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m
p
u
tin
g
.
T
h
e
s
c
h
ed
u
li
n
g
i
n
clo
u
d
co
m
p
u
ti
n
g
i
s
d
if
f
er
e
n
t
f
r
o
m
th
e
tr
ad
itio
n
al
s
y
s
te
m
b
ec
au
s
e
o
f
clo
u
d
co
m
p
u
tin
g
f
ea
t
u
r
es
lik
e
elast
icit
y
,
p
a
y
-
p
er
-
u
s
e
m
o
d
el,
on
-
d
e
m
an
d
,
m
u
l
ti
-
ten
a
n
t.
T
h
e
s
ch
ed
u
lin
g
h
as
t
w
o
p
h
a
s
es
r
eso
u
r
ce
p
r
o
v
is
io
n
i
n
g
an
d
ta
s
k
s
c
h
ed
u
li
n
g
.
T
h
e
r
eso
u
r
ce
s
ar
e
s
elec
ted
b
ased
o
n
th
e
q
u
alit
y
o
f
s
er
v
ice
(
Qo
S)
p
ar
a
m
eter
s
a
n
d
th
e
n
s
elec
t
th
e
s
u
itab
le
VM
in
s
ta
n
ce
f
o
r
t
h
o
s
e
tas
k
s
in
r
eso
u
r
ce
p
r
o
v
is
io
n
.
F
in
all
y
,
th
e
d
esire
d
VM
i
n
s
tan
ce
is
allo
t
ted
to
th
e
av
a
ilab
le
h
o
s
ts
o
r
p
h
y
s
ica
l
m
ac
h
i
n
es.
T
h
e
r
eso
u
r
ce
p
r
o
v
is
io
n
i
n
g
i
s
s
ig
n
i
f
ica
n
tl
y
r
elate
d
to
b
alan
ce
t
h
e
lo
ad
.
T
h
e
o
p
tim
a
l o
r
d
er
o
f
th
e
task
s
is
to
f
in
d
ac
co
r
d
in
g
to
t
h
e
s
c
h
ed
u
li
n
g
o
b
j
ec
tiv
es i
n
tas
k
s
c
h
ed
u
li
n
g
.
A
lt
h
o
u
g
h
s
ev
er
al
tr
ad
itio
n
al
alg
o
r
ith
m
s
li
k
e
f
ir
s
t
co
m
e
f
ir
s
t
s
er
v
e
(
F
C
FS
)
,
a
n
d
s
h
o
r
tes
t
j
o
b
f
ir
s
t
(
SJ
F),
w
er
e
p
r
o
p
o
s
ed
b
u
t
n
o
t
p
er
f
o
r
m
ed
w
e
ll
as
t
h
e
s
e
ar
e
d
ev
elo
p
ed
f
o
r
th
e
tr
ad
itio
n
al
co
m
p
u
tatio
n
.
T
h
e
s
ch
ed
u
lin
g
i
n
clo
u
d
co
m
p
u
ti
n
g
is
a
n
NP
-
h
ar
d
p
r
o
b
lem
.
So
,
th
e
m
eta
-
h
e
u
r
is
tic
alg
o
r
it
h
m
s
li
k
e
a
g
en
e
tic
alg
o
r
ith
m
(
G
A
)
,
an
d
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
P
SO)
,
ar
e
a
g
o
o
d
o
p
tio
n
,
w
h
er
e
y
o
u
f
i
n
d
th
e
n
ea
r
-
o
p
ti
m
al
s
o
lu
tio
n
.
B
u
t
w
it
h
th
e
i
n
cr
ea
s
e
in
t
h
e
p
r
o
b
le
m
s
p
ac
e,
t
h
e
m
eta
-
h
eu
r
i
s
tic
al
g
o
r
it
h
m
s
s
h
o
wed
s
o
m
e
li
m
itatio
n
s
lik
e
s
t
u
c
k
i
n
th
e
lo
ca
l
o
p
ti
m
al
s
o
lu
tio
n
.
W
ith
t
h
e
f
u
r
t
h
er
r
is
e
in
t
h
e
clo
u
d
'
s
co
m
p
lex
s
ch
ed
u
li
n
g
p
r
o
b
lem
,
v
ar
io
u
s
h
y
b
r
id
al
g
o
r
ith
m
s
w
e
r
e
p
r
o
p
o
s
ed
,
co
m
b
in
i
n
g
t
w
o
o
r
m
o
r
e
alg
o
r
it
h
m
s
a
n
d
tak
i
n
g
th
e
ad
v
an
tag
e
s
o
f
in
v
o
l
v
ed
alg
o
r
it
h
m
s
to
ef
f
icie
n
tl
y
s
o
lv
e
t
h
e
p
r
o
b
le
m
.
Th
e
m
aj
o
r
o
f
r
ev
ie
w
p
ap
er
s
w
a
s
f
o
c
u
s
ed
o
n
t
h
e
r
eso
u
r
ce
s
ch
ed
u
li
n
g
b
ased
o
n
h
e
u
r
is
t
ics
an
d
m
eta
-
h
eu
r
i
s
tics
m
et
h
o
d
s
lik
e
i
n
[
1
]
,
[
4
]
-
[
6
]
.
L
iv
n
y
et
a
l.
[
7
]
th
e
r
ev
ie
w
w
a
s
b
ased
o
n
w
o
r
k
f
lo
w
s
c
h
ed
u
li
n
g
ac
co
r
d
in
g
to
th
e
o
b
j
ec
tiv
e
an
d
ex
ec
u
tio
n
m
o
d
el.
So
m
e
o
th
er
p
a
p
er
s
lik
e
[
8
]
-
[
1
0
]
w
er
e
also
f
o
cu
s
ed
o
n
w
o
r
k
f
lo
w
s
ch
ed
u
li
n
g
a
n
d
d
ev
elo
p
a
tax
o
n
o
m
y
o
f
w
o
r
k
f
lo
w
m
a
n
ag
e
m
e
n
t
a
n
d
s
c
h
ed
u
li
n
g
.
I
n
t
h
i
s
p
ap
er
,
w
e
co
n
s
id
er
th
e
h
y
b
r
id
s
ch
ed
u
l
in
g
alg
o
r
it
h
m
s
in
clo
u
d
co
m
p
u
t
in
g
f
o
r
r
ev
ie
w
.
W
e
d
is
cu
s
s
ed
v
ar
io
u
s
o
b
j
ec
tiv
es
in
v
o
l
v
ed
in
o
p
ti
m
izatio
n
,
t
h
e
s
i
m
u
lato
r
u
s
ed
an
d
cla
s
s
i
f
ies ac
co
r
d
in
g
to
th
e
i
n
v
o
l
v
ed
alg
o
r
it
h
m
s
.
T
h
e
r
est
o
f
th
e
ar
ticle
is
o
r
g
a
n
ized
as
f
o
llo
w
s
.
I
n
s
ec
t
io
n
2
,
w
e
g
av
e
a
g
en
er
al
in
tr
o
d
u
ct
io
n
to
t
h
e
s
ch
ed
u
lin
g
p
r
o
b
le
m
,
d
if
f
er
en
t
t
y
p
es
o
f
s
ch
ed
u
li
n
g
p
r
o
b
le
m
s
in
clo
u
d
co
m
p
u
ti
n
g
.
T
h
e
v
a
r
io
u
s
o
b
j
ec
tiv
es
o
r
Qo
S p
ar
a
m
eter
s
f
o
u
n
d
i
n
t
h
e
l
iter
atu
r
e
ar
e
d
is
c
u
s
s
ed
i
n
s
ec
ti
o
n
3
w
it
h
t
h
eir
m
a
th
e
m
atica
l
d
ef
i
n
itio
n
.
T
h
e
n
,
w
e
class
i
f
y
t
h
e
h
y
b
r
id
al
g
o
r
ith
m
s
ac
co
r
d
in
g
to
t
h
e
i
n
v
o
lv
ed
alg
o
r
ith
m
s
.
W
e
also
g
a
v
e
a
n
o
v
er
v
ie
w
o
f
th
e
s
e
h
y
b
r
i
d
al
g
o
r
ith
m
s
a
lo
n
g
w
it
h
t
h
e
r
esear
ch
d
ir
ec
tio
n
s
a
n
d
is
s
u
es r
elate
d
to
s
c
h
ed
u
li
n
g
i
n
clo
u
d
co
m
p
u
t
in
g
.
T
h
e
co
m
p
lete
d
etail
s
ar
e
g
i
v
e
n
in
s
ec
tio
n
4
.
I
n
s
ec
tio
n
5
,
w
e
d
is
cu
s
s
t
h
e
tr
en
d
s
a
n
d
f
u
t
u
r
e
d
ir
ec
tio
n
o
f
h
y
b
r
i
d
s
ch
ed
u
lin
g
al
g
o
r
ith
m
s
i
n
clo
u
d
co
m
p
u
t
in
g
.
T
h
e
p
ap
er
is
en
d
ed
w
it
h
co
n
cl
u
s
io
n
as
m
e
n
tio
n
ed
in
s
ec
tio
n
6
.
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.
12
,
No
.
1
,
Feb
r
u
ar
y
20
22
:
8
8
0
-
895
882
2.
SCH
E
DU
L
I
NG
P
RO
B
L
E
M
T
h
e
s
ch
ed
u
li
n
g
ar
ch
itec
tu
r
e
o
f
clo
u
d
co
m
p
u
ti
n
g
is
il
lu
s
tr
at
ed
in
Fi
g
u
r
e
2
.
T
h
e
u
s
er
s
s
u
b
m
itted
t
h
e
task
to
th
e
s
c
h
ed
u
li
n
g
s
er
v
e
r
.
T
h
e
s
ch
ed
u
ler
ass
i
g
n
s
th
e
ap
p
r
o
p
r
iate
r
eso
u
r
ce
s
f
o
r
task
ex
ec
u
tio
n
u
s
i
n
g
s
ch
ed
u
lin
g
alg
o
r
it
h
m
s
ac
co
r
d
in
g
to
th
e
u
s
er
r
eq
u
ir
e
m
e
n
ts
.
T
h
e
r
eso
u
r
ce
s
ar
e
av
ailab
le
o
n
th
e
h
o
s
ts
o
r
p
h
y
s
ical
m
ac
h
in
e
s
o
f
th
e
clo
u
d
p
r
o
v
id
er
.
E
ac
h
h
o
s
t
i
s
d
i
v
id
ed
in
to
v
ar
io
u
s
v
ir
t
u
al
m
ac
h
i
n
es.
W
h
e
n
th
e
tas
k
co
m
p
lete
s
it
s
e
x
ec
u
tio
n
,
t
h
e
r
esu
lt
w
i
ll r
etu
r
n
to
th
e
r
esp
ec
ti
v
e
u
s
er
.
Fig
u
r
e
2
.
Sch
ed
u
li
n
g
in
clo
u
d
co
m
p
u
ti
n
g
e
n
v
ir
o
n
m
en
t
T
h
e
s
ch
ed
u
ler
g
e
n
er
ates
th
e
m
ap
p
in
g
b
et
w
ee
n
tas
k
s
a
n
d
v
ir
tu
al
m
ac
h
i
n
es
o
r
v
ir
tu
a
l
m
a
ch
in
e
s
an
d
h
o
s
ts
f
o
r
allo
t
m
en
t
ac
co
r
d
in
g
to
th
e
p
er
f
o
r
m
a
n
ce
p
ar
a
m
e
ter
s
.
