I
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3
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2
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20
2
5
,
p
p
.
1
195
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1
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7
I
SS
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1
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1
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cs
.v
3
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.
i
2
.
pp
1
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1195
J
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O
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Th
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p
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s
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t
c
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o
ries
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f
o
r
o
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ti
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izin
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th
e
tas
k
s
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li
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u
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Hy
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iza
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g
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m
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se
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ly
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ize
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O),
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se
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m
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risti
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ly
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b
e
lu
g
a
wh
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le
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ti
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iza
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(BWO),
y
ield
s
t
o
t
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e
e
v
o
l
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ti
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o
f
a
n
e
w
a
lg
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rit
h
m
k
n
o
w
n
a
s
“
h
y
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ri
d
d
a
rts
g
a
m
e
h
y
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th
e
sis
–
b
e
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g
a
wh
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le
o
p
ti
m
iza
ti
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n
”
(
h
y
b
rid
DG
H
-
BW
O)
a
lg
o
ri
th
m
.
Tas
k
sc
h
e
d
u
li
n
g
o
p
ti
m
iza
ti
o
n
in
c
lo
u
d
e
n
v
ir
o
n
m
e
n
t
is
a
c
rit
i
c
a
l
p
ro
c
e
ss
a
n
d
is
d
e
term
in
e
d
a
s
a
non
-
d
e
term
in
isti
c
p
o
ly
n
o
m
ial
(
NP
)
-
h
a
rd
p
ro
b
lem
.
M
e
tah
e
u
r
isti
c
tec
h
n
iq
u
e
s
a
re
h
ig
h
-
lev
e
l
o
p
t
imiz
a
ti
o
n
a
l
g
o
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it
h
m
s,
d
e
sig
n
e
d
to
so
l
v
e
a
wid
e
ra
n
g
e
o
f
c
o
m
p
lex
,
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ti
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iza
ti
o
n
p
ro
b
lem
s.
In
t
h
e
h
y
b
rid
i
z
a
ti
o
n
o
f
DG
O
a
n
d
BWO
m
e
tah
e
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risti
c
a
lg
o
rit
h
m
s,
e
x
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i
ti
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n
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a
p
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h
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m
s
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re
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o
m
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i
n
e
d
to
g
e
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e
r,
a
n
d
t
h
is
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n
h
a
n
c
e
s
t
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c
h
a
n
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e
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o
f
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n
d
i
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g
th
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q
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li
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io
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d
to
u
sin
g
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si
n
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le
a
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m
a
l
o
n
e
.
Ot
h
e
r
b
e
n
e
fi
ts
o
f
th
e
p
r
o
p
o
se
d
a
lg
o
rit
h
m
:
in
c
re
a
se
d
o
v
e
ra
ll
e
fficie
n
c
y
,
a
s
“
h
y
b
rid
DGH
-
BWO”
a
lg
o
rit
h
m
c
a
n
e
x
p
l
o
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t
th
e
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o
m
p
lem
e
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tary
stre
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g
th
s
o
f
b
o
th
DG
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a
n
d
BWO
a
lg
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h
m
s
t
o
c
o
n
v
e
rg
e
to
o
p
ti
m
a
l
so
lu
ti
o
n
s
m
o
re
q
u
ic
k
ly
.
Wi
d
e
ra
n
g
e
o
f
d
i
v
e
rsity
is
a
lso
i
n
tro
d
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c
e
d
in
t
h
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se
a
rc
h
sp
a
c
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n
d
th
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s
h
e
lp
s
in
a
v
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id
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tt
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trap
p
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d
i
n
l
o
c
a
l
o
p
ti
m
a
.
