n
6
.
Sectio
n
7
d
is
cu
s
s
ed
th
e
ex
p
er
i
m
e
n
tal
s
et
u
p
.
R
es
u
lts
o
b
tai
n
ed
ar
e
p
r
esen
ted
in
Sectio
n
8
.
Sectio
n
9
d
is
c
u
s
s
ed
th
e
r
es
u
lts
o
f
s
i
m
u
lati
o
n
an
d
S
ec
tio
n
1
0
co
n
clu
d
ed
th
e
p
ap
er
.
2.
RE
L
AT
E
D
SCH
E
DU
L
I
N
G
WO
RK
S B
ASE
D
T
A
G
UCH
I
AP
P
RO
ACH
W
e
r
ep
o
r
ted
f
e
w
e
x
i
s
ti
n
g
r
ese
ar
ch
er
s
t
h
at
e
x
p
lo
r
ed
T
ag
u
ch
i
b
ased
ap
p
r
o
ac
h
to
s
o
lv
ed
o
p
ti
m
izatio
n
p
r
o
b
lem
s
as
f
o
llo
w
ed
:
[
2
9
]
,
u
s
ed
T
ag
u
ch
i
m
et
h
o
d
in
co
n
j
u
n
ctio
n
w
i
th
s
i
m
u
lat
io
n
m
o
d
elin
g
o
f
n
e
w
ap
p
licatio
n
s
f
o
r
r
o
b
o
tic
fl
ex
ib
le
ass
e
m
b
l
y
ce
ll
s
(
R
F
AC
s
)
to
m
i
n
i
m
ized
to
tal
tar
d
in
e
s
s
an
d
n
u
m
b
er
o
f
tar
d
y
j
o
b
s
.
I
n
[
8
]
,
an
i
m
p
r
o
v
ed
d
i
f
f
er
en
tia
l
e
v
o
lu
tio
n
ar
y
alg
o
r
it
h
m
(
I
DE
A
)
u
s
i
n
g
T
ag
u
c
h
i
b
ased
ap
p
r
o
ac
h
th
at
o
p
tim
ized
tas
k
s
c
h
ed
u
l
in
g
a
n
d
r
eso
u
r
ce
allo
ca
tio
n
p
r
o
b
le
m
w
er
e
p
r
ese
n
ted
.
I
n
[
3
0
]
,
an
o
p
ti
m
u
m
s
c
h
ed
u
li
n
g
alg
o
r
ith
m
b
ased
T
ag
u
c
h
i
ap
p
r
o
ac
h
w
a
s
p
r
esen
ted
.
I
n
[
3
1
]
,
a
h
y
b
r
id
n
o
-
w
ai
t
f
lex
ib
le
f
lo
w
s
h
o
p
s
c
h
ed
u
l
in
g
alg
o
r
ith
m
t
h
at
co
m
b
i
n
ed
n
o
n
-
s
tatic
g
en
e
tic
al
g
o
r
ith
m
(
N
SG
A
-
I
I
)
w
it
h
v
ar
iab
le
n
ei
g
h
b
o
r
h
o
o
d
s
ea
r
c
h
(
VNS)
w
a
s
p
r
esen
ted
b
ased
o
n
T
ag
u
ch
i
m
et
h
o
d
an
d
m
in
i
m
ized
m
ak
esp
an
a
n
d
m
ea
n
tar
d
in
e
s
s
o
f
j
o
b
s
.
I
n
[
3
2
]
,
an
i
m
p
r
o
v
ed
e
f
f
ec
t
iv
e
g
e
n
etic
a
lg
o
r
ith
m
u
s
in
g
T
ag
u
ch
i
ap
p
r
o
ac
h
w
as
p
r
esen
ted
th
at
m
i
n
i
m
ized
to
tal
o
r
d
er
co
m
p
let
io
n
ti
m
e
(
m
ak
e
s
p
an
)
.
I
n
[
3
3
]
,
an
alg
o
r
i
t
h
m
t
h
at
s
t
u
d
ied
th
e
e
f
f
ec
t
as
s
o
ciate
d
with
s
c
h
ed
u
li
n
g
r
u
les
b
ased
o
n
t
h
e
p
er
f
o
r
m
an
ce
o
f
a
d
y
n
a
m
ic
s
c
h
ed
u
lin
g
in
f
lex
i
b
le
m
a
n
u
f
ac
t
u
r
i
n
g
s
y
s
te
m
s
was
p
r
esen
ted
u
s
i
n
g
T
ag
u
ch
i
ap
p
r
o
ac
h
.
I
n
[
3
4
]
,
a
T
ag
u
ch
i
-
b
ased
g
e
n
etic
al
g
o
r
it
h
m
(
T
B
GA
)
th
a
t
s
o
lv
ed
t
h
e
p
r
o
b
lem
o
f
j
o
b
s
h
o
p
s
ch
ed
u
lin
g
w
a
s
p
r
ese
n
ted
.