Fig
u
r
e
3
s
h
o
w
s
a
s
a
m
p
le
m
a
p
p
in
g
o
f
n
u
m
b
er
o
f
task
s
to
th
e
n
u
m
b
er
o
f
th
e
v
ir
tu
al
m
ac
h
in
e
s
,
th
e
n
th
er
e
ar
e
co
m
b
i
n
atio
n
is
p
o
s
s
ib
le
if
b
r
u
te
f
o
r
ce
alg
o
r
ith
m
i
s
u
s
ed
.
Si
m
ilar
i
s
th
e
ca
s
e
w
it
h
v
ir
tu
al
m
ac
h
i
n
es
a
n
d
h
o
s
ts
.
T
h
e
s
c
h
ed
u
li
n
g
is
co
n
s
id
er
ed
a
co
m
p
le
x
p
r
o
b
lem
,
an
d
t
h
e
s
o
lu
tio
n
i
s
n
o
t
co
m
p
leted
in
p
o
l
y
n
o
m
ial
ti
m
e
[
1
1
]
.
I
t
is
g
o
o
d
to
f
in
d
a
n
ea
r
-
o
p
ti
m
al
s
o
lu
tio
n
to
th
e
s
c
h
ed
u
li
n
g
p
r
o
b
le
m
w
it
h
th
e
h
elp
o
f
m
eta
-
h
e
u
r
is
tic
al
g
o
r
ith
m
s
.
Fig
u
r
e
3
.
Ma
p
p
in
g
a
m
o
n
g
ta
s
k
s
,
VM
s
a
n
d
h
o
s
t
s
T
h
e
p
o
p
u
latio
n
-
b
ased
m
e
ta
-
h
eu
r
i
s
tic
s
c
h
ed
u
li
n
g
alg
o
r
ith
m
s
ta
k
e
a
s
et
o
f
s
o
lu
tio
n
s
k
n
o
w
n
a
s
p
o
p
u
latio
n
.
E
ac
h
m
e
m
b
er
o
f
th
e
p
o
p
u
latio
n
r
ep
r
esen
ts
t
h
e
s
o
lu
tio
n
to
th
e
p
r
o
b
le
m
.
Fo
r
ea
ch
iter
atio
n
,
th
e
s
o
lu
tio
n
s
w
ill
i
m
p
r
o
v
e
to
m
o
v
e
to
w
ar
d
s
th
e
n
ea
r
-
o
p
ti
m
al
r
es
u
lt
s
.
Fig
u
r
e
4
s
h
o
w
s
th
e
s
a
m
p
le
en
co
d
in
g
o
f
a
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:
2088
-
8708
Hyb
r
id
s
ch
ed
u
lin
g
a
lg
o
r
ith
ms in
clo
u
d
co
mp
u
tin
g
:
a
r
ev
iew
(
N
ee
r
a
j A
r
o
r
a
)
883
p
o
p
u
latio
n
o
f
m
e
m
b
er
s
,
task
s
,
an
d
v
ir
t
u
al
m
ac
h
in
e
s
co
n
c
er
n
in
g
t
h
e
tas
k
s
c
h
ed
u
l
in
g
p
r
o
b
lem
.
I
n
tas
k
s
ch
ed
u
lin
g
al
g
o
r
ith
m
s
,
ea
c
h
s
o
lu
tio
n
h
as t
h
e
m
ap
p
i
n
g
o
f
ta
s
k
s
a
n
d
v
ir
t
u
al
m
ac
h
i
n
es.
Fig
u
r
e
4
.
E
n
co
d
in
g
o
f
s
ch
ed
u
l
in
g
p
r
o
b
le
m
i
n
clo
u
d
co
m
p
u
ti
n
g
2
.
1
.
B
a
s
ic
s
cheduli
ng
pro
ble
m
T
h
e
s
ch
ed
u
li
n
g
p
r
o
b
le
m
in
clo
u
d
co
m
p
u
ti
n
g
h
as
t
w
o
p
ar
ts
r
eso
u
r
ce
p
r
o
v
is
io
n
i
n
g
an
d
task
s
ch
ed
u
lin
g
.
T
h
e
task
s
c
h
ed
u
li
n
g
p
r
o
b
le
m
is
co
n
s
id
er
ed
a
m
ap
p
in
g
p
r
o
b
lem
.
Se
v
er
al
tas
k
s
ar
e
m
ap
p
ed
to
th
e
v
ar
io
u
s
a
v
ailab
le
v
ir
t
u
al
m
ac
h
in
e
s
u
c
h
th
a
t
it
o
p
ti
m
izes
t
h
e
o
b
j
ec
tiv
e
co
n
s
id
er
ed
b
y
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
.
Ma
th
e
m
atica
ll
y
,
th
e
p
r
o
b
le
m
ca
n
b
e
s
ee
n
as
a
m
ap
p
in
g
(
,
)
:
→
,
s
et
o
f
v
ir
t
u
al
m
ac
h
i
n
es
VM
i
s
{
1
,
2
,
.
.
.
,
}
an
d
s
et
o
f
ta
s
k
s
T
is
=
{
1
,
2
,
.
.
.
,
}
.
E
ac
h
v
ir
t
u
al
m
ac
h
in
e
h
as
its
ch
ar
ac
ter
is
tic
s
li
k
e
id
,
clo
ck
s
p
ee
d
in
MI
P
S,
R
A
M
s
ize,
a
n
d
s
e
v
er
al
p
r
o
ce
s
s
o
r
s
.
E
ac
h
t
ask
also
h
as
it
s
ch
ar
ac
ter
is
tic
s
li
k
e
id
,
len
g
t
h
,
ar
r
iv
al
ti
m
e,
an
d
f
ile
s
iz
e.
Usi
n
g
t
h
ese
ch
ar
ac
ter
is
tics
o
f
tas
k
s
an
d
v
ir
tu
a
l
m
ac
h
in
e
s
,
o
n
e
n
ee
d
s
to
d
esig
n
t
h
e
s
c
h
ed
u
li
n
g
alg
o
r
it
h
m
f
o
r
g
en
er
ati
n
g
th
e
m
ap
p
in
g
s
o
th
at
th
e
tar
g
eted
o
b
j
ec
tiv
es
w
i
ll
b
e
o
p
ti
m
ized
.
W
h
er
ea
s
,
in
r
eso
u
r
ce
p
r
o
v
i
s
io
n
in
g
,
t
h
e
VM
s
ar
e
allo
tted
to
av
ailab
le
p
h
y
s
ical
m
ac
h
in
e
s
ac
co
r
d
in
g
to
t
h
eir
ch
ar
ac
ter
is
tics
.
T
h
e
b
asic in
te
n
ti
o
n
o
f
r
eso
u
r
ce
p
r
o
v
is
io
n
in
g
i
s
to
b
alan
ce
th
e
lo
ad
a
m
o
n
g
th
e
av
ai
lab
le
s
er
v
er
s
f
o
r
b
etter
f
u
n
ctio
n
i
n
g
[
5
]
.
Ma
th
em
atica
ll
y
,
th
e
p
r
o
b
lem
ca
n
b
e
s
ee
n
as
a
m
ap
p
in
g
(
,
)
:
→
,
s
e
t
o
f
p
h
y
s
ical
m
ac
h
i
n
e
s
o
r
h
o
s
ts
=
{
1
,
2
,
.
.
.
,
}
an
d
s
et
o
f
v
ir
tu
a
l
m
ac
h
in
e
s
VM
is
{
1
,
2
,
.
.
.
,
}
as sh
o
w
n
in
th
e
Fi
g
u
r
e
3
.
2
.
2
.
Schedu
lin
g
pro
ble
m
w
it
h c
o
ns
t
ra
ints
T
h
e
s
ch
ed
u
li
n
g
ca
n
b
e
d
o
n
e
b
y
co
n
s
id
er
in
g
s
o
m
e
co
n
s
tr
ai
n
ts
,
w
h
er
e
th
e
v
al
u
es
o
f
o
b
j
e
ctiv
es
ar
e
l
y
i
n
g
b
et
w
ee
n
s
o
m
e
p
r
ed
ef
in
e
d
th
r
esh
o
ld
li
m
its
.
T
h
e
m
o
s
t
co
m
m
o
n
ar
e
th
e
b
u
d
g
e
t
an
d
d
ea
d
lin
e
co
n
s
tr
ai
n
t
s
.
I
n
b
u
d
g
et
co
n
s
tr
ai
n
ts
,
th
e
u
s
er
'
s
ta
s
k
s
n
ee
d
to
b
e
co
m
p
l
ete
i
n
s
o
m
e
p
r
ed
ef
in
ed
th
r
es
h
o
ld
o
f
t
h
e
co
s
t,
w
h
er
ea
s
in
d
ea
d
lin
e
co
n
s
tr
ai
n
t,
t
h
e
m
a
k
esp
an
v
al
u
e
is
co
n
s
id
er
ed
[
1
2
]
.
Ma
th
e
m
at
icall
y
,
th
e
s
c
h
ed
u
li
n
g
p
r
o
b
lem
w
it
h
co
n
s
tr
ain
ts
ca
n
b
e
d
escr
ib
ed
u
s
in
g
(
1
)
an
d
(
2
)
:
min
/
ma
x
f
(
x
)
=
x
(
1
)
s
u
b
j
ec
t to
:
x
∈
ob
je
c
ti
ve
s
|
{
y
≤
x
≤
z
}
(
2
)
w
h
er
e
(
)
is
th
e
f
it
n
ess
f
u
n
ctio
n
(
d
is
cu
s
s
ed
in
s
ec
tio
n
3
)
a
r
e
th
e
o
b
j
ec
tiv
e
v
alu
es
an
d
t
h
e
v
al
u
e
o
f
s
h
o
u
ld
lies
in
b
et
w
ee
n
p
r
ed
ef
i
n
ed
th
r
es
h
o
ld
v
al
u
es
an
d
2
.
3
.
Wo
rk
f
lo
w
s
chedu
lin
g
pro
ble
m
T
h
e
w
o
r
k
f
lo
w
s
ch
ed
u
li
n
g
is
s
ch
ed
u
lin
g
w
it
h
p
r
ec
ed
en
ce
c
o
n
s
tr
ain
t,
w
h
er
e
t
h
e
o
r
d
er
o
f
ar
r
iv
al
o
f
task
s
is
co
n
s
id
er
ed
[
8
]
.
T
h
is
s
ch
ed
u
lin
g
i
s
also
ca
lled
d
e
p
en
d
en
t
tas
k
s
ch
ed
u
li
n
g
.
T
h
e
w
o
r
k
f
lo
w
ca
n
b
e
r
ep
r
esen
ted
u
s
i
n
g
d
ir
ec
ted
ac
y
clic
g
r
ap
h
(
D
AG)
,
w
h
er
e
t
h
e
n
o
d
es
in
th
e
D
A
G
r
ep
r
esen
t
th
e
task
s
(
T
)
,
an
d
th
e
ed
g
es
(
E
)
j
o
in
i
n
g
th
e
n
o
d
es
r
ep
r
esen
t
t
h
e
d
ep
en
d
e
n
c
y
b
et
w
ee
n
t
h
e
tas
k
s
.
A
s
a
m
p
le
w
o
r
k
f
lo
w
is
s
h
o
w
n
i
n
Fig
u
r
e
5
,
co
n
tain
in
g
s
ix
ta
s
k
s
{
1
,
2
,
3
,
4
,
5
,
6
}
.
T
h
e
task
s
1
an
d
{
4
,
5
,
6
}
ar
e
th
e
e
n
tr
y
an
d
e
x
it
task
s
,
r
esp
ec
tiv
el
y
.