K
ey
w
o
r
d
s
:
C
lo
u
d
co
m
p
u
tin
g
Me
tah
eu
r
is
tics
Op
tim
izatio
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Swar
m
-
b
ased
o
p
tim
izatio
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T
ask
s
ch
ed
u
lin
g
T
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is i
s
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n
o
p
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n
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c
c
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ss
a
rticle
u
n
d
e
r th
e
CC B
Y
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SA
li
c
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se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ma
n
is
h
C
h
h
ab
r
a
Sch
o
o
l o
f
C
o
m
p
u
tin
g
Scien
ce
an
d
E
n
g
in
ee
r
in
g
,
Galg
o
tias
Un
iv
er
s
ity
Gr
ea
ter
No
id
a,
I
n
d
ia
E
m
ail:
m
an
is
h
ch
ab
r
a
7
7
@
g
m
a
il.c
o
m
1.
I
NT
RO
D
UCT
I
O
N
R
ec
en
tly
,
clo
u
d
tech
n
o
lo
g
y
b
e
co
m
es
th
e
f
o
u
n
d
atio
n
f
o
r
m
a
n
y
o
r
g
a
n
izatio
n
s
as
well
as
in
d
i
v
id
u
als
as
it
p
r
o
v
id
es
a
c
o
n
v
e
n
ien
t
way
to
ac
ce
s
s
clo
u
d
r
eso
u
r
ce
s
lik
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n
u
m
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u
s
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to
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tem
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etwo
r
k
s
(
W
ANs),
ap
p
licatio
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s
an
d
s
er
v
ices
r
em
o
tely
t
h
r
o
u
g
h
th
e
i
n
ter
n
et
with
o
u
t
in
s
tallin
g
an
d
m
ain
tain
in
g
th
e
m
o
n
-
p
r
e
m
is
es.
C
lo
u
d
tech
n
o
lo
g
y
th
r
e
e
s
tan
d
ar
d
s
ar
e:
in
f
r
astru
ctu
r
e
as
a
s
er
v
ice
(
I
aa
S),
p
latf
o
r
m
as
a
s
er
v
ice
(
PaaS)
,
s
o
f
twar
e
as
a
s
er
v
ice
(
SaaS)
[
1
]
.
Fiv
e
k
ey
c
h
ar
ac
ter
is
tics
ar
e:
o
n
-
d
e
m
an
d
s
elf
-
s
er
v
ice,
b
r
o
ad
n
etwo
r
k
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ce
s
s
,
r
eso
u
r
ce
p
o
o
lin
g
,
r
a
p
id
elasticity
,
an
d
m
ea
s
u
r
ed
s
er
v
ice
[
1
]
.
Fo
u
r
d
ep
lo
y
m
en
t
m
o
d
els ar
e
: p
u
b
lic
clo
u
d
,
p
r
iv
ate
clo
u
d
,
c
o
m
m
u
n
ity
clo
u
d
,
h
y
b
r
id
clo
u
d
[
1
]
.
Key
f
ea
tu
r
e
o
f
clo
u
d
e
n
v
ir
o
n
m
en
t
is
to
s
er
v
e
ten
s
o
f
t
h
o
u
s
an
d
s
a
n
d
m
o
r
e
o
f
u
s
er
s
r
eq
u
ests
co
n
cu
r
r
en
tly
,
w
h
ich
r
e
q
u
ir
e
d
a
n
ef
f
icien
t
task
s
ch
ed
u
lin
g
alg
o
r
ith
m
[
2
]
.
Ho
wev
er
,
in
m
ajo
r
i
ty
,
d
u
e
to
im
p
r
o
p
er
s
ch
ed
u
lin
g
,
r
eso
u
r
ce
s
a
r
e
eith
e
r
u
n
d
er
u
tili
ze
d
o
r
o
v
er
u
tili
ze
d
wh
ich
in
cr
ea
s
es th
e
cl
o
u
d
r
eso
u
r
ce
s
wastag
e
an
d
th
u
s
d
ec
lin
e
in
ef
f
icien
cy
.
Fo
r
ef
f
icien
t
u
s
ag
e
o
f
clo
u
d
r
eso
u
r
ce
s
,
th
er
e
ar
e
v
ar
io
u
s
av
ailab
le
s
ch
ed
u
lin
g
m
o
d
els
an
d
o
p
tim
izatio
n
cr
iter
ia.