B
ased
o
n
ex
is
ti
n
g
w
o
r
k
s
,
th
is
s
tu
d
y
ex
p
lo
r
ed
th
e
m
et
h
o
d
u
s
ed
t
o
d
esig
n
ed
T
B
G
A
as
p
r
o
p
o
s
ed
in
[
3
4
]
f
o
r
th
e
d
esig
n
o
f
o
u
r
p
r
o
p
o
s
ed
O
T
B
-
C
SO
al
g
o
r
ith
m
th
at
ad
d
r
ess
e
d
in
d
ep
en
d
en
t
tas
k
s
ch
ed
u
lin
g
an
d
ac
h
iev
ed
m
in
i
m
u
m
m
a
k
esp
an
o
f
to
tal
tas
k
s
s
c
h
ed
u
led
ac
r
o
s
s
VM
s
f
o
r
a
d
y
n
a
m
ic
clo
u
d
en
v
ir
o
n
m
e
n
t
.
3.
M
AT
H
E
M
AT
I
CAL M
O
DE
L
O
F
T
H
E
SCH
E
DU
L
I
N
G
G
O
AL
T
h
e
f
o
r
m
u
latio
n
o
f
th
e
o
b
j
ec
tiv
e
m
o
d
el
f
o
r
th
e
tas
k
s
c
h
ed
u
li
n
g
p
r
o
b
le
m
is
b
ased
o
n
[
3
5
]
a
n
d
[
3
6
]
as
f
o
llo
w
:
L
et
(
)
=
{
1
,
2
,
…
.
,
}
d
en
o
t
e
th
e
s
et
o
f
clo
u
d
lets
th
at
ar
e
in
d
ep
en
d
en
t
o
f
ea
ch
o
th
er
s
ch
ed
u
led
o
n
v
ir
tu
al
m
ac
h
in
e
s
(
VM
)
(
)
=
{
1
,
2
,
…
.
.
,
}
.
Su
p
p
o
s
e
a
clo
u
d
let
(
)
is
s
c
h
ed
u
led
o
n
a
VM
(
)
,
ex
ec
u
t
io
n
ti
m
e
(
,
)
o
f
a
clo
u
d
let
ex
ec
u
ted
b
y
o
n
e
V
M
(
)
is
ca
lcu
la
ted
u
s
i
n
g
E
q
u
at
io
n
1
[
9
]
.
(
,
)
=
(
)
(
)
×
(
)
,
(
1
)
∀
∈
,
=
{
1
}
,
∈
,
=
{
1
}
W
h
er
e:
(
,
)
is
th
e
e
x
ec
u
t
io
n
ti
m
e
o
f
r
u
n
n
i
n
g
a
s
in
g
le
clo
u
d
let
o
n
o
n
e
v
ir
tu
a
l
m
ac
h
i
n
e;
(
)
is
th
e
len
g
t
h
o
f
a
clo
u
d
let
i
n
m
illi
o
n
in
s
tr
u
c
tio
n
(
MI
)
;
(
)
is
th
e
VM
p
r
o
ce
s
s
in
g
s
p
ee
d
s
i
n
m
i
llio
n
in
s
tr
u
ctio
n
s
p
er
s
ec
o
n
d
(
MI
P
S);
(
)
is
th
e
n
u
m
b
er
o
f
p
r
o
ce
s
s
i
n
g
ele
m
en
ts
.
W
h
en
s
e
v
e
r
al
VM
s
ar
e
in
v
o
l
v
ed
i
n
e
x
ec
u
tin
g
s
et
o
f
c
lo
u
d
lets
,
t
h
e
to
tal
ex
ec
u
tio
n
ti
m
e
(
,
)
o
f
a
ll
clo
u
d
lets
e
x
ec
u
ted
o
n
al
l
VM
s
is
ca
lc
u
lated
u
s
in
g
E
q
u
at
io
n
2
.
(
,
)
=
∑
(
(
)
(
)
×
(
)
)
(
2
)
∀
=
{
1
,
2
,
…
,
n
}
,
=
{
1
,
2
,
…
.
.
,
m
}
=
{
ma
x
∑
(
(
)
(
)
∗
(
)
)
}
(
3
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
S
o
lvin
g
Ta
s
k
S
c
h
ed
u
lin
g
P
r
o
b
lem
in
C
lo
u
d
C
o
mp
u
tin
g
E
n
vi
r
o
n
men
t U
s
in
g
Ort
h
o
g
o
n
a
l .
.
.
.
(
Da
n
la
mi
Ga
b
i)
1491
∀
=
{
1
,
2
,
…
,
n
}
,
=
{
1
,
2
,
…
.
.
,
m
}
4.
CAT SW
ARM
O
P
T
I
M
I
Z
A
T
I
O
N
(
CSO
)
C
SO
i
s
o
n
e
a
m
o
n
g
s
w
ar
m
o
p
t
i
m
izatio
n
tech
n
iq
u
e
ad
d
ed
to
t
h
e
f
a
m
il
y
o
f
s
w
ar
m
i
n
tell
ig
e
n
ce
(
SI)
b
y
[
1
7
]
.
T
h
e
in
ter
esti
n
g
b
eh
a
v
io
r
o
f
ca
t
en
ab
led
th
e
m
to
o
b
s
er
v
e
th
at
ca
t
h
a
s
b
o
th
r
esti
n
g
an
d
ch
asin
g
b
eh
av
io
r
.
T
h
is
b
eh
av
io
r
is
m
o
d
eled
in
to
s
ee
k
i
n
g
a
n
d
tr
ac
in
g
m
o
d
e.