E
ac
h
ed
g
e
o
f
th
e
D
A
G
s
h
o
w
s
th
e
d
ep
en
d
en
cies
b
et
w
ee
n
th
e
tas
k
s
.
Fo
r
ex
am
p
le,
2
ex
ec
u
ted
a
f
ter
1
is
s
h
o
w
n
b
y
t
h
e
p
air
ed
s
et
{
1
,
2
}
.
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.
12
,
No
.
1
,
Feb
r
u
ar
y
20
22
:
8
8
0
-
895
884
Fig
u
r
e
5
.
A
s
i
m
p
le
w
o
r
k
f
lo
w
I
n
r
ec
en
t
y
ea
r
s
,
t
h
e
p
r
o
p
o
s
ed
s
ch
ed
u
li
n
g
alg
o
r
it
h
m
s
wer
e
test
ed
i
n
t
h
e
r
ea
l
w
o
r
k
s
cie
n
ti
f
ic
w
o
r
k
lo
ad
s
.
T
h
ese
w
o
r
k
lo
ad
s
ex
p
r
ess
th
e
co
m
p
lex
co
m
p
u
tati
o
n
p
r
o
b
lem
s
t
h
at
ca
n
b
e
s
o
lv
e
d
u
s
in
g
d
is
tr
ib
u
ted
an
d
p
ar
allel
co
m
p
u
ti
n
g
[
1
3
]
.
T
h
er
e
ar
e
s
ev
er
al
ty
p
es
o
f
s
ci
en
ti
f
ic
w
o
r
k
f
lo
w
li
k
e
C
y
b
er
Sh
ak
e,
I
n
s
p
ir
al,
Mo
n
tag
e,
an
d
Sip
th
,
av
a
ilab
le
b
y
P
eg
a
s
u
s
[
1
4
]
.
T
h
e
C
y
b
e
r
Sh
ak
e
w
o
r
k
f
lo
w
a
n
al
y
ze
s
t
h
e
d
is
aster
m
o
d
eli
n
g
an
d
p
r
ed
ictio
n
o
f
th
e
p
ar
ticu
l
ar
g
eo
g
r
ap
h
ical
lo
ca
tio
n
b
y
d
esig
n
i
n
g
t
h
e
h
az
ar
d
m
ap
s
[
1
5
]
.
Fo
r
s
tu
d
y
in
g
a
n
d
an
a
l
y
z
in
g
t
h
e
g
r
a
v
itatio
n
al
w
a
v
ef
o
r
m
,
t
h
e
i
n
s
p
ir
al
w
o
r
k
f
lo
w
is
u
s
ed
[
1
2
]
.
T
h
e
m
o
n
tag
e
wo
r
k
f
lo
w
i
s
u
s
ed
b
y
NAS
A
in
astro
p
h
y
s
ic
s
to
cr
ea
te
th
e
c
u
s
to
m
m
o
s
aic
s
o
f
t
h
e
s
k
y
b
y
ta
k
i
n
g
m
u
l
tip
le
i
n
p
u
t
i
m
ag
e
s
[
1
6
]
.
Sip
h
t
w
o
r
k
f
lo
w
ap
p
licatio
n
i
s
m
ai
n
t
ain
ed
b
y
Har
v
ar
d
to
u
s
e
i
n
th
e
f
ield
o
f
b
io
in
f
o
r
m
atic
s
[
1
7
]
.
3.
F
I
T
N
E
SS
F
U
NCT
I
O
N
T
h
e
f
itn
e
s
s
f
u
n
ctio
n
d
escr
ib
ed
th
e
tar
g
eted
o
b
j
ec
tiv
es
to
b
e
o
p
tim
ized
u
s
i
n
g
th
e
p
r
o
p
o
s
ed
s
ch
ed
u
lin
g
alg
o
r
ith
m
[
9
]
.
I
f
o
n
l
y
o
n
e
o
b
j
ec
tiv
e
i
s
p
r
esen
t
i
n
t
h
e
f
it
n
es
s
f
u
n
ctio
n
,
th
e
n
it
is
a
s
i
n
g
le
o
b
j
ec
tiv
e
f
u
n
ctio
n
,
also
k
n
o
w
n
a
s
o
b
j
ec
tiv
e
f
u
n
c
tio
n
[
7
]
.
Si
m
ilar
l
y
,
i
f
it
co
n
ta
in
s
t
w
o
-
o
b
j
ec
tiv
e,
it
is
k
n
o
w
n
as
a
b
i
-
o
b
j
ec
tiv
e
f
u
n
ctio
n
,
a
n
d
it
h
as
m
o
r
e
t
h
a
n
t
w
o
o
b
j
ec
tiv
es
a
n
d
is
th
e
n
k
n
o
w
n
as
a
m
u
lti
-
o
b
j
ec
tiv
e
f
u
n
c
tio
n
.
Ge
n
er
all
y
,
th
e
f
it
n
es
s
f
u
n
ctio
n
c
a
n
b
e
r
ep
r
esen
ted
as (
3
)
,
(
1
,
2
,
…
)
=
1
×
1
+
2
×
2
+
…
+
×
(
3
)
w
h
er
e
1
,
2
,
…
ar
e
th
e
o
b
j
ec
tiv
es
an
d
1
,
2
,
.
.
.
.
.
ar
e
th
e
w
e
ig
h
t
as
s
i
g
n
ed
f
o
r
ea
ch
o
b
j
ec
tiv
es.
Fig
u
r
e
6
s
h
o
w
s
t
h
e
d
i
f
f
er
e
n
t
o
b
j
ec
tiv
es
co
n
s
id
er
ed
in
t
h
e
ex
i
s
ti
n
g
s
ch
ed
u
lin
g
al
g
o
r
ith
m
s
.
T
h
e
d
if
f
er
en
t
o
b
j
ec
tiv
e
s
co
n
s
id
er
ed
in
th
e
li
ter
atu
r
e
w
il
l b
e
ex
p
lain
ed
in
t
h
e
f
o
llo
w
i
n
g
s
u
b
-
s
ec
tio
n
s
.
Fig
u
r
e
6
.
Ob
j
ec
tiv
es o
f
s
ch
ed
u
lin
g
alg
o
r
it
h
m
s
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:
2088
-
8708
Hyb
r
id
s
ch
ed
u
lin
g
a
lg
o
r
ith
ms in
clo
u
d
co
mp
u
tin
g
:
a
r
ev
iew
(
N
ee
r
a
j A
r
o
r
a
)
885
3
.
1
.
M
a
k
esp
a
n
T
h
e
m
a
k
esp
an
is
t
h
e
m
a
x
i
m
u
m
co
m
p
letio
n
ti
m
e
tak
e
n
b
y
v
ir
tu
al
m
ac
h
i
n
es.
I
n
o
th
er
w
o
r
d
s
,
th
e
ti
m
e
tak
en
w
h
e
n
all
th
e
tas
k
s
a
r
e
f
in
is
h
ed
its
ex
ec
u
tio
n
o
n
th
e
v
ir
t
u
al
m
ac
h
i
n
e
is
t
h
e
m
a
k
esp
an
[
1
8
]
.
Ma
th
e
m
atica
ll
y
,
th
e
m
a
k
esp
a
n
ca
n
b
e
d
er
iv
ed
u
s
in
g
(
4
)
,
=
{
|
=
1
,
2
,
.
.
.
}
(
4
)
w
h
er
e
is
th
e
co
m
p
letio
n
ti
m
e
o
f
v
ir
t
u
al
m
ac
h
i
n
e
.
T
h
e
co
m
p
letio
n
ti
m
e
is
t
h
e
m
a
x
i
m
u
m
e
x
ec
u
t
io
n
ti
m
e
o
f
ta
s
k
s
.
I
n
ca
s
e
tas
k
s
ar
e
d
ep
en
d
en
t,
t
h
e
n
t
h
e
w
ai
tin
g
ti
m
e
o
f
ta
s
k
s
i
s
al
s
o
co
n
s
id
er
ed
.
T
h
e
co
m
p
letio
n
ti
m
e
is
d
ep
icted
in
(
5
)
.
=
{
ma
x
(
)
if
f
(
)
=
∅
(
+
)
if
f
(
)
≠
∅
(
5
)
T
h
e
w
aiti
n
g
t
i
m
e
o
f
tas
k
is
th
e
m
a
x
i
m
u
m
co
m
p
letio
n
ti
m
e
o
f
all
th
e
p
r
ed
ec
ess
o
r
task
s
o
f
a
w
o
r
k
f
lo
w
,
a
s
s
h
o
w
n
in
(
6
)
.
T
h
e
ex
ec
u
tio
n
ti
m
e
o
f
a
tas
k
ca
n
b
e
ca
lc
u
lated
u
s
i
n
g
(
7
)
,
=
{
0
if
f
(
)
=
∅
ma
x
(
)
if
f
(
)
≠
∅
(
6
)
=
(
)
×
(
7
)
w
h
er
e
is
th
e
s
ize
o
f
th
e
tas
k
in
a
m
illi
o
n
i
n
s
tr
u
ctio
n
(
MI
)
,
(
)
is
th
e
n
u
m
b
er
o
f
co
r
e
ass
ig
n
e
d
to
th
e
VM
,
s
ize
o
f
ea
c
h
co
r
e
in
MI
P
S.
3
.
2
.
Co
s
t
T
h
e
co
s
t is th
e
cr
u
cia
l o
b
j
ec
tiv
e
to
b
e
o
p
tim
ized
,
as c
lo
u
d
co
m
p
u
ti
n
g
f
o
llo
w
s
a
p
a
y
-
as
-
y
o
u
-
g
o
b
illi
n
g
s
ch
e
m
e,
u
s
i
n
g
a
n
e
f
f
icie
n
t
s
c
h
ed
u
li
n
g
al
g
o
r
ith
m
.
Ge
n
er
all
y
,
t
h
e
clo
u
d
s
er
v
ice
p
r
o
v
id
er
s
ch
ar
g
es
f
o
r
s
o
m
e
s
p
ec
if
ic
ti
m
e
i
n
ter
v
al
b
ased
o
n
t
h
e
a
m
o
u
n
t
o
f
s
to
r
ag
e.
E
x
e
cu
tio
n
co
s
ts
,
co
m
m
u
n
icatio
n
co
s
ts
,
an
d
s
to
r
ag
e
co
s
ts
ar
e
co
n
s
id
er
ed
in
clo
u
d
co
m
p
u
ti
n
g
.
VM
's
to
tal
ex
e
cu
tio
n
co
s
t
is
t
h
e
co
s
t
ch
ar
g
ed
o
f
VM
p
er
u
n
it
in
ter
v
a
l
an
d
th
e
ex
ec
u
tio
n
ti
m
e
o
f
task
s
o
n
t
h
at
VM
.
Ma
th
e
m
atica
ll
y
,
th
e
to
tal
ex
ec
u
tio
n
co
s
t
(
T
E
C
)
o
f
VM
is
s
h
o
w
n
in
(
8
)
.
=
∑
∈
,
=
1
×
:
∈
(
8
)
Si
m
i
lar
l
y
,
t
h
e
to
tal
ex
ec
u
tio
n
co
s
t o
f
w
o
r
k
f
lo
w
W
is
g
i
v
en
i
n
(
9
)
[
7
]
,
=
∑
∈
,
=
1
×
:
∈
(
9
)
w
h
er
e
is
t
h
e
co
s
t
o
f
t
y
p
e
-
i
V
M
in
s
tan
ce
f
o
r
a
u
n
it
t
i
m
e
in
th
e
clo
u
d
d
ata
ce
n
ter
.
is
t
h
e
t
i
m
e
p
er
io
d
f
o
r
w
h
ic
h
th
e
u
s
er
u
s
es t
h
e
r
eso
u
r
ce
s
.
is
th
e
e
x
ec
u
t
io
n
ti
m
e
o
f
t
ask
b
y
t
y
p
e
-
i V
M
i
n
s
tan
ce
.