Nu
m
er
o
u
s
class
ic,
d
eter
m
in
is
tic
alg
o
r
ith
m
s
ar
e
a
v
ailab
le
f
o
r
s
ch
ed
u
l
in
g
u
s
er
r
eq
u
ests
.
Fo
r
ex
am
p
le,
p
r
io
r
ity
s
ch
ed
u
lin
g
,
f
ir
s
t
-
co
m
e
f
ir
s
t
-
s
er
v
e
(
FC
FS
)
s
ch
ed
u
lin
g
[
3
]
,
a
n
d
r
o
u
n
d
r
o
b
i
n
(
R
R
)
s
ch
ed
u
lin
g
[
3
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
1
1
9
5
-
1
2
0
7
1196
alg
o
r
ith
m
s
.
B
u
t
in
clo
u
d
c
o
m
p
u
tin
g
e
n
v
ir
o
n
m
en
t,
s
ch
e
d
u
li
n
g
p
r
o
b
lem
is
estab
lis
h
ed
as
a
n
o
n
-
d
eter
m
in
is
tic
p
o
ly
n
o
m
ial
tim
e
(
NP)
h
ar
d
p
r
o
b
lem
[
4
]
,
[
5
]
,
s
o
tr
ad
itio
n
al
class
ic
s
ch
ed
u
lin
g
alg
o
r
ith
m
s
ar
e
u
n
ab
le
to
s
o
lv
e
clo
u
d
co
m
p
u
tin
g
p
r
o
b
lem
s
.
T
h
er
ef
o
r
e
,
v
ar
io
u
s
h
eu
r
is
tic
as
well
as
m
etah
eu
r
is
tic
-
b
ased
s
c
h
ed
u
lin
g
tec
h
n
iq
u
es
ar
e
wid
ely
u
s
ed
in
s
o
lv
in
g
clo
u
d
o
p
tim
izatio
n
p
r
o
b
lem
[
6
]
.
T
h
er
e
ar
e
n
u
m
e
r
o
u
s
h
eu
r
is
tic
an
d
m
eta
-
h
eu
r
is
tic
tech
n
iq
u
es,
b
u
t
m
eta
h
eu
r
is
tic
m
eth
o
d
s
ar
e
ex
ten
s
iv
ely
e
m
p
l
o
y
ed
in
r
eso
l
v
in
g
r
ea
l
-
tim
e
o
p
t
im
izatio
n
p
r
o
b
le
m
s
.
m
eta
-
h
eu
r
is
tic
alg
o
r
ith
m
s
ar
e
class
if
ied
in
to
f
o
u
r
ca
teg
o
r
ies.
T
h
ese
ar
e:
p
h
y
s
ics
-
b
ased
alg
o
r
ith
m
s
:
th
ey
r
ely
o
n
th
e
laws
o
f
n
atu
r
e,
s
u
ch
as
b
la
ck
h
o
les,
g
alax
ies,
an
d
g
r
av
itat
io
n
laws.
Fo
r
ex
am
p
le,
b
lac
k
h
o
le
(
B
H
)
a
lg
o
r
ith
m
[
7
]
,
g
r
a
v
itatio
n
al
s
ea
r
ch
alg
o
r
i
th
m
(
GSA)
[
8
]
.
Swar
m
-
b
ased
o
r
s
war
m
in
tellig
en
ce
alg
o
r
ith
m
s
:
th
ese
ar
e
n
atu
r
e
in
s
p
ir
ed
,
p
o
p
u
latio
n
d
ep
e
n
d
en
t
alg
o
r
ith
m
s
.
T
h
e
y
ar
e
estab
lis
h
ed
o
n
th
e
in
te
r
ac
tio
n
b
etwe
en
liv
in
g
o
r
g
a
n
is
m
s
s
u
ch
as
a
g
r
o
u
p
o
f
b
ir
d
s
,
a
s
ch
o
o
l
o
f
f
is
h
es,
an
d
a
co
l
o
n
y
o
f
an
ts
.