A
co
n
tr
o
l
v
ar
iab
le
ca
lled
th
e
m
ix
ed
r
atio
(
MR)
is
u
s
ed
to
d
ef
in
e
t
h
e
p
o
s
itio
n
o
f
t
h
e
ca
t i.
e.
,
s
ee
k
i
n
g
o
r
tr
ac
in
g
m
o
d
e.
4
.
1
.
See
k
ing
M
o
de
T
h
e
s
ee
k
in
g
m
o
d
e,
b
ein
g
a
g
l
o
b
al
s
ea
r
ch
asp
ec
t
o
f
C
SO
d
ef
in
ed
ca
t
b
eh
av
io
r
as
p
er
r
esti
n
g
,
lo
o
k
in
g
ar
o
u
n
d
,
at
th
e
s
a
m
e
ti
m
e
d
ec
id
in
g
n
e
x
t p
o
s
itio
n
to
m
o
v
e
to
[
3
7
]
.
T
h
is
m
o
d
e
is
s
h
o
w
n
in
Al
g
o
r
ith
m
1
.
Alg
o
r
ith
m
1
:
S
e
e
k
in
g
M
o
d
e
P
ro
c
e
ss
[
9
]
1.
G
e
n
e
r
a
te
Y
(
w
h
e
re
Y
=
S
M
P
)
c
o
p
ies
o
f
k
-
th
c
a
t,
i.
e
Z
qd
w
h
e
re
(1
≤
q
≤Y
)
a
n
d
(
1
≤d
≤D
)
w
h
e
re
D
is
th
e
o
v
e
ra
ll
d
im
e
n
sio
n
.
2.
Ch
a
n
g
e
a
t
ra
n
d
o
m
th
e
d
im
e
n
sio
n
o
f
a
cat
b
y
a
p
p
l
y
in
g
m
u
tatio
n
o
p
e
ra
to
r
to
Zq
d
.
3.
De
ter
m
in
e
th
e
f
it
n
e
ss
v
a
lu
e
s o
f
a
l
l
c
h
a
n
g
e
d
c
a
ts.
4.
Disc
o
v
e
r
b
e
st ca
ts
(n
o
n
-
d
o
m
in
a
n
t
)
b
a
se
d
o
n
t
h
e
ir
f
it
n
e
ss
v
a
lu
e
s.
5.
Re
p
lac
e
th
e
p
o
sit
io
n
o
f
th
e
k
-
th
c
a
t
a
f
ter p
ick
in
g
a
c
a
n
d
id
a
te
a
t
ra
n
d
o
m
f
ro
m
Y.
4
.
2
.
T
ra
cing
M
o
de
T
h
e
tr
ac
in
g
m
o
d
e
co
r
r
esp
o
n
d
s
to
lo
ca
l sear
ch
[
9]
,
[
3
7
]
.
I
t is h
o
w
e
v
er
p
r
esen
ted
as f
o
llo
w
:
i.
C
o
m
p
u
te
an
d
u
p
d
ate
ca
t
k
-
th
v
elo
cit
y
u
s
in
g
n
e
w
v
elo
cit
y
i
n
E
q
u
atio
n
4
:
,
=
,
+
(
1
×
1
×
(
–
,
)
)
(
4
)
=
1
,
2
…
.
.
,
W
h
er
e
c
;
th
e
co
n
s
ta
n
t
v
al
u
e
o
f
ac
ce
ler
atio
n
,
r
;
is
th
e
u
n
i
f
o
r
m
l
y
d
is
tr
ib
u
tio
n
r
a
n
d
o
m
n
u
m
b
er
in
th
e
r
a
n
g
e
o
f
[
0
,
1
]
.
Fo
r
ea
ch
iter
atio
n
,
E
q
u
atio
n
5
w
ill
b
e
u
s
ed
to
u
p
d
a
te
th
e
v
elo
cit
y
.
ii.
A
d
d
n
e
w
v
elo
cit
y
b
y
co
m
p
u
t
i
n
g
t
h
e
cu
r
r
e
n
t
(
n
e
w
)
p
o
s
itio
n
o
f
th
e
k
-
th
ca
t u
s
in
g
E
q
u
atio
n
5
:
,
=
,
+
,
(
5
)
iii.
Dete
r
m
i
n
e
f
i
tn
e
s
s
v
alu
e
s
o
f
all
ca
ts
.
iv
.
Up
d
ates a
ch
iev
e
co
n
ten
ts
w
it
h
b
est ca
ts
.
5.
P
RO
P
O
SE
D
CSO
T
RACI
N
G
M
O
DE
(
L
O
CAL S
E
ARC
H
)
Ou
r
o
b
j
ec
tiv
e
is
to
m
in
i
m
ize
m
ak
e
s
p
an
o
f
to
tal
tas
k
s
s
c
h
ed
u
led
ac
r
o
s
s
VM
s
in
o
r
d
er
to
r
ed
u
ce
tas
k
ex
ec
u
t
io
n
d
ela
y
.
A
s
a
r
esu
l
t,
an
o
p
tim
u
m
tas
k
s
ch
ed
u
l
in
g
al
g
o
r
ith
m
b
ased
o
n
T
a
g
u
c
h
i
is
p
r
o
p
o
s
ed
[
8
]
,
[
1
3
]
,
[
3
2
]
.