W
h
en
th
e
tas
k
is
a
llo
tted
o
n
t
h
e
d
i
f
f
er
e
n
t
m
ac
h
i
n
es,
th
e
co
m
m
u
n
icatio
n
co
s
t
is
al
s
o
in
c
l
u
d
ed
.
I
t
is
also
k
n
o
w
n
as d
ata
tr
an
s
f
er
co
s
t a
n
d
m
a
th
e
m
atica
l
l
y
s
h
o
w
n
i
n
(
1
0
)
[
1
9
]
,
=
(
,
)
(
,
)
(
1
0
)
w
h
er
e
is
t
h
e
co
m
m
u
n
icatio
n
co
s
t
b
et
w
ee
n
ta
s
k
s
an
d
.
(
,
)
is
th
e
len
g
th
o
f
t
h
e
o
u
tp
u
t
f
i
le
o
f
t
as
k
an
d
(
,
)
is
t
h
e
b
an
d
w
id
th
b
e
t
w
ee
n
t
h
e
r
eso
u
r
ce
s
.
I
f
th
e
task
s
ar
e
o
n
th
e
s
a
m
e
m
a
ch
in
e,
t
h
e
co
m
m
u
n
icatio
n
co
s
t
b
ec
o
m
e
ze
r
o
.
Oth
er
t
y
p
es
o
f
co
s
t
s
i
n
clu
d
e
t
h
e
co
s
t
o
f
s
to
r
in
g
th
e
task
s
o
n
t
h
e
r
eso
u
r
ce
.
T
h
is
t
y
p
e
o
f
co
s
t
d
ep
en
d
s
u
p
o
n
t
h
e
s
ize
o
f
t
h
e
t
ask
allo
tted
to
t
h
e
r
e
s
o
u
r
ce
s
,
th
e
clo
u
d
p
r
o
v
id
er
ch
ar
g
e,
ac
co
r
d
in
g
to
t
h
e
v
o
l
u
m
e
o
f
d
ata
f
i
les s
to
r
ed
o
n
to
th
e
m
ac
h
i
n
e
s
.
3
.
3
.
Ut
iliza
t
io
n
T
h
e
VM
u
tili
za
tio
n
i
s
t
h
e
r
atio
n
o
f
p
r
o
ce
s
s
i
n
g
ti
m
e
a
n
d
m
a
k
e
s
p
an
an
d
ex
p
r
es
s
ed
u
s
i
n
g
(
1
1
)
,
=
∑
=
1
(
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.
12
,
No
.
1
,
Feb
r
u
ar
y
20
22
:
8
8
0
-
895
886
w
h
er
e,
is
th
e
p
r
o
ce
s
s
i
n
g
ti
m
e
o
f
tas
k
o
n
.
3
.
4
.
T
hro
ug
hp
ut
T
h
e
th
r
o
u
g
h
p
u
t
i
s
ca
lc
u
lated
as
t
h
e
n
u
m
b
er
o
f
ta
s
k
in
s
tr
u
c
t
io
n
s
co
m
p
leted
i
n
a
u
n
it
ti
m
e
[
2
0
]
.
T
h
e
th
r
o
u
g
h
p
u
t o
f
is
ca
lcu
lated
as
p
er
(
1
2
)
,
ℎ
ℎ
=
∑
(
)
=
1
(
1
2
)
w
h
er
e
is
t
h
e
co
m
p
letio
n
ti
m
e
o
f
th
e
an
d
(
)
is
th
e
s
ize
o
f
in
m
illi
o
n
i
n
s
tr
u
ctio
n
s
.
3
.
5
.
L
o
a
d
ba
la
ncing
T
h
e
L
o
ad
b
alan
cin
g
i
s
m
ea
s
u
r
ed
u
s
in
g
t
h
e
d
eg
r
ee
o
f
i
m
b
a
la
n
ce
as
m
en
t
io
n
ed
in
t
h
e
(
1
3
)
[
2
1
]
,
=
−
(
1
3
)
w
h
er
e
,
an
d
ar
e
m
a
x
i
m
u
m
,
m
i
n
i
m
u
m
,
a
n
d
av
er
a
g
e
to
tal
e
x
ec
u
t
io
n
t
i
m
e
s
a
m
o
n
g
all
VM
s
,
r
esp
ec
tiv
el
y
.
E
T
ca
n
b
e
f
in
d
u
s
in
g
(
1
3
)
.
3
.
6
.
O
t
her
T
h
er
e
ar
e
o
th
er
Qo
S p
ar
am
ete
r
s
m
e
n
tio
n
ed
in
[
5
]
:
Scalab
ilit
y
:
T
h
is
m
etr
ics
i
s
u
s
ed
to
ch
ec
k
th
e
p
er
f
o
r
m
an
ce
o
f
an
alg
o
r
it
h
m
i
n
t
h
e
in
cr
ea
s
in
g
n
u
m
b
er
o
f
n
o
d
es.
Fau
lt
to
ler
an
ce
:
I
t
is
u
s
ed
to
ch
ec
k
th
e
ca
p
ab
ilit
y
o
f
t
h
e
a
l
g
o
r
ith
m
w
o
r
k
in
g
i
n
u
n
d
er
s
o
m
e
f
ail
u
r
e
li
k
e
lin
k
s
,
an
d
p
r
o
ce
s
s
i
n
g
u
n
its
.
E
n
er
g
y
co
n
s
u
m
p
tio
n
:
I
t
s
h
o
ws
th
e
to
tal
a
m
o
u
n
t
o
f
e
n
er
g
y
co
n
s
u
m
ed
b
y
th
e
s
y
s
te
m
.
T
h
i
s
p
ar
a
m
eter
is
u
s
ed
to
av
o
id
o
v
er
h
ea
ti
n
g
o
f
a
p
ar
ticu
lar
n
o
d
e
b
y
u
s
i
n
g
an
e
f
f
icie
n
t
en
er
g
y
-
s
a
v
i
n
g
lo
ad
b
alan
cin
g
alg
o
r
ith
m
.
4.
H
YB
RID SCH
E
D
UL
I
N
G
A
L
G
O
R
I
T
H
M
S
Fro
m
t
h
e
liter
at
u
r
e,
w
e
f
o
u
n
d
th
at
m
o
s
t
o
f
t
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
alg
o
r
ith
m
s
w
er
e
t
h
e
co
m
b
i
n
atio
n
o
f
th
e
p
o
p
u
lar
m
e
ta
-
h
e
u
r
is
tic
al
g
o
r
ith
m
s
li
k
e
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
P
SO)
[
2
2
]
,
g
en
etic
alg
o
r
ith
m
s
(
G
A
)
[
2
3
]
,
an
t
co
lo
n
y
o
p
ti
m
izatio
n
(
AC
O)
[
2
4
]
,
o
r
its
v
ar
ian
ts
.
W
e
class
i
f
y
th
e
h
y
b
r
id
alg
o
r
it
h
m
ac
co
r
d
in
g
to
th
e
in
v
o
l
v
ed
al
g
o
r
ith
m
s
d
u
r
in
g
t
h
e
h
y
b
r
id
izatio
n
,
a
s
s
h
o
w
n
i
n
Fig
u
r
e
7
.
T
h
e
f
o
llo
w
in
g
s
u
b
-
s
ec
tio
n
w
i
ll
r
ev
ie
w
th
e
h
y
b
r
id
alg
o
r
ith
m
s
ar
e
p
er
th
e
class
if
icatio
n
.
Fig
u
r
e
7
.
C
lass
if
ica
tio
n
o
f
h
y
b
r
id
s
ch
ed
u
li
n
g
al
g
o
r
it
h
m
s
in
cl
o
u
d
co
m
p
u
ti
n
g
4
.
1
.
H
y
brid us
ing
P
SO
Sh
ir
v
an
i
[
2
5
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
d
is
cr
ete
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
HDP
SO)
al
g
o
r
it
h
m
,
w
h
ich
w
a
s
d
esig
n
ed
u
s
i
n
g
a
d
is
cr
ete
p
ar
ticle
s
w
ar
m
al
g
o
r
ith
m
a
n
d
h
ill
-
cli
m
b
i
n
g
al
g
o
r
ith
m
.
T
h
e
b
asic
id
ea
w
as
to
u
s
e
a
h
il
l
-
c
li
m
b
i
n
g
alg
o
r
it
h
m
f
o
r
lo
ca
l
s
ea
r
ch
to
b
alan
c
e
b
etw
ee
n
ex
p
lo
r
atio
n
an
d
e
x
p
lo
itatio
n
.
T
h
e
o
b
j
ec
tiv
e
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:
2088
-
8708
Hyb
r
id
s
ch
ed
u
lin
g
a
lg
o
r
ith
ms in
clo
u
d
co
mp
u
tin
g
:
a
r
ev
iew
(
N
ee
r
a
j A
r
o
r
a
)
887
w
a
s
to
m
i
n
i
m
ize
th
e
m
a
k
e
s
p
an
.
T
h
e
HDP
SO
p
er
f
o
r
m
ed
b
et
ter
in
s
c
h
ed
u
le
len
g
t
h
r
atio
(
S
L
R
)
,
Sp
ee
d
-
Up
,
a
n
d
ef
f
icien
c
y
th
a
n
th
e
o
th
er
ex
iti
n
g
h
eu
r
i
s
tics
ap
p
r
o
ac
h
es
an
d
m
eta
-
h
e
u
r
is
tics
ap
p
r
o
ac
h
es
lik
e
G
A
,
P
SO
.
I
n
th
e
n
ea
r
f
u
tu
r
e,
t
h
e
au
t
h
o
r
s
w
ill co
n
s
id
er
th
e
m
a
k
esp
an
a
n
d
co
s
t o
b
j
ec
tiv
es f
o
r
o
p
ti
m
izi
n
g
th
e
s
ch
ed
u
li
n
g
.
Do
m
an
al
e
t
a
l.
[
2
6
]
p
r
esen
te
d
th
e
alg
o
r
it
h
m
,
w
h
ic
h
w
as
t
h
e
h
y
b
r
id
v
er
s
io
n
o
f
m
o
d
i
f
ie
d
P
SO
an
d
Mo
d
if
ied
C
at
S
w
ar
m
A
l
g
o
r
ith
m
;
t
h
e
al
g
o
r
ith
m
r
ed
u
ce
d
t
h
e
a
v
er
ag
e
r
esp
o
n
s
e
ti
m
e.
I
t
in
cr
ea
s
ed
r
eso
u
r
ce
u
tili
za
t
io
n
co
m
p
ar
ed
w
it
h
r
o
u
n
d
-
r
o
b
in
(
R
R
)
,
m
o
d
if
ied
P
SO
(
MP
SO
)
,
m
o
d
if
ied
ca
t
s
w
ar
m
o
p
ti
m
iza
tio
n
(
MCS
O
)
,
AC
O,
a
n
d
E
x
ac
t
al
g
o
r
ith
m
.
T
h
e
s
i
m
u
lato
r
u
s
ed
was
P
y
Si
m
to
v
alid
ate
t
h
e
r
e
s
u
l
ts
.
T
h
e
a
u
th
o
r
s
w
il
l
co
n
s
id
er
th
e
d
y
n
a
m
ic
s
ch
ed
u
li
n
g
i
n
w
h
ich
ta
s
k
s
w
ill
b
e
en
te
r
in
g
t
h
e
clo
u
d
w
it
h
d
if
f
er
en
t
i
n
ter
-
ar
r
i
v
al
ti
m
es
i
n
f
u
tu
r
e
w
o
r
k
.