Fo
r
ex
a
m
p
le,
b
elu
g
a
wh
ale
o
p
tim
izatio
n
(
B
W
O)
alg
o
r
ith
m
[
9
]
,
an
t
co
lo
n
y
o
p
tim
izatio
n
(
AC
O)
[
1
0
]
,
p
ar
ticle
s
war
m
o
p
tim
iz
atio
n
(
PS
O)
[
1
1
]
.
E
v
o
lu
tio
n
a
r
y
alg
o
r
ith
m
s
:
T
h
e
y
r
ely
o
n
t
h
e
p
r
o
ce
s
s
o
f
n
atu
r
al
s
elec
tio
n
.
Ov
er
all,
a
n
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
co
n
tain
s
f
o
u
r
s
tep
s
:
in
itializatio
n
,
s
elec
tio
n
,
g
en
etic
o
p
er
ato
r
s
an
d
ter
m
in
atio
n
.
Fo
r
ex
am
p
l
e,
g
en
etic
alg
o
r
ith
m
(
GA)
[
1
2
]
,
d
if
f
er
e
n
tial
ev
o
lu
t
io
n
(
DE
)
[
1
3
]
.
Gam
e
-
b
ased
alg
o
r
ith
m
s
:
alg
o
r
ith
m
s
r
ely
o
n
g
am
es
u
s
es
two
s
tr
ateg
ies.
Fir
s
t,
m
o
d
ellin
g
th
e
g
am
e
r
u
le
s
an
d
s
ec
o
n
d
ly
,
th
e
p
lay
er
’
s
d
if
f
er
en
t
b
eh
a
v
io
r
.
Fo
r
ex
am
p
le,
d
ar
ts
g
am
e
o
p
tim
izer
(
DGO)
[
1
4
]
,
h
id
e
o
b
jects g
am
e
o
p
tim
izatio
n
(
HOGO
)
[
1
5
]
.
Pro
b
lem
s
tatem
en
t:
ef
f
icien
t
ta
s
k
s
ch
ed
u
lin
g
is
cr
itical
b
u
t
ch
allen
g
in
g
d
u
e
to
th
e
co
m
p
lex
ity
o
f
clo
u
d
en
v
ir
o
n
m
en
ts
.
T
r
ad
itio
n
al
d
et
er
m
in
is
tic
alg
o
r
ith
m
s
,
ar
e
i
n
s
u
f
f
icien
t
b
ec
a
u
s
e
clo
u
d
s
ch
e
d
u
lin
g
is
a
NP
-
h
ar
d
p
r
o
b
lem
,
lead
in
g
t
o
th
e
n
e
ed
f
o
r
m
etah
eu
r
is
tic
ap
p
r
o
a
ch
es.
Alm
o
s
t
all
m
eta
h
eu
r
is
tic
alg
o
r
ith
m
s
a
r
e
n
o
n
d
eter
m
in
is
tic
an
d
a
p
p
r
o
x
i
m
ate.
T
h
ese
ar
e
u
n
i
v
er
s
al
p
r
o
b
lem
-
s
o
lv
in
g
alg
o
r
ith
m
s
,
c
o
v
er
i
n
g
v
e
r
y
lar
g
e
s
ca
les
o
f
p
r
o
b
lem
s
a
n
d
g
en
e
r
ates
s
atis
f
ac
to
r
y
r
esu
lts
.
T
h
e
r
ef
o
r
e
,
i
m
p
lem
en
tin
g
m
etah
eu
r
is
tic
tech
n
iq
u
es
in
clo
u
d
co
m
p
u
tin
g
,
it
b
ec
o
m
e
p
o
s
s
ib
l
e
t
o
s
o
lv
e
v
ar
i
o
u
s
NP
-
h
ar
d
p
r
o
b
lem
s
in
a
s
h
o
r
t
d
u
r
atio
n
a
n
d
h
y
b
r
id
izatio
n
o
f
m
eta
-
h
eu
r
is
tic
alg
o
r
ith
m
s
ar
e
ca
p
ab
le
o
f
ac
h
iev
in
g
n
ea
r
o
p
tim
al
s
o
lu
tio
n
s
in
a
lim
ited
tim
e
co
n
s
tr
ain
ts
.