On
th
e
o
t
h
er
h
a
n
d
,
C
SO
g
lo
b
al
s
ea
r
c
h
(
s
ee
k
i
n
g
m
o
d
e)
an
d
t
h
e
lo
ca
l
s
ea
r
c
h
(
tr
ac
in
g
m
o
d
e)
ar
e
ca
r
r
ied
o
u
t
in
d
ep
en
d
en
tl
y
an
d
th
at
r
eq
u
ir
e
a
v
er
y
h
i
g
h
co
m
p
u
tatio
n
ti
m
e
[
2
]
,
[
9
]
.
I
n
o
r
d
e
r
to
o
v
er
co
m
e
t
h
is
,
th
e
tr
ac
i
n
g
m
o
d
e
n
ee
d
s
to
b
e
m
o
d
i
f
ied
.
T
h
e
tr
ac
i
n
g
m
o
d
e
o
p
er
atio
n
o
f
ca
t
s
w
ar
m
is
r
e
-
s
tr
u
ctu
r
ed
b
y
ap
p
l
y
i
n
g
T
ag
u
ch
i
m
et
h
o
d
as f
o
llo
w
:
i.
Gen
er
ate
t
w
o
v
elo
cit
y
s
et
s
:
,
(
)
=
{
1
,
(
)
=
,
(
−
1
)
+
(
1
×
1
×
(
(
−
1
)
–
,
(
−
1
)
)
2
,
(
)
=
,
(
−
1
)
+
(
1
×
1
×
(
(
−
1
)
–
,
(
−
1
)
)
(
6
)
Su
c
h
th
at
:
,
(
)
=
{
1
,
(
)
,
ℎ
"
1"
2
,
(
)
,
ℎ
(
7
)
W
h
er
e:
,
r
ep
r
esen
ts
t
w
o
ca
n
d
id
ate’
s
v
elo
cit
y
s
ets;
is
d
i
m
en
s
io
n
o
f
t
h
e
s
o
lu
tio
n
s
p
ac
e;
r
ep
r
esen
ts
g
lo
b
al
b
est
th
e
p
o
s
iti
o
n
o
f
th
e
ca
t;
is
th
e
lo
c
al
b
est
p
o
s
itio
n
o
f
th
e
ca
t;
1
r
ep
r
esen
t
u
n
i
f
o
r
m
r
an
d
o
m
n
u
m
b
er
i
n
t
h
e
r
an
g
e
o
f
[
0
,
1
]
,
1
is
a
co
n
s
tan
t
v
al
u
e
o
f
ac
ce
ler
atio
n
;
,
r
ep
r
esen
t
p
o
s
itio
n
o
f
th
e
ca
t a
n
d
t
,
is
th
e
n
u
m
b
er
o
f
iter
atio
n
.
T
h
e
s
ize
o
f
o
r
th
o
g
o
n
al
ar
r
a
y
i
s
d
eter
m
i
n
ed
ac
co
r
d
in
g
to
s
ize
o
f
ta
s
k
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
3
,
J
u
n
e
2
0
1
7
:
1
4
8
9
–
1
4
9
7
1492
f
r
o
m
th
e
g
en
er
ate
d
t
w
o
v
e
lo
cities,
o
n
e
is
ch
o
s
en
to
u
p
d
ate
th
e
o
r
ig
in
al
v
elo
cit
y
V
k
,
d
ea
ch
ti
m
e
th
er
e
is
a
r
u
n
o
f
th
e
e
x
p
er
i
m
e
n
t a
cc
o
r
d
in
g
to
E
q
u
atio
n
8
:
,
=
{
,
[
,
+
,
(
)
]
>
,
,
+
,
,
(
)
ℎ
(
8
)
ii.
A
d
d
n
e
w
v
elo
cit
y
b
y
co
m
p
u
t
i
n
g
t
h
e
cu
r
r
e
n
t
(
n
e
w
)
p
o
s
itio
n
o
f
k
-
th
ca
t u
s
in
g
E
q
u
atio
n
9
.
,
=
,
+
,
(
9
)
iii.
Dete
r
m
i
n
e
f
i
tn
e
s
s
v
alu
e
o
f
ea
c
h
ca
t.
iv
.
Su
m
t
h
e
f
it
n
es
s
o
f
a
ll
ca
ts
ac
c
o
r
d
in
g
to
t
h
eir
g
e
n
er
ated
v
e
lo
cities,
co
m
p
ar
e
a
n
d
s
elec
t
th
e
f
i
n
al
v
elo
cit
y
to
f
o
r
m
u
late
t
h
e
latest
v
elo
cit
y
.
6.
TA
G
UCH
I
T
ASK
SCH
E
DU
L
I
N
G
O
P
T
I
M
I
Z
A
T
I
O
N
Or
th
o
g
o
n
a
l
ar
r
ay
b
ased
T
ag
u
ch
i
ap
p
r
o
ac
h
is
a
g
o
o
d
o
p
ti
m
iz
atio
n
m
et
h
o
d
.