J
en
a
et
a
l.
[
2
7
]
w
a
s
co
n
s
id
er
m
o
d
i
f
ied
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
MP
SO)
an
d
i
m
p
r
o
v
ed
th
e
Q
-
lear
n
in
g
al
g
o
r
ith
m
to
p
r
o
p
o
s
e
a
h
y
b
r
id
alg
o
r
ith
m
n
a
m
ed
QM
P
SO
f
o
r
lo
ad
b
alan
cin
g
in
clo
u
d
co
m
p
u
ti
n
g
.
T
h
e
v
elo
cit
y
i
n
MP
SO
i
s
ad
j
u
s
ted
u
s
in
g
t
h
e
b
es
t
ac
tio
n
g
e
n
er
a
ted
b
y
t
h
e
i
m
p
r
o
v
ed
Q
-
lear
n
i
n
g
alg
o
r
it
h
m
.
T
h
e
o
b
j
ec
tiv
es
o
f
th
e
al
g
o
r
i
th
m
wer
e
to
o
p
tim
ize
t
h
e
m
a
k
esp
a
n
,
th
r
o
u
g
h
p
u
t,
an
d
en
er
g
y
u
tili
za
tio
n
.
T
h
e
r
esu
lts
w
er
e
v
alid
ated
u
s
in
g
th
e
C
lo
u
d
Si
m
3
.
0
.
3
s
i
m
u
lato
r
.
T
h
e
r
es
u
lts
s
h
o
w
ed
a
r
ed
u
ctio
n
i
n
t
h
e
w
aiti
n
g
ti
m
e
o
f
th
e
task
s
co
n
ce
r
n
in
g
MP
SO
a
n
d
Q
-
lear
n
i
n
g
al
g
o
r
ith
m
s
.
I
n
d
ep
en
d
en
t
ta
s
k
s
w
er
e
co
n
s
id
er
ed
f
o
r
t
h
e
i
m
p
le
m
en
ta
tio
n
i
n
th
e
n
ea
r
f
u
t
u
r
e
as th
e
a
u
t
h
o
r
s
co
n
s
id
er
ed
o
n
l
y
t
h
e
d
ep
en
d
en
t ta
s
k
s
.
Fire
f
l
y
al
g
o
r
ith
m
w
as
co
m
b
in
ed
w
it
h
a
n
i
m
p
r
o
v
ed
P
SO
al
g
o
r
ith
m
(
I
P
SO)
to
m
ak
e
a
n
o
t
h
er
h
y
b
r
id
alg
o
r
ith
m
n
a
m
ed
I
P
SO
-
Fire
f
l
y
al
g
o
r
ith
m
[
2
8
]
.
T
h
e
b
asic
id
ea
w
as
to
u
s
e
t
h
e
f
ir
e
f
l
y
al
g
o
r
ith
m
f
o
r
p
r
o
v
id
in
g
th
e
in
i
tial
s
o
l
u
tio
n
o
f
I
P
SO.
T
h
e
I
P
SO
-
Fire
f
l
y
al
g
o
r
ith
m
p
er
f
o
r
m
ed
w
el
l
in
ter
m
s
o
f
av
er
ag
e
lo
ad
,
av
er
ag
e
tu
r
n
ar
o
u
n
d
ti
m
e,
av
er
ag
e
r
esp
o
n
s
e
ti
m
e
co
m
p
ar
ed
to
R
R
,
F
C
FS
,
SJ
F,
G
A
,
I
P
SO,
P
SO,
Fire
f
l
y
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
co
n
v
er
g
ed
f
a
s
t
in
c
o
m
p
ar
is
o
n
to
o
th
er
s
tate
-
of
-
ar
t
alg
o
r
ith
m
s
.
T
h
e
s
i
m
u
lato
r
u
s
ed
w
as
M
A
T
L
A
B
s
o
f
t
w
ar
e
f
o
r
ca
r
r
ied
o
u
t w
o
r
k
.
Ma
n
s
o
u
r
i
et
a
l.
[
2
9
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
task
s
ch
ed
u
li
n
g
alg
o
r
ith
m
u
s
i
n
g
th
e
f
u
zz
y
s
y
s
te
m
a
n
d
m
o
d
i
f
ied
p
ar
ticle
s
w
ar
m
o
p
tim
izatio
n
tech
n
iq
u
es.
T
h
e
o
b
j
e
ctiv
es
w
er
e
to
m
i
n
i
m
ize
m
ak
e
s
p
an
an
d
m
ax
i
m
ize
r
eso
u
r
ce
u
tili
za
tio
n
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
(
FMP
SO)
i
n
cr
ea
s
ed
th
e
ex
p
lo
r
atio
n
ab
ilit
y
b
y
i
n
tr
o
d
u
cin
g
f
o
u
r
m
o
d
i
f
ied
v
elo
cit
y
u
p
d
atin
g
m
eth
o
d
s
.
Fu
r
t
h
er
,
th
e
cr
o
s
s
o
v
er
an
d
m
u
ta
tio
n
o
p
er
ato
r
is
co
m
b
in
ed
w
it
h
th
e
P
SO
alg
o
r
ith
m
f
o
r
m
o
r
e
i
m
p
r
o
v
e
m
en
t.
T
h
e
r
es
u
lt
s
s
h
o
w
ed
i
m
p
r
o
v
e
m
e
n
t
in
th
e
d
eg
r
ee
o
f
i
m
b
alan
ce
,
m
a
k
esp
an
,
ex
ec
u
t
io
n
ti
m
e,
ef
f
icie
n
c
y
,
an
d
im
p
r
o
v
e
m
en
t
r
atio
co
m
p
ar
ed
to
s
tan
d
ar
d
-
P
SO
(
SP
SO
)
,
s
tan
d
ar
d
-
G
A
(
SG
A
)
,
m
o
d
i
f
ied
-
PS
O
(
MP
SO
)
,
an
d
s
tan
d
ar
d
GA
w
ith
f
u
zz
y
th
eo
r
y
(
FUGE
)
alg
o
r
ith
m
.
T
h
e
p
r
ec
e
d
en
ce
o
f
task
s
a
n
d
f
au
lt to
ler
an
ce
p
ar
a
m
eter
w
ill
b
e
co
n
s
id
er
ed
f
o
r
f
u
r
th
er
s
t
u
d
y
in
t
h
e
f
u
t
u
r
e.
Do
r
d
aie
an
d
Nav
i
m
ip
o
u
r
[
3
0
]
u
s
ed
a
h
il
l
-
c
li
m
b
i
n
g
al
g
o
r
ith
m
f
o
r
lo
ca
l
s
ea
r
c
h
to
m
o
d
i
f
y
t
h
e
e
x
is
ti
n
g
P
SO
alg
o
r
it
h
m
a
n
d
p
r
o
p
o
s
ed
a
h
y
b
r
id
p
ar
ticle
s
w
ar
m
o
p
ti
m
izat
io
n
a
n
d
h
ill
-
cli
m
b
i
n
g
alg
o
r
ith
m
f
o
r
ta
s
k
s
ch
ed
u
lin
g
in
t
h
e
clo
u
d
en
v
ir
o
n
m
e
n
t.
T
h
e
aim
w
a
s
to
r
ed
u
ce
th
e
m
ak
e
s
p
an
o
f
tas
k
s
ch
e
d
u
lin
g
.
T
h
e
au
th
o
r
s
u
s
ed
C
#
in
an
A
z
u
r
e
clo
u
d
e
n
v
ir
o
n
m
en
t to
g
en
er
ate
th
e
ex
p
er
i
m
en
t r
es
u
lts
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
a
n
d
s
o
m
e
ex
is
t
in
g
alg
o
r
it
h
m
s
w
er
e
tes
ted
u
n
d
er
r
an
d
o
m
a
n
d
s
cien
tif
ic
D
A
G
to
s
h
o
w
t
h
e
ef
f
ic
ien
c
y
i
n
ter
m
s
o
f
m
ak
e
s
p
an
a
n
d
f
o
u
n
d
b
en
ef
icia
l
co
m
p
ar
ed
to
o
th
er
alg
o
r
ith
m
s
.
I
n
th
e
f
u
t
u
r
e,
r
ea
l
-
w
o
r
ld
D
A
G
w
a
s
u
s
ed
to
test
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
b
y
co
n
s
id
er
in
g
lo
ad
b
alan
ci
n
g
.
J
an
a
an
d
P
o
r
a
y
[
3
1
]
u
s
ed
t
wo
p
o
p
u
lar
b
io
-
in
s
p
ir
ed
m
eta
-
h
eu
r
i
s
tics
al
g
o
r
ith
m
s
,
g
e
n
etic
an
d
P
SO
alg
o
r
ith
m
,
f
o
r
task
s
c
h
ed
u
lin
g
in
clo
u
d
co
m
p
u
ti
n
g
.
T
h
e
o
b
jectiv
e
is
to
r
ed
u
ce
th
e
r
esp
o
n
s
e
t
i
m
e
o
f
t
h
e
VM
.
T
h
e
au
th
o
r
s
u
s
ed
t
h
e
C
lo
u
d
Si
m
s
i
m
u
lato
r
to
s
i
m
u
late
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
a
n
d
ev
al
u
a
ted
w
it
h
Ma
x
-
Mi
n
an
d
m
i
n
i
m
u
m
e
x
ec
u
tio
n
t
i
m
e
alg
o
r
ith
m
.
T
h
e
r
esu
lt
s
s
h
o
w
ed
t
h
at
t
h
e
p
r
o
p
o
s
ed
alg
o
r
i
th
m
m
in
i
m
ize
s
t
h
e
w
ait
in
g
ti
m
e
a
n
d
r
esp
o
n
s
e
ti
m
e.
T
h
e
a
u
t
h
o
r
w
ill
co
n
s
id
er
d
y
n
a
m
ic
tas
k
allo
ca
tio
n
,
m
i
n
i
m
izatio
n
o
f
elec
tr
ic
co
s
t,
an
d
in
ter
n
a
l c
o
m
m
u
n
icat
io
n
s
i
n
to
ac
co
u
n
t i
n
t
h
e
n
ea
r
f
u
tu
r
e.
Ver
m
a
an
d
Ka
u
s
h
al
[
3
2
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
m
u
lti
-
o
b
j
ec
tiv
e
p
ar
ticle
s
w
ar
m
o
p
ti
m
iza
tio
n
(
HP
SO)
f
o
r
s
cien
t
if
ic
w
o
r
k
f
lo
w
s
c
h
ed
u
li
n
g
p
r
o
b
lem
s
i
n
th
e
I
aa
S
clo
u
d
en
v
ir
o
n
m
e
n
t.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
(
HP
SO)
u
s
ed
a
m
u
lti
-
o
b
j
ec
tiv
e
p
ar
ticle
s
w
ar
m
o
p
ti
m
iza
tio
n
a
lg
o
r
i
th
m
w
it
h
a
lis
t
-
b
ased
h
e
u
r
is
t
ic
i.e
.
,
b
u
d
g
et
a
n
d
d
ea
d
lin
e
co
n
s
tr
ai
n
ed
h
eter
o
g
e
n
eo
u
s
ea
r
lies
t
f
i
n
is
h
t
i
m
e
(
B
DHE
FT
)
.