T
h
u
s
,
h
y
b
r
id
izatio
n
is
c
o
n
s
id
er
ed
as
an
ef
f
icien
t
way
f
o
r
s
o
lv
in
g
v
e
r
y
c
o
m
p
le
x
an
d
s
o
p
h
is
ticated
r
ea
l
-
tim
e
p
r
o
b
lem
s
.
T
h
is
p
ap
er
p
r
o
p
o
s
es
a
“
h
y
b
r
id
DGH
-
B
W
O”
alg
o
r
ith
m
,
wh
ich
is
th
e
h
y
b
r
id
izatio
n
o
f
two
m
etah
eu
r
is
tic
alg
o
r
ith
m
s
,
f
ir
s
t
g
a
m
e
-
b
ased
alg
o
r
ith
m
n
am
ely
DGO
[
1
4
]
a
n
d
s
ec
o
n
d
s
war
m
-
b
ased
in
te
llig
en
ce
alg
o
r
ith
m
n
am
ely
B
W
O
[
9
]
.
T
h
e
wo
r
k
c
o
n
tr
ib
u
tio
n
o
f
h
y
b
r
id
DGH
-
B
W
O
alg
o
r
ith
m
in
clu
d
es
:
−
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
o
f
f
er
s
m
o
r
e
f
lex
ib
ilit
y
as
it
ca
n
b
e
ea
s
ily
m
o
d
if
ied
to
s
o
lv
e
v
ar
io
u
s
s
ch
ed
u
lin
g
p
r
o
b
lem
s
b
y
m
o
d
if
y
in
g
th
e
as
s
o
ciate
d
alg
o
r
ith
m
s
an
d
p
ar
am
eter
s
.
−
Scalab
ilit
y
is
ea
s
i
ly
ac
h
iev
ab
le
as
p
r
o
p
o
s
ed
alg
o
r
ith
m
is
ca
p
ab
le
o
f
h
an
d
lin
g
h
u
g
e
am
o
u
n
t
o
f
d
ata
f
o
r
p
r
o
ce
s
s
in
g
.
−
Seq
u
en
tial
h
y
b
r
i
d
s
ea
r
ch
s
tr
ateg
y
is
u
s
ed
f
o
r
h
y
b
r
i
d
izatio
n
o
f
DGO
an
d
B
W
O
alg
o
r
ith
m
wh
ich
lead
s
to
th
e
d
ev
elo
p
m
en
t o
f
“
h
y
b
r
id
DG
H
-
B
W
O”
alg
o
r
ith
m
.
−
A
b
alan
ce
d
ap
p
r
o
ac
h
is
estab
lis
h
ed
b
etwe
en
ex
p
ed
itio
n
a
n
d
c
o
n
v
er
g
en
ce
s
tate.
−
I
n
tr
o
d
u
cin
g
a
ch
ec
k
c
o
n
d
itio
n
to
h
y
b
r
id
ized
b
o
th
DGO
an
d
B
W
O
alg
o
r
ith
m
s
s
tr
en
g
th
.
−
E
f
f
icien
t
task
s
ch
ed
u
lin
g
in
clo
u
d
en
v
ir
o
n
m
e
n
t
with
m
ax
im
u
m
r
eso
u
r
ce
u
tili
za
tio
n
,
task
g
u
ar
an
tee
r
atio
,
an
d
th
r
o
u
g
h
p
u
t is ac
h
iev
a
b
le
t
o
an
ex
te
n
t.
−
I
n
ad
d
itio
n
to
ab
o
v
e,
m
in
im
iz
atio
n
o
f
e
n
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
m
ea
n
r
esp
o
n
s
e
tim
e
is
also
ac
h
iev
ab
le.
I
n
co
n
tin
u
atio
n
,
s
ec
tio
n
2
p
r
es
en
ted
liter
atu
r
e
s
u
r
v
ey
.
Sectio
n
3
d
escr
ib
es
th
e
p
r
o
p
o
s
ed
m
e
th
o
d
,
its
an
aly
tical
m
o
d
el,
alg
o
r
it
h
m
,
f
lo
wch
a
r
t,
an
d
o
b
jectiv
e
f
u
n
ctio
n
.