T
h
e
d
etail
p
s
eu
d
o
co
d
e
o
f
T
ag
u
ch
i
m
et
h
o
d
f
o
r
o
u
r
m
a
tr
i
x
ex
p
er
i
m
en
t i
s
p
r
esen
ted
i
n
Alg
o
r
ith
m
2
[
3
2
]
.
Alg
o
r
ith
m
2
:
T
a
g
u
c
h
i
Op
ti
m
iza
ti
o
n
A
lg
o
rit
h
m
[
9
]
1.
S
e
lec
t
tw
o
-
lev
e
l
o
rth
o
g
o
n
a
l
a
rra
y
f
o
r
m
a
tri
x
e
x
p
e
ri
m
e
n
ts
su
c
h
t
h
a
t
L
n
(2
n
-
1
)
∀
n
≥N
+
1
a
n
d
N
re
p
re
se
n
t
tas
k
n
u
m
b
e
rs.
2.
G
e
n
e
r
a
te t
w
o
se
ts
o
f
v
e
lo
c
it
ies
V
s
e
t
1
K
,
d
(
)
a
n
d
V
s
e
t
2
K
,
d
(t)
a
c
c
o
rd
i
n
g
to
Eq
u
a
tio
n
(6
)
3.
De
ter
m
in
e
d
im
e
n
sio
n
o
f
sc
h
e
d
u
li
n
g
p
ro
b
lem
w
h
ich
c
o
rre
sp
o
n
d
s
t
o
tas
k
n
u
m
b
e
r
N.
4.
Ca
lcu
late
th
e
f
it
n
e
ss
v
a
lu
e
s o
f
n
e
x
p
e
rime
n
ts
in
a
c
c
o
rd
a
n
c
e
t
o
th
e
Orth
o
g
o
n
a
l
(
2
−
1
)
a
rra
y
.
T
h
e
ab
o
v
e
alg
o
r
ith
m
i
s
ap
p
lie
d
at
tr
ac
in
g
m
o
d
e
o
f
ca
t
s
w
ar
m
o
p
ti
m
izatio
n
(
C
SO)
f
o
r
m
i
n
i
m
izatio
n
o
f
m
a
k
esp
an
.
6
.
1
.
O
T
B
-
CSO
B
a
s
ed
T
a
s
k
Sche
du
lin
g
Alg
o
rit
h
m
W
e
d
ev
elo
p
ed
o
u
r
OT
B
-
C
S
O
b
ased
alg
o
r
ith
m
to
s
o
lv
e
th
e
p
r
o
p
o
s
ed
task
s
ch
ed
u
li
n
g
p
r
o
b
le
m
p
r
esen
ted
at
E
q
u
atio
n
3
u
s
in
g
A
l
g
o
r
ith
m
3
b
elo
w
.
Alg
o
r
ith
m
3
:
OT
B
-
CS
O
A
lg
o
rit
h
m
S
ta
r
t
1.
In
it
ialize
a
ss
o
c
iate
d
p
o
siti
o
n
,
c
a
ts
’
p
a
ra
m
e
t
e
rs;
M
R,
m
ix
e
d
ra
ti
o
;
Y,
th
e
p
o
siti
o
n
o
f
c
a
ts,
v
e
lo
c
it
y
o
f
c
a
ts
a
n
d
f
lag
o
f
e
v
e
r
y
c
a
t
to
d
is
ti
n
g
u
ish
c
a
t
in
t
o
se
e
k
in
g
a
n
d
trac
i
n
g
m
o
d
e
.
2.
De
ter
m
in
e
a
ll
re
q
u
ire attri
b
u
tes
s
u
c
h
as
v
irt
u
a
l
m
a
c
h
in
e
n
u
m
b
e
r,
th
e
n
u
m
b
e
r
o
f
p
ro
c
e
ss
in
g
e
le
m
e
n
ts,
p
ro
c
e
ss
in
g
p
o
w
e
r
to
c
a
l
c
u
late
c
a
ts’
f
it
n
e
ss
f
u
n
c
ti
o
n
.
3.
Co
m
p
u
te all
c
a
ts
a
c
c
o
rd
in
g
to
d
e
f
in
e
d
o
b
jec
ti
v
e
(F
it
n
e
ss
)
f
u
n
c
ti
o
n
s
in
Eq
u
a
tio
n
(3
)
4.
Co
m
p
a
re
f
it
n
e
ss
f
u
n
c
ti
o
n
o
f
a
ll
c
a
ts
a
n
d
k
e
e
p
p
o
siti
o
n
w
it
h
b
e
st f
it
n
e
ss
v
a
lu
e
.
5.
Do
6.
i
n
c
re
m
e
n
t_
it
e
ra
ti
o
n
_
n
u
m
b
e
r
←
t
+
1
7.
If (se
e
k
i
n
g
fla
g
←
)
8.
Ca
ll
a
lg
o
r
ith
m
1
b
y
a
p
p
l
y
in
g
se
e
k
in
g
m
o
d
e
p
ro
c
e
ss
9.
Else
10.
Ca
ll
a
l
g
o
r
ith
m
2
b
y
a
p
p
ly
in
g
trac
in
g
m
o
d
e
p
ro
c
e
ss
b
a
se
d
T
a
g
u
c
h
i
a
p
p
r
o
a
c
h
11.
En
d
if
12.