T
h
e
f
it
n
e
s
s
f
u
n
ctio
n
w
as
co
m
p
o
s
ed
o
f
t
w
o
co
n
f
lic
tin
g
o
b
j
ec
tiv
es
m
a
k
esp
an
a
n
d
co
s
t
u
n
d
er
t
h
e
d
ea
d
lin
e
an
d
b
u
d
g
et
co
n
s
tr
ain
t
s
.
T
h
e
au
th
o
r
s
e
x
ten
d
th
e
f
u
n
ctio
n
al
it
y
o
f
C
lo
u
d
Si
m
f
o
r
w
o
r
k
f
lo
w
s
ch
ed
u
li
n
g
f
o
r
s
i
m
u
lat
io
n
an
d
an
al
y
s
is
o
f
r
es
u
lts
.
T
h
e
s
i
m
u
la
tio
n
r
esu
lt
s
s
h
o
w
ed
th
at
t
h
e
p
r
o
p
o
s
ed
HP
SO
co
n
v
er
g
ed
f
ast
a
n
d
h
as
a
u
n
i
f
o
r
m
s
p
ac
in
g
a
m
o
n
g
t
h
e
s
o
l
u
tio
n
s
co
m
p
ar
ed
w
ith
o
t
h
er
s
tate
-
of
-
ar
t
m
u
lt
i
-
o
b
j
ec
tiv
e
m
e
ta
-
h
e
u
r
i
s
tics
li
k
e
n
o
n
-
d
o
m
in
a
ted
s
o
r
t
g
en
et
ic
alg
o
r
it
h
m
-
II
(
NSG
A
-
II
)
,
m
u
lt
i
-
o
b
j
ec
tiv
e
P
SO
(
MO
P
SO
)
,
an
d
f
u
zz
y
d
o
m
in
an
ce
s
o
r
t
b
ased
d
is
cr
ete
P
SO
(
ϵ
-
FDP
SO
)
.
So
m
e
o
th
er
Qo
S
co
n
s
tr
ain
t
s
,
lik
e
r
el
iab
ilit
y
,
tr
u
s
t
m
a
n
a
g
e
m
e
n
t,
V
M
m
ig
r
atio
n
,
w
ill
b
e
co
n
s
id
er
ed
f
o
r
f
u
tu
r
e
w
o
r
k
.
C
o
n
ce
p
ts
li
k
e
n
e
u
r
al
n
et
w
o
r
k
s
,
an
d
f
u
zz
y
lo
g
ic
ca
n
b
e
test
ed
to
im
p
r
o
v
e
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
.
A
h
y
b
r
id
h
e
u
r
is
tic
b
ased
o
n
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
P
SO)
an
d
g
r
av
itat
io
n
s
ea
r
c
h
alg
o
r
ith
m
s
(
GSA
)
w
as
p
r
o
p
o
s
ed
in
[
3
3
]
f
o
r
w
o
r
k
f
lo
w
s
c
h
ed
u
li
n
g
in
t
h
e
clo
u
d
e
n
v
ir
o
n
m
e
n
t.
T
h
e
s
i
m
u
latio
n
w
as
d
o
n
e
u
s
i
n
g
th
e
C
lo
u
d
S
i
m
to
o
l
k
it
w
it
h
t
h
e
a
m
az
o
n
E
C
2
r
ef
er
e
n
cin
g
p
r
ice.
T
h
e
p
r
o
p
o
s
ed
al
g
o
r
ith
m
n
o
ti
f
ie
s
t
h
e
s
ig
n
i
f
ica
n
t
r
ed
u
ct
io
n
in
co
s
t
co
m
p
ar
ed
to
e
x
i
s
tin
g
n
o
n
-
h
e
u
r
is
tic,
P
SO,
an
d
GS
A
alg
o
r
i
th
m
u
n
d
er
d
ea
d
li
n
e
co
n
s
tr
ain
t.
On
e
ca
n
i
m
p
r
o
v
e
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
b
y
c
h
o
o
s
in
g
th
e
VM
n
u
m
b
er
ac
co
r
d
in
g
to
th
e
h
is
to
r
ical
d
ata
in
f
u
t
u
r
e
w
o
r
k
.
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.
12
,
No
.
1
,
Feb
r
u
ar
y
20
22
:
8
8
0
-
895
888
Au
t
h
o
r
s
p
r
o
p
o
s
ed
T
w
o
-
h
y
b
r
id
alg
o
r
it
h
m
s
in
[
3
4
]
n
a
m
ed
b
e
s
t f
i
t P
SO (
B
FP
SO)
a
n
d
P
SO
-
t
ab
u
s
ea
r
c
h
(
P
SOT
S)
.
T
h
e
b
asic
id
ea
o
f
B
FP
SO
w
as
to
in
it
ialize
th
e
P
S
O
u
s
i
n
g
t
h
e
B
est
f
it
alg
o
r
it
h
m
in
s
tead
o
f
r
an
d
o
m
v
alu
e
s
.
I
n
P
SOT
S,
th
e
lo
ca
l
s
ea
r
ch
ab
il
it
y
w
a
s
i
m
p
r
o
v
e
d
b
y
ap
p
l
y
T
ab
u
s
ea
r
ch
(
T
S
)
to
P
SO.
B
o
th
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
alg
o
r
ith
m
s
p
er
f
o
r
m
ed
b
etter
in
ter
m
s
o
f
m
ak
esp
an
,
co
s
t,
a
n
d
r
eso
u
r
ce
u
t
ilizatio
n
co
m
p
ar
ed
to
s
tan
d
ar
d
P
SO
w
h
e
n
s
i
m
u
la
ted
u
s
i
n
g
th
e
C
lo
u
d
S
i
m
to
o
lk
i
t.
T
h
e
au
th
o
r
s
p
lan
to
i
m
p
r
o
v
e
th
e
s
ta
n
d
ar
d
P
SO
u
s
i
n
g
o
th
er
g
r
ee
d
y
m
et
h
o
d
s
in
th
e
f
u
t
u
r
e.
Geo
r
g
e
[
3
5
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
p
ar
ticle
s
w
ar
m
o
p
ti
m
izat
i
o
n
,
an
d
m
u
l
ti
o
b
j
ec
tiv
e
b
at
alg
o
r
ith
m
(
P
SO
-
MO
B
A
)
a
lg
o
r
it
h
m
f
o
r
m
i
n
i
m
izi
n
g
th
e
p
r
o
f
i
t
u
s
i
n
g
r
eso
u
r
ce
s
c
h
ed
u
li
n
g
,
lo
w
p
o
w
er
co
n
s
u
m
p
tio
n
i
n
clo
u
d
c
o
m
p
u
ti
n
g
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
as
t
h
e
co
m
b
i
n
atio
n
o
f
P
SO
an
d
m
u
lti
-
o
b
j
ec
tiv
e
b
at
a
lg
o
r
it
h
m
(
MO
B
A
)
an
d
r
eso
l
v
ed
th
e
g
lo
b
al
co
n
v
er
g
e
n
ce
p
r
o
b
le
m
.
I
n
t
h
e
f
u
t
u
r
e,
o
th
er
f
ac
to
r
s
w
ill
b
e
in
cl
u
d
ed
to
r
ed
u
ce
th
e
co
s
t a
n
d
m
ax
i
m
ize
th
e
p
r
o
f
it.
4
.
2
.
H
y
brid us
ing
GA
T
h
e
alg
o
r
ith
m
h
y
b
r
id
elec
tr
o
s
ea
r
ch
w
it
h
a
g
en
e
tic
al
g
o
r
ith
m
(
HE
SG
A
)
w
as
p
r
o
p
o
s
ed
b
y
V
ellian
g
ir
i
et
a
l.
[
3
6
]
to
o
p
ti
m
ize
t
h
e
r
es
u
lts
i
n
ter
m
s
o
f
m
a
k
esp
a
n
,
e
x
ec
u
tio
n
ti
m
e,
a
n
d
co
s
t
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
tak
es
ad
v
a
n
tag
e
o
f
b
o
t
h
t
h
e
i
n
v
o
l
v
ed
al
g
o
r
ith
m
s
.
T
h
e
G
A
w
a
s
ad
eq
u
ate
to
ac
h
iev
e
th
e
lo
ca
l
o
p
ti
m
izatio
n
r
esu
lt
s
w
h
ile
t
h
e
elec
tr
o
s
ea
r
c
h
al
g
o
r
ith
m
b
est
ac
h
ie
v
ed
th
e
g
lo
b
al
r
esu
l
ts
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
i
s
test
ed
u
s
i
n
g
C
lo
u
d
Si
m
3
.
0
.
3
s
i
m
u
lat
o
r
an
d
f
i
n
d
s
th
at
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
o
u
tp
er
f
o
r
m
t
h
at
GA
,
E
S
,
AC
O,
a
n
d
h
y
b
r
id
p
ar
ticle
s
w
ar
m
o
p
ti
m
i
za
tio
n
g
en
et
ic
al
g
o
r
ith
m
(
HP
SOG
A
)
.
T
h
e
d
ep
en
d
en
c
y
o
f
task
s
a
n
d
s
cie
n
ti
f
ic
w
o
r
k
f
lo
w
w
ill
ev
al
u
ate
o
th
er
p
ar
a
m
eter
s
li
k
e
t
h
e
d
e
g
r
ee
o
f
i
m
b
ala
n
ce
a
n
d
e
n
er
g
y
e
f
f
ici
en
c
y
i
n
t
h
e
f
u
t
u
r
e
w
o
r
k
s
.
A
ziza
a
n
d
Kr
ich
e
n
[
3
7
]
co
n
s
i
d
e
r
th
e
d
ep
en
d
en
c
y
o
f
tas
k
s
a
n
d
p
r
o
p
o
s
ed
a
h
y
b
r
id
alg
o
r
ith
m
w
it
h
t
w
o
p
o
p
u
lar
Hete
r
o
g
en
eo
u
s
ea
r
lie
s
t
f
i
n
i
s
h
t
i
m
e
(
HE
FT
)
an
d
GA
al
g
o
r
ith
m
s
,
n
a
m
ed
h
y
b
r
id
o
f
HE
FT
an
d
GA
(
HE
FT
-
GA
)
.
T
h
e
i
n
itia
l
p
o
p
u
latio
n
o
f
G
A
is
in
itialized
u
s
i
n
g
t
h
e
r
es
u
lt
g
e
n
er
ated
b
y
t
h
e
HE
F
T
alg
o
r
ith
m
.
T
h
e
HE
FT
GA
ex
p
er
i
m
en
ted
w
it
h
r
ea
l
w
o
r
k
s
cie
n
ti
f
ic
w
o
r
k
f
lo
w
s
li
k
e
Mo
n
tag
e,
C
y
b
er
s
h
ak
e,
E
p
i
g
en
o
m
ics
,
L
aser
i
n
ter
f
er
o
m
eter
g
r
a
v
itati
o
n
al
-
w
a
v
e
o
b
s
er
v
ato
r
y
(
L
I
G
O
)
,
an
d
s
R
N
A
id
e
n
ti
f
icatio
n
p
r
o
to
co
l
u
s
in
g
h
i
g
h
-
th
r
o
u
g
h
p
u
t
tec
h
n
o
lo
g
ies
(
SIP
HT
)
to
o
p
tim
ize
th
e
r
e
s
u
l
ts
i
n
ter
m
s
o
f
e
x
ec
u
tio
n
ti
m
e
a
n
d
ex
ec
u
t
io
n
co
s
t
u
s
in
g
th
e
w
o
r
k
f
lo
w
s
i
m
s
i
m
u
lato
r
.
P
o
w
er
co
n
s
u
m
p
tio
n
w
ill b
e
co
n
s
id
er
ed
in
f
u
tu
r
e
w
o
r
k
.