Sectio
n
4
d
ef
in
es
th
e
ex
p
e
r
im
en
tal
s
etu
p
.
R
esu
lts
an
d
d
i
s
c
u
s
s
i
o
n
a
r
e
d
is
c
u
s
s
e
d
i
n
s
e
c
ti
o
n
5
.
F
i
n
a
l
l
y
,
c
o
n
c
l
u
s
i
o
n
a
l
o
n
g
w
i
t
h
f
u
t
u
r
e
a
s
p
ec
t
s
i
s
e
n
c
a
p
s
u
l
a
t
e
d
i
n
s
e
c
t
i
o
n
6.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
Kalr
a
an
d
Sin
g
h
[
1
6
]
,
g
av
e
a
r
ev
iew
o
f
f
iv
e
d
i
f
f
er
en
t
m
eta
h
e
u
r
is
tic
s
ch
ed
u
lin
g
tech
n
iq
u
es.
T
h
ese
ar
e
AC
O,
PS
O,
B
AT
alg
o
r
ith
m
,
GA,
an
d
lea
g
u
e
ch
am
p
i
o
n
s
h
ip
alg
o
r
ith
m
(
L
C
A)
.
Au
t
h
o
r
s
also
d
ef
i
n
ed
th
e
o
p
tim
izatio
n
c
r
iter
ia
to
b
e
c
o
n
s
id
er
ed
wh
ile
s
ch
ed
u
lin
g
task
s
in
clo
u
d
e
n
v
ir
o
n
m
e
n
t,
s
u
c
h
as
m
ak
esp
a
n
,
a
n
d
waitin
g
tim
e.
M
u
r
ad
et
a
l.
[
1
7
]
g
a
v
e
a
n
o
v
er
all
v
iew
o
f
v
ar
i
o
u
s
jo
b
s
ch
ed
u
lin
g
tech
n
iq
u
es
(
J
ST)
an
d
r
eso
u
r
ce
allo
ca
tio
n
s
(
R
A)
tech
n
iq
u
es
f
o
r
clo
u
d
co
m
p
u
tin
g
en
v
ir
o
n
m
en
t.
Au
th
o
r
s
class
if
ied
s
ch
ed
u
lin
g
tech
n
iq
u
es
as
h
eu
r
is
tic,
m
etah
eu
r
is
tic
an
d
h
y
b
r
id
s
ch
ed
u
lin
g
.
Mo
h
am
m
ad
za
d
eh
et
a
l.
[
1
8
]
,
g
i
v
es
an
o
v
er
v
iew
o
f
v
a
r
io
u
s
wh
ale
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
OA)
v
ar
ian
ts
u
s
ed
b
y
s
e
v
er
al
au
th
o
r
s
f
o
r
ef
f
icien
t
s
ch
ed
u
lin
g
in
clo
u
d
en
v
ir
o
n
m
en
t.
T
h
ese
v
ar
i
o
u
s
task
s
ch
ed
u
lin
g
m
o
d
els
ar
e:
s
tan
d
ar
d
W
OA,
m
u
lti
-
o
b
jectiv
e
W
OA,
im
p
r
o
v
ed
W
OA,
an
d
hy
b
r
id
W
OA.
Ma
in
s
ch
ed
u
lin
g
o
b
jectiv
es
to
ac
h
iev
ed
a
r
e:
m
ak
esp
a
n
,
b
u
d
g
et
,
q
u
ality
o
f
s
er
v
ice
(
Qo
S),
en
er
g
y
ef
f
icien
cy
,
co
s
t,
r
eso
u
r
ce
u
tili
za
tio
n
,
lo
ad
b
alan
cin
g
,
p
er
f
o
r
m
a
n
ce
,
ef
f
icie
n
cy
,
d
ea
d
lin
e
,
an
d
s
ec
u
r
ity
.
C
h
en
et
a
l.
[
1
9
]
,
p
r
o
p
o
s
ed
a
m
et
h
o
d
f
o
r
task
s
c
h
ed
u
lin
g
in
clo
u
d
co
m
p
u
tin
g
u
s
in
g
W
OA
-
b
ased
o
p
tim
izatio
n
.