Ch
o
o
se
c
u
rre
n
t
b
e
st m
e
m
b
e
r
a
s
Xl
b
e
s
t
a
n
d
c
o
rre
sp
o
n
d
i
n
g
b
e
st p
o
siti
o
n
a
s
X
pb
e
s
t
13.
If (
Xl
b
e
s
t
>
X
gb
e
s
t
)
14.
Xl
b
e
s
t
=
X
gb
e
s
t
15.
X
pb
e
s
t
=
G
b
e
s
t
_
i
d
/
/
c
u
rre
n
t
b
e
st p
o
siti
o
n
b
e
c
o
m
e
s th
e
g
lo
b
a
l
b
e
st
p
o
siti
o
n
16.
C
o
m
p
u
te
a
n
d
u
p
d
a
te t
h
e
n
e
w
v
e
lo
c
it
y
a
n
d
p
o
siti
o
n
a
c
c
o
rd
i
n
g
to
(
Eq
u
a
tio
n
(8
)
a
n
d
(9
)
)
17.
If (ter
m
in
a
tio
n
c
o
n
d
iti
o
n
r
e
a
c
h
e
d
)
18.
Ou
tp
u
t
th
e
p
o
siti
o
n
a
s th
e
b
e
st
tas
k
sc
h
e
d
u
li
n
g
p
a
tt
e
r
n
(tas
k
se
q
u
e
n
c
e
)
th
a
t
re
tu
r
n
s t
h
e
b
e
st
f
it
n
e
ss
(
m
a
k
e
sp
a
n
).
19.
Else
20.
G
o
to
ste
p
6
.
21.
En
d
if
22.
En
d
if
En
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
S
o
lvin
g
Ta
s
k
S
c
h
ed
u
lin
g
P
r
o
b
lem
in
C
lo
u
d
C
o
mp
u
tin
g
E
n
vi
r
o
n
men
t U
s
in
g
Ort
h
o
g
o
n
a
l .
.
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[
2
3
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,
[
3
5
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.
Ob
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CO
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ase
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m
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ize
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ce
o
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th
e
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r
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o
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ith
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.
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NT
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h
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th
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h
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s
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d
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t
h
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y
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d
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T
r
u
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t
Fu
n
d
(
T
E
T
Fu
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d
)
Nig
er
ia
in
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r
r
y
i
n
g
o
u
t t
h
is
r
esear
c
h
.
RE
F
E
R
E
NC
E
S
[1
]
G
.
A
c
e
to
,
e
t
a
l.
,
“
Cl
o
u
d
m
o
n
it
o
ri
n
g
:
A
su
rv
e
y
,
”
Co
mp
u
ter
Ne
two
r
k
s
,
v
o
l
/i
ss
u
e
:
5
7
(2
0
1
3
),
p
p
.
2
0
9
3
-
2
1
1
5
,
2
0
1
3
.
[2
]
S
.
Ba
n
e
rjee
,
e
t
a
l.
,
“
De
v
e
lo
p
m
e
n
t
a
n
d
A
n
a
l
y
sis
o
f
a
Ne
w
Clo
u
d
l
e
t
A
ll
o
c
a
ti
o
n
S
trate
g
y
f
o
r
Qo
S
Im
p
ro
v
e
m
e
n
t
in
Clo
u
d
,
”
Ara
b
J
o
u
rn
a
l
o
f
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
,
v
o
l
/i
ss
u
e
:
4
0
(
5
)
,
p
p
.
1
4
0
9
-
1
4
2
5
,
2
0
1
5
.
[3
]
K.
B.
Be
y
,
e
t
a
l.
,
“
Ba
lan
c
in
g
He
u
risti
c
f
o
r
In
d
e
p
e
n
d
e
n
t
T
a
sk
S
c
h
e
d
u
li
n
g
in
Clo
u
d
C
o
m
p
u
ti
n
g
,
”
in
p
ro
c
e
e
d
in
g
s
o
f
1
2
t
h
In
ter
n
a
ti
o
n
a
l
S
y
mp
o
si
u
m o
n
Pro
g
ra
mm
i
n
g
a
n
d
S
y
ste
ms
(
IS
PS
)
,
p
p
.
1
–
6
,
2
0
1
5
.
[4
]
G
.
S
.
Do
m
a
n
a
l
a
n
d
G
.
R.
M
Re
d
d
y
,
“
Op
ti
m
a
l
L
o
a
d
Ba
lan
c
in
g
in
Clo
u
d
C
o
m
p
u
ti
n
g
b
y
Eff
icie
n
t
Util
iza
ti
o
n
o
f
V
irt
u
a
l
M
a
c
h
in
e
s,”
in
p
ro
c
e
e
d
i
n
g
s
o
f
th
e
S
ixth
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
mm
u
n
ica
t
io
n
S
y
ste
ms
a
n
d
Ne
two
rk
in
g
(
COM
S
NET
S
),
p
p
.
1
-
4
,
2
0
1
4
.
[5
]
F.
Du
ra
o
,
e
t
a
l
.
,
“
S
y
ste
m
a
ti
c
Re
v
ie
w
o
n
Clo
u
d
C
o
m
p
u
ti
n
g
,
”
J
o
u
rn
a
l
o
f
S
u
p
e
rc
o
mp
u
t
in
g
,
v
o
l.