T
h
e
Gen
etic
alg
o
r
ith
m
w
as
co
m
b
i
n
ed
w
ith
t
h
e
g
r
a
v
itatio
n
al
s
ea
r
ch
al
g
o
r
ith
m
to
o
v
er
co
m
e
t
h
e
d
r
a
w
b
ac
k
o
f
g
r
a
v
itat
io
n
al
s
e
ar
ch
b
y
s
to
r
in
g
t
h
e
b
est
p
ar
t
icle
p
o
s
itio
n
[
3
8
]
.
C
h
au
d
h
ar
y
a
n
d
Ku
m
ar
[
3
8
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
g
e
n
etic
-
g
r
a
v
itatio
n
al
s
ea
r
c
h
al
g
o
r
ith
m
(
HG
-
G
S
A
)
to
r
ed
u
ce
t
h
e
to
tal
co
s
t.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
r
ed
u
ce
s
t
h
e
to
tal
co
s
t
an
d
in
cr
ea
s
e
s
u
til
izatio
n
co
m
p
ar
ed
w
it
h
P
SO,
C
lo
u
d
-
G
S
A
,
an
d
lin
ea
r
i
m
p
r
o
v
ed
GS
A
(
L
I
GS
A
-
C
)
ap
p
r
o
ac
h
es.
T
h
e
s
i
m
u
la
to
r
u
s
e
d
w
as
C
lo
u
d
Si
m
3
.
0
.
3
.
I
n
th
e
f
u
tu
r
e,
t
h
e
au
t
h
o
r
s
w
il
l
f
o
c
u
s
o
n
t
h
e
n
e
w
h
y
b
r
id
alg
o
r
ith
m
b
ased
o
n
t
h
e
b
io
-
i
n
s
p
ir
ed
h
eu
r
is
t
ics.
T
h
e
au
t
h
o
r
s
w
il
l
also
w
o
r
k
o
n
th
e
co
n
ce
p
t
s
b
ased
o
n
t
h
e
ce
n
t
er
o
f
m
a
s
s
-
b
ased
cr
o
s
s
o
v
er
an
d
d
iv
er
s
it
y
-
b
ased
cr
o
s
s
o
v
er
te
ch
n
iq
u
es
to
r
ed
u
ce
co
s
ts
in
t
h
e
f
u
t
u
r
e.
Nate
s
an
a
n
d
C
h
o
k
k
ali
n
g
a
m
[
3
9
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
m
u
l
ti
-
o
b
j
ec
tiv
e
ta
s
k
s
c
h
ed
u
li
n
g
alg
o
r
ith
m
co
m
b
i
n
i
n
g
w
h
ale
o
p
ti
m
izatio
n
al
g
o
r
ith
m
(
W
O
A
)
w
it
h
G
A
to
o
p
ti
m
ize
m
a
k
esp
a
n
an
d
co
s
t,
en
ac
t
m
en
t
a
m
elio
r
atio
n
r
ate
(
E
AR
)
.
T
h
e
b
asic
id
ea
w
as
f
ir
s
t
to
u
s
e
W
OA
a
n
d
u
p
d
ate
th
e
w
o
r
s
t
ch
r
o
m
o
s
o
m
e
u
s
i
n
g
t
h
e
GA
al
g
o
r
ith
m
's
o
p
er
atio
n
s
li
k
e
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
,
n
a
m
ed
W
h
ale
Gen
etic
Op
t
i
m
izatio
n
A
l
g
o
r
ith
m
,
w
as
s
i
m
u
lated
u
s
i
n
g
a
C
lo
u
d
Si
m
s
i
m
u
lato
r
an
d
f
o
u
n
d
t
h
at
it
g
a
v
e
o
p
tim
ized
r
esu
lt
s
in
co
m
p
ar
is
o
n
to
o
th
er
s
tan
d
ar
d
alg
o
r
ith
m
s
li
k
e
f
ir
s
t
co
m
e
f
ir
s
t
s
er
v
e,
Mi
n
-
Min
,
Ma
x
-
Min
alg
o
r
it
h
m
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
as
also
p
er
f
o
r
m
ed
b
etter
i
n
c
o
m
p
ar
is
o
n
to
s
ta
n
d
alo
n
e
G
A
a
n
d
W
O
A
.
T
h
e
au
th
o
r
s
w
i
ll
co
n
s
id
er
o
th
er
p
ar
am
e
ter
s
li
k
e
en
er
g
y
co
n
s
u
m
p
t
io
n
,
s
ec
u
r
it
y
,
an
d
r
eliab
ilit
y
in
t
h
e
n
ea
r
f
u
t
u
r
e.
Sric
h
a
n
d
an
et
a
l.
[
4
0
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
m
u
lti
-
o
b
j
ec
tiv
e
g
en
etic
an
d
b
ac
ter
ial
f
o
r
ag
i
n
g
alg
o
r
ith
m
n
a
m
ed
M
HB
F
A
,
f
o
r
task
s
c
h
ed
u
li
n
g
in
clo
u
d
co
m
p
u
ti
n
g
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ta
k
es
ac
co
u
n
t
o
f
t
h
e
ad
v
an
ta
g
es
a
n
d
d
is
ad
v
a
n
ta
g
e
s
o
f
t
h
e
in
v
o
l
v
ed
alg
o
r
ith
m
s
.
T
h
e
g
en
etic
a
lg
o
r
it
h
m
h
a
s
lo
w
lo
ca
l
s
ea
r
ch
ca
p
ab
ilit
y
b
u
t
e
x
ce
lle
n
t
i
n
g
l
o
b
al
s
ea
r
ch
w
h
ile
b
ac
ter
ial
f
o
r
ag
in
g
w
as
g
o
o
d
in
lo
ca
l
s
ea
r
ch
b
u
t
d
ef
icien
t
i
n
g
lo
b
al
s
ea
r
ch
.
T
h
e
t
w
o
o
b
j
ec
tiv
es,
m
ak
e
s
p
an
,
an
d
en
er
g
y
,
wer
e
co
n
s
id
er
ed
f
o
r
t
h
e
m
in
im
i
z
a
t
i
o
n
o
f
t
h
e
f
i
tn
es
s
f
u
n
c
t
i
o
n
.
T
h
e
s
im
u
la
t
i
o
n
ex
p
e
r
im
en
t
s
w
e
r
e
c
a
r
r
i
e
d
o
u
t
u
s
in
g
M
a
tl
a
b
R
2
0
1
3
a
.
T
h
e
p
r
o
p
o
s
e
d
a
l
g
o
r
ith
m
m
in
im
i
z
es
th
e
m
ak
e
s
p
an
an
d
r
e
d
u
c
es
en
e
r
g
y
c
o
n
s
u
m
p
ti
o
n
in
c
o
m
p
a
r
is
o
n
w
ith
GA
,
PS
O
,
b
ac
ter
ial
f
o
r
ag
i
n
g
alg
o
r
ith
m
(
B
FA
)
.
T
h
e
s
a
m
e
w
o
r
k
ca
n
b
e
ca
r
r
ied
o
u
t
u
n
d
e
r
d
e
p
e
n
d
e
n
t
t
a
s
k
s
.
T
h
e
f
u
r
t
h
e
r
i
m
p
r
o
v
e
m
e
n
t
i
n
t
h
e
p
r
o
p
o
s
e
d
a
l
g
o
r
i
t
h
m
'
s
c
o
n
v
e
r
g
e
n
c
e
r
a
t
e
w
a
s
p
o
s
s
i
b
l
e
b
y
r
e
d
u
c
i
n
g
e
x
t
r
a
t
i
m
i
n
g
t
a
k
e
n
b
y
t
h
e
c
r
o
s
s
o
v
e
r
a
n
d
m
u
t
a
t
i
o
n
o
p
e
r
a
t
i
o
n
s
i
n
t
h
e
f
u
t
u
r
e.
Ma
n
asra
h
an
d
A
li
[
4
1
]
u
s
ed
th
e
t
w
o
m
o
s
t
p
o
p
u
lar
b
io
-
in
s
p
ir
ed
alg
o
r
ith
m
s
,
th
e
g
en
e
tic
alg
o
r
ith
m
a
n
d
th
e
p
ar
ticle
s
w
ar
m
o
p
ti
m
izati
o
n
alg
o
r
it
h
m
,
to
p
r
o
p
o
s
e
a
n
e
w
h
y
b
r
id
G
A
-
P
SO
a
lg
o
r
it
h
m
f
o
r
w
o
r
k
s
f
lo
w
s
ch
ed
u
lin
g
i
n
clo
u
d
co
m
p
u
tin
g
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
ta
r
ted
w
it
h
th
e
r
a
n
d
o
m
p
o
p
u
lati
o
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,
th
e
n
ap
p
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th
e
g
en
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al
g
o
r
ith
m
f
o
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th
e
f
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h
al
f
o
f
th
e
iter
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n
.
T
h
e
s
o
l
u
tio
n
s
g
en
er
ated
b
y
th
e
g
e
n
et
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alg
o
r
it
h
m
w
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in
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a
s
t
h
e
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Fo
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f
th
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iter
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P
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w
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s
r
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t
o
cr
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th
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ter
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o
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f
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g
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h
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task
s
'
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ar
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r
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L
o
h
e
s
w
ar
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a
n
d
P
r
em
alat
h
a
[
4
2
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
alg
o
r
ith
m
co
m
b
in
in
g
th
e
g
en
et
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o
r
ith
m
(
GA
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an
d
i
n
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o
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A
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ith
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t
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at
th
e
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p
o
s
ed
alg
o
r
ith
m
lo
w
er
s
t
h
e
a
v
er
ag
e
s
c
h
ed
u
le
le
n
g
t
h
co
m
p
ar
ed
to
o
th
er
ex
is
tin
g
al
g
o
r
ith
m
s
.
An
o
th
er
h
y
b
r
id
e
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o
lu
t
io
n
ar
y
w
o
r
k
f
lo
w
s
ch
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li
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g
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o
r
it
h
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n
a
m
ed
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e
w
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e
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r
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s
t
f
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n
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h
ti
m
e
(
L
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w
as
p
r
o
p
o
s
ed
b
y
Na
s
o
n
o
v
et
a
l.
[
4
3
]
.
T
h
e
p
r
o
p
o
s
e
d
alg
o
r
ith
m
u
s
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e
HE
FT
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d
G
A
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g
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h
m
's
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est ch
ar
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ter
is
tics
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n
d
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s
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n
alter
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o
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t
w
a
s
m
a
k
esp
a
n
.
Si
m
u
la
tio
n
e
x
p
er
i
m
e
n
t
r
esu
lts
i
n
co
m
p
u
ta
tio
n
al
en
v
ir
o
n
m
e
n
t
s
i
m
u
lato
r
s
h
o
w
t
h
at
t
h
e
p
r
o
p
o
s
ed
alg
o
r
it
h
m
w
o
r
k
s
b
etter
th
an
t
h
e
tr
ad
itio
n
al
HE
FT
alg
o
r
ith
m
i
n
a
d
y
n
a
m
ic
h
eter
o
g
e
n
eo
u
s
d
i
s
tr
ib
u
ted
co
m
p
u
tat
io
n
al
e
n
v
ir
o
n
m
en
t
.
T
h
e
w
o
r
k
ca
n
b
e
ex
ten
d
ed
to
a
m
u
lti
-
h
e
u
r
is
tics
s
ch
e
m
e
w
h
er
e
d
if
f
er
e
n
t
h
eu
r
i
s
tics
w
ill b
e
u
s
ed
p
ar
allell
y
in
t
o
u
r
n
a
m
en
t
m
o
d
e
f
o
r
b
etter
r
esu
lts
.
Ka
m
ali
n
ia
an
d
Gh
a
f
f
ar
i
[
1
1
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
m
eta
-
h
eu
r
i
s
tic
ta
s
k
s
c
h
ed
u
l
in
g
m
et
h
o
d
c
o
m
b
i
n
i
n
g
th
e
Gen
e
tic
an
d
d
if
f
er
en
tial
ev
o
lu
tio
n
(
DE
)
alg
o
r
it
h
m
s
.
T
h
e
p
r
o
p
o
s
ed
n
o
v
el
h
y
b
r
id
g
e
n
etic
alo
n
g
w
it
h
t
h
e
DE
alg
o
r
ith
m
.
T
h
e
b
asic
id
ea
w
as
to
ap
p
ly
G
A
,
a
n
d
t
h
e
s
o
lu
tio
n
g
e
n
er
ated
b
y
G
A
w
as
t
h
e
i
n
itial
p
o
p
u
latio
n
o
f
th
e
DE
al
g
o
r
ith
m
.
T
h
e
s
i
m
u
lat
io
n
w
as
d
o
n
e
u
s
in
g
Vi
s
u
a
l
St
u
d
io
2
0
1
3
an
d
th
e
C
#
.
n
e
t
p
r
o
g
r
a
m
m
in
g
la
n
g
u
a
g
e.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
i
m
p
r
o
v
es
r
eso
u
r
ce
e
f
f
icien
c
y
an
d
m
i
n
i
m
izes
th
e
m
ak
e
s
p
an
co
m
p
ar
ed
w
it
h
HE
FT
(
Up
R
an
k
,
Do
w
n
R
a
n
k
,
an
d
L
e
v
elR
a
n
k
)
an
d
B
in
ar
y
g
e
n
etic
alg
o
r
ith
m
(
B
GA
)
.
T
h
e
p
r
o
p
o
s
ed
n
o
v
el
ap
p
r
o
ac
h
also
r
ed
u
ce
s
t
h
e
cr
itical
p
at
h
an
d
r
ed
u
ce
co
m
m
u
n
icatio
n
co
s
t
a
m
o
n
g
t
h
e
p
r
o
ce
s
s
o
r
s
.
I
n
f
u
t
u
r
e
w
o
r
k
,
th
e
h
y
b
r
id
izatio
n
ca
n
b
e
d
o
n
e
u
s
i
n
g
o
th
er
m
eta
-
h
eu
r
i
s
tic
a
lg
o
r
it
h
m
s
,
a
n
d
t
h
e
p
er
f
o
r
m
a
n
ce
ca
n
b
e
an
al
y
ze
d
u
s
i
n
g
s
i
m
u
lat
io
n
r
es
u
lts
.
Ah
m
ad
et
a
l.
[
4
4
]
u
s
ed
HE
F
T
g
en
er
ated
s
o
lu
t
io
n
s
to
th
e
i
n
itial
v
al
u
e
o
f
G
A
to
p
r
o
p
o
s
e
a
h
y
b
r
id
g
en
et
ic
al
g
o
r
ith
m
f
o
r
w
o
r
k
f
lo
w
s
ch
ed
u
li
n
g
in
clo
u
d
co
m
p
u
tin
g
.
T
h
e
o
b
j
ec
tiv
e
w
a
s
to
r
ed
u
ce
t
h
e
m
ak
e
s
p
an
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
as
test
ed
ag
ai
n
s
t
h
eu
r
i
s
tics
al
g
o
r
ith
m
s
lik
e
Mo
d
i
f
ied
cr
itica
l
p
ath
(
MCP
)
an
d
HE
FT
,
a
g
en
er
ic
ev
o
lu
tio
n
ar
y
a
lg
o
r
it
h
m
(
P
E
G
A
)
,
an
d
r
ec
en
tl
y
p
r
o
p
o
s
ed
h
y
b
r
id
g
en
e
tic
alg
o
r
ith
m
s
lik
e
m
u
ltip
le
p
r
io
r
it
y
q
u
eu
e
s
g
e
n
etic
alg
o
r
it
h
m
(
MP
QG
A
)
a
n
d
h
y
b
r
id
s
u
cc
es
s
o
r
co
n
ce
r
n
e
d
h
eu
r
i
s
tics
g
e
n
etic
s
ch
ed
u
lin
g
(
HS
C
G
S
)
.
T
h
e
r
esu
lt
s
s
h
o
w
ed
i
m
p
r
o
v
e
m
e
n
t
i
n
av
er
a
g
e
s
c
h
ed
u
le
len
g
t
h
,
l
o
ad
b
alan
cin
g
,
a
n
d
co
m
m
u
n
icatio
n
to
co
m
p
u
ta
tio
n
r
atio
.
I
n
t
h
e
f
u
tu
r
e,
a
u
th
o
r
s
tr
y
to
o
p
ti
m
ize
th
e
s
ch
ed
u
li
n
g
u
s
i
n
g
m
o
r
e
d
ata
-
in
te
n
s
i
v
e
an
d
co
m
p
le
x
w
o
r
k
f
l
o
w
s
li
k
e
r
ea
l
-
w
o
r
ld
s
cie
n
ti
f
ic
w
o
r
k
f
lo
w
s
.
Dela
v
a
a
n
d
A
r
y
a
n
[
4
5
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
h
e
u
r
is
tic
a
lg
o
r
ith
m
(
HS
G
A
)
f
o
r
f
i
n
d
in
g
t
h
e
o
p
ti
m
al
s
o
lu
tio
n
in
ter
m
s
o
f
m
a
k
esp
a
n
an
d
lo
ad
b
alan
cin
g
o
f
w
o
r
k
f
l
o
w
s
c
h
ed
u
li
n
g
in
a
clo
u
d
co
m
p
u
tin
g
e
n
v
ir
o
n
m
e
n
t.
T
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
alg
o
r
ith
m
u
s
ed
B
est
-
Fi
t
an
d
R
o
u
n
d
R
o
b
in
alg
o
r
ith
m
s
to
o
b
tain
th
e
g
en
et
ic
alg
o
r
it
h
m
's
in
itial
p
o
p
u
latio
n
.
HSG
A
p
r
o
d
u
ce
d
b
etter
r
esu
lts
in
co
m
p
ar
is
o
n
to
v
ar
ian
t
s
o
f
t
h
e
G
A
alg
o
r
ith
m
w
it
h
an
in
cr
ea
s
i
n
g
n
u
m
b
er
o
f
ta
s
k
s
.
4
.
3
.
H
y
brid us
ing
ACO
Kau
r
a
n
d
Ka
u
r
[
4
6
]
d
ev
elo
p
ed
a
VM
lo
ad
b
alan
cin
g
f
r
am
e
w
o
r
k
n
a
m
ed
H
y
b
r
id
ap
p
r
o
ac
h
b
ased
Dea
d
lin
e
co
n
s
tr
ai
n
ed
,
d
y
n
a
m
i
c
VM
p
r
o
v
is
io
n
i
n
g
,
a
n
d
lo
ad
b
alan
cin
g
(
HDD
-
P
L
B
)
.
T
h
e
HDD
-
P
L
B
w
as
u
s
ed
f
o
r
ev
al
u
ati
n
g
t
h
e
t
w
o
p
r
o
p
o
s
ed
h
y
b
r
id
alg
o
r
ith
m
s
ca
lled
p
r
ed
ict
ea
r
lies
t
f
i
n
i
s
h
t
i
m
e
-
AC
O
(
P
E
F
T
-
AC
O
)
an
d
h
eter
o
g
e
n
eo
u
s
ea
r
lies
t
f
i
n
is
h
ti
m
e
-
AC
O
(
HE
FT
-
A
C
O
)
us
i
n
g
C
lo
u
d
W
o
r
k
f
lo
w
Si
m
u
lat
o
r
.
P
E
F
T
-
AC
O
w
as
th
e
h
y
b
r
id
izatio
n
o
f
p
r
ed
ict
ea
r
lies
t
f
in
i
s
h
ti
m
e
(
P
E
FT
)
h
eu
r
is
tics
a
n
d
an
t
co
lo
n
y
o
p
ti
m
izati
o
n
(
AC
O)
.
HE
FT
-
AC
O
w
as
th
e
h
y
b
r
id
izatio
n
o
f
Hete
r
o
g
en
eo
u
s
E
ar
lies
t
Fin
i
s
h
T
i
m
e
(
P
E
FT
)
h
eu
r
is
ti
cs
an
d
an
t
co
lo
n
y
o
p
tim
iza
tio
n
(
A
C
O)
.
T
h
e
o
b
j
e
ctiv
es
i
n
cl
u
d
e
m
ak
e
s
p
an
a
n
d
co
s
t.
T
h
e
r
esu
lts
s
h
o
w
t
h
at
th
e
P
E
FT
-
AC
O
g
i
v
e
s
o
p
tim
a
l r
esu
l
ts
i
n
C
y
p
er
Sh
a
k
e
an
d
L
I
GO
w
o
r
k
f
lo
w
.
T
h
e
an
t
co
lo
n
y
o
p
ti
m
iza
tio
n
(
AC
O)
alg
o
r
it
h
m
w
a
s
co
m
b
i
n
ed
w
ith
a
g
en
e
tic
alg
o
r
it
h
m
in
[
4
7
]
f
o
r
s
o
lv
i
n
g
t
h
e
tas
k
s
ch
ed
u
li
n
g
p
r
o
b
lem
i
n
clo
u
d
co
m
p
u
ti
n
g
to
p
r
o
p
o
s
e
a
g
en
etic
-
a
n
t
-
co
lo
n
y
h
y
b
r
id
Alg
o
r
it
h
m
.
T
h
e
b
asic
id
ea
w
as
to
in
i
tializ
ed
th
e
p
h
er
o
m
o
n
e
i
n
AC
O
w
it
h
t
h
e
b
est
ch
r
o
m
o
s
o
m
e
g
e
n
er
ate
b
y
G
A
.
T
h
e
ai
m
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
was
lo
ad
b
alan
cin
g
a
n
d
o
p
ti
m
al
ti
m
e
s
p
an
.
T
h
e
s
i
m
u
latio
n
w
a
s
d
o
n
e
u
s
i
n
g
th
e
C
lo
u
d
Si
m
s
i
m
u
lato
r
an
d
f
o
u
n
d
th
at
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
p
r
o
d
u
ce
d
g
o
o
d
r
esu
lts
in
t
er
m
s
o
f
ac
co
u
n
ted
o
b
j
ec
tiv
es
co
m
p
ar
ed
to
s
tan
d
a
r
d
GA
an
d
A
C
O.
I
n
th
e
f
u
tu
r
e,
th
e
s
a
m
e
ex
p
er
i
m
e
n
t
s
w
ill
b
e
co
n
d
u
cted
in
th
e
r
ea
l c
lo
u
d
en
v
ir
o
n
m
e
n
t to
f
o
r
m
m
o
r
e
p
r
ac
tical
r
esu
l
ts
.
Gh
u
m
m
a
n
an
d
Ka
u
r
[
4
8
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
s
ch
ed
u
li
n
g
alg
o
r
ith
m
u
s
i
n
g
i
m
p
r
o
v
ed
m
ax
-
m
i
n
a
n
d
AC
O
alg
o
r
it
h
m
f
o
r
lo
ad
b
al
an
cin
g
i
n
clo
u
d
co
m
p
u
ti
n
g
.
T
h
e
o
b
j
ec
tiv
e
w
as
to
m
i
n
i
m
ize
th
e
m
a
k
esp
a
n
.
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