6
8
,
p
p
.
1
3
2
1
-
1
3
4
6
,
2
0
1
4
.
[6
]
A
.
V
.
L
a
k
r
a
a
n
d
D.
K.
Ya
d
a
v
,
“
M
u
lt
i
-
Ob
jec
ti
v
e
Tas
k
S
c
h
e
d
u
li
n
g
A
lg
o
rit
h
m
f
o
r
Clo
u
d
C
o
m
p
u
ti
n
g
T
h
ro
u
g
h
p
u
t
Op
ti
m
iza
ti
o
n
,
”
Pro
c
e
d
ia
Co
m
p
u
t
e
r S
c
ien
c
e
J
o
u
rn
a
l
,
v
o
l
.
4
8
,
p
p
.
1
0
7
-
1
1
3
,
2
0
1
5
.
[7
]
V
.
A
.
L
e
e
n
a
,
e
t
a
l.
,
“
G
e
n
e
ti
c
A
l
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[8
]
Z.
Zh
o
u
a
n
d
H.
Zh
ig
a
n
g
,
“
Tas
k
S
c
h
e
d
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l
in
g
A
lg
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rit
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m
b
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se
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o
n
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re
e
d
y
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trate
g
y
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d
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m
p
u
ti
n
g
,
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h
e
Op
e
n
Cy
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s Jo
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rn
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l
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l.
8
,
p
p
.
1
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1
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4
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2
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.
[9
]
D.
G
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,
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t
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l.
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“
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T
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se
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g
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Co
m
p
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t
&
Ap
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li
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,
2
0
1
6
.
[1
0
]
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.
A
b
d
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ll
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l.
,
“
S
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6
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[1
1
]
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2
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p
.
2
3
6
–
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0
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2
0
1
4
.
[1
2
]
H.
S
.
A
l
-
Olima
t
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a
l.
,
“
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),
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p
.
9
9
1
–
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5
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0
1
5
.
[1
3
]
U.
Bh
o
i
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n
d
N.
Ra
m
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j
,
“
En
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4
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p
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3
.
[1
4
]
R.
X
u
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e
t
a
l.
,
“
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n
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i
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tch
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n
t
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ti
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iza
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,
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9
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p
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.
[1
5
]
Z.
Zh
o
u
a
n
d
H.
Zh
ig
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n
g
,
“
Tas
k
S
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Cy
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8
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p
p
.
1
1
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4
.
[1
6
]
M
.
R.
G
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re
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n
d
D.
S
.
A
Jo
h
n
so
n
,
“
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NP
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p
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,”
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rk
,
W
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,
2
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S.
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Ch
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d
P
.
W
.
T
sa
i,
“
Co
m
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tatio
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[1
8
]
A
.
I.
Aw
a
d
,
e
t
a
l.
,
“
Dy
n
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m
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M
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je
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Ba
se
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M
o
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if
ied
P
a
rti
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l
e
S
w
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r
m
Op
ti
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i
z
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ti
o
n
,
”
Ad
v
a
n
c
e
s
in
Co
mp
u
ter
S
c
ien
c
e
:
An
In
ter
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ti
o
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l
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o
u
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n
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l
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l
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ss
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(
5
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,
p
p
.
1
1
0
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1
7
,
2
0
1
5
.
[1
9
]
B,
L
.
D.
Dh
in
e
sh
a
n
d
P
.
V
.
Krish
n
a
,
“
Ho
n
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y
Be
e
Be
h
a
v
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r
In
sp
ired
L
o
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d
Ba
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Tas
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in
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m
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,”
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o
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A
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p
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9
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0
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[2
0
]
A
.
B.
El
-
S
isi,
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t
a
l.
,
“
In
telli
g
e
n
t
M
e
th
o
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f
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r
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u
d
S
c
h
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li
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se
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w
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4
)
,
p
p
.
3
9
-
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4
,
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0
1
4
.
[2
1
]
R.
K.
Je
n
a
,
“
M
u
lt
i
-
o
b
jec
ti
v
e
Tas
k
S
c
h
e
d
u
li
n
g
in
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u
d
En
v
iro
n
m
e
n
t
Us
in
g
Ne
ste
d
P
S
O
F
ra
m
e
w
o
rk
,”
Pro
c
e
d
ia
Co
mp
u
ter
S
c
ien
c
e
J
o
u
rn
a
l
,
v
o
l
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s
su
e
:
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7
(
2
0
1
5
)
,
p
p
.
1
2
1
9
-
1
2
2
7
,
2
0
1
5
.
[2
2
]
F
.
Ra
m
e
z
a
n
i,
e
t
a
l.
,
“
Ev
o
lu
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o
n
a
ry
a
lg
o
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m
-
b
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s
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lt
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jec
ti
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Tas
k
S
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h
e
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n
g
Op
ti
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iza
ti
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n
M
o
d
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l
i
n
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d
En
v
iro
n
m
e
n
ts,
”
S
p
ri
n
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r
S
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e
-
Bu
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w
Y
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rk
,
v
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l
.
1
8
,
p
p
.
1
7
3
7
-
1
7
5
7
,
2
0
1
5
.
[2
3
]
S
.
S
in
g
h
a
n
d
M
.
Ka
lra,
“
S
c
h
e
d
u
li
n
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o
f
In
d
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p
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ti
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n
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ti
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in
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ro
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telli
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rk
s (
CICN),
p
p
.
5
6
5
-
5
6
9
,
2
0
1
4
.
[2
4
]
Z.
W
u
,
e
t
a
l.
,
“
A
Re
v
ise
d
Disc
r
e
te
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iz
a
ti
o
n
f
o
r
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u
d
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o
rk
f
lo
w
S
c
h
e
d
u
li
n
g
,
”
in
p
ro
c
e
e
d
in
g
s
o
f
th
e
I
n
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
C
o
mp
u
ta
ti
o
n
a
l
I
n
telli
g
e
n
c
e
a
n
d
S
e
c
u
rity
(
CIS
)
.
1
1
-
1
4
De
c
e
m
b
e
r.
Na
n
n
i
n
g
,
Ch
in
a
,
p
p
.
1
8
4
-
1
8
8
,
2
0
1
0
.
[2
5
]
S.
C.
Ch
u
a
n
d
P
.
W
.
T
sa
i,
“
Co
m
p
u
tatio
n
a
l
in
tel
li
g
e
n
c
e
b
a
se
d
o
n
th
e
b
e
h
a
v
io
r
o
f
c
a
ts
,”
In
ter
n
a
t
io
n
a
l
J
o
u
r
n
a
l
o
f
In
n
o
v
a
ti
v
e
Co
m
p
u
ti
n
g
,
In
f
o
rm
a
ti
o
n
,
a
n
d
C
o
n
tr
o
l
,
v
o
l
/i
ss
u
e
:
3
(2
0
0
7
),
p
p
.
1
6
3
-
1
7
3
,
2
0
0
7
.
[2
6
]
P
.
M
.
P
ra
d
h
a
n
a
n
d
G
.
P
a
n
d
a
,
“
S
o
lv
in
g
M
u
lt
i
-
o
b
jec
ti
v
e
p
ro
b
lem
s
u
sin
g
c
a
t
s
w
a
r
m
o
p
ti
m
i
z
a
ti
o
n
,
”
An
in
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Exp
e
rt
S
y
ste
m wi
th
A
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c
a
ti
o
n
,
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o
l.
3
9
,
p
p
.
2
9
5
6
-
2
9
6
4
,
2
0
1
2
.
[2
7
]
G
.
Tag
u
c
h
i,
e
t
a
l.
,
“
Ro
b
u
st E
n
g
in
e
e
rin
g
,
”
Ne
w
Yo
rk
,
M
c
G
r
a
w
-
Hill
,
2
0
0
0
.
[2
8
]
U.
Bh
o
i
a
n
d
N.
Ra
m
a
n
u
j,
“
En
h
a
n
c
e
d
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9
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A
b
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,
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t
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l.
,
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latio
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ly
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ter
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l
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p
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6
6
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0
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3
.
[3
0
]
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.
R.
C
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v
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t
a
l.
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.
[3
1
]
H.
A
se
f
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t
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l.
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“
A
H
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NS
GA
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ter
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p
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.
[3
2
]
H.
C.
Ch
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,
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t
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l.
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p
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[3
3
]
R.
Ca
p
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t
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,
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ter
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[3
4
]
J.
T
Tsa
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t
a
l.
,
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n
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ter
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ti
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.
[3
5
]
S
.
Bil
g
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iy
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n
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l.
,
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M
u
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-
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jec
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ter
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ti
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ti
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ss
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(1
)
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p
.
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5
,
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0
1
5
.
[3
6
]
F
.
Ra
m
e
z
a
n
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t
a
l.
,
“
T
a
s
k
-
Ba
se
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lan
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ti
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g
Us
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g
P
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rti
c
le S
w
a
r
m
Op
ti
m
iza
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n
.
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ter
n
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t
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l
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rn
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ra
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lel
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ra
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n
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l/
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(
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0
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4
),
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p
.
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0
1
3
.
[3
7
]
R.
S
h
o
jae
e
,
e
t
a
l.
,
“
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Ne
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Ca
t
S
w
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m
Op
ti
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iza
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lg
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sk
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ll
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ti
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Distrib
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s,” in
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sy
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ter
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s (
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,
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p
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6
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.
[3
8
]
R.
C.
E
b
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rh
a
rt
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n
d
Y.
S
h
i,
“
Co
m
p
a
rin
g
In
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h
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a
n
d
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o
n
st
rictio
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c
to
rs
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p
a
rti
c
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sw
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r
m
o
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ti
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iza
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n
,
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n
p
ro
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e
e
d
in
g
s o
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th
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EE
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fer
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n
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lu
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ry
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t
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ICEC
,
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p
.
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4
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,
2
0
0
0
.
[3
9
]
M
.
S
.
A
b
d
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lh
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m
id
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t
a
l.
,
“
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a
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to
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u
sin
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d
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a
m
ic
c
lu
ste
rin
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a
lg
o
rit
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m
,
”
N
e
u
ra
l
Co
m
p
u
t
&
Ap
p
li
c
,
2
0
1
6
.
B
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RAP
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D.
in
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Un
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siti
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M
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p
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