T
h
is
p
r
o
p
o
s
ed
m
eth
o
d
is
im
p
r
o
v
ed
W
OA
f
o
r
clo
u
d
(
I
W
C
)
task
s
ch
ed
u
lin
g
.
T
h
e
o
b
jectiv
e
o
f
I
W
C
m
e
t
h
o
d
o
l
o
g
y
i
s
t
o
m
i
n
i
m
i
z
e
t
h
e
e
x
e
c
u
t
i
o
n
t
i
m
e
,
l
o
a
d
,
a
n
d
c
o
s
t
o
f
t
h
e
c
l
o
u
d
c
o
m
p
u
t
i
n
g
s
y
s
t
em
.
Z
h
o
n
g
e
t
a
l
.
[
9
],
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Op
timiz
in
g
clo
u
d
ta
s
ks sch
ed
u
lin
g
b
a
s
ed
o
n
t
h
e
h
yb
r
id
iz
a
ti
o
n
o
f d
a
r
ts
g
a
me
…
(
Ma
n
is
h
C
h
h
a
b
r
a
)
1197
d
escr
ib
es
a
s
war
m
-
b
ased
m
etah
eu
r
is
tic
alg
o
r
ith
m
n
am
el
y
,
B
W
O
a
lg
o
r
ith
m
f
o
r
s
o
lv
in
g
v
ar
io
u
s
o
p
tim
izatio
n
p
r
o
b
lem
s
.
T
h
e
alg
o
r
ith
m
is
d
esig
n
ed
o
n
th
e
b
asis
o
f
b
elu
g
a
w
h
ales’
b
eh
a
v
io
r
in
t
h
e
Ar
tic
Oc
ea
n
,
wh
ich
in
clu
d
es
s
wim
m
in
g
in
g
r
o
u
p
,
lo
o
k
in
g
f
o
r
p
r
ey
a
n
d
f
in
ally
p
lu
n
g
e
in
t
o
th
e
o
ce
an
b
ed
i.e
.
wh
ale
d
r
o
p
s
.
Deh
g
h
a
n
i
et
a
l.
[
1
4
]
,
p
r
o
p
o
s
ed
a
g
am
e
-
b
ased
o
p
tim
izatio
n
m
eth
o
d
,
n
am
ely
DGO.
T
h
e
a
r
ch
itectu
r
e
o
f
DG
O
is
estab
lis
h
ed
b
y
r
ep
licatin
g
th
e
Dar
t
g
am
e
r
u
l
es.
T
h
e
k
ey
p
o
in
ts
o
f
DGO
alg
o
r
ith
m
ar
e
its
s
im
p
le
eq
u
ati
o
n
s
an
d
a
b
s
en
ce
o
f
co
n
tr
o
l
p
ar
a
m
eter
s
.
C
h
en
et
a
l.
[
2
0
]
,
p
r
o
p
o
s
ed
a
m
etah
eu
r
is
tic
alg
o
r
ith
m
–
eg
r
et
s
war
m
o
p
ti
m
izatio
n
alg
o
r
ith
m
(
E
SOA)
–
to
en
h
an
ce
th
e
b
alan
cin
g
b
etwe
en
ex
p
e
d
itio
n
an
d
co
n
v
er
g
en
ce
s
tates
o
f
alg
o
r
ith
m
.
T
h
e
alg
o
r
ith
m
is
s
tim
u
lated
b
y
th
e
h
u
n
tin
g
s
k
ills
o
f
two
eg
r
et
s
p
ec
ies’
–
th
e
S
n
o
wy
eg
r
et’
s
s
it
-
an
d
-
wait
ap
p
r
o
ac
h
,
an
d
th
e
Gr
ea
t
eg
r
et’
s
ag
g
r
ess
iv
e
ap
p
r
o
ac
h
.
T
h
e
d
is
cr
im
in
an
t
s
itu
atio
n
is
u
s
ed
to
estab
lis
h
a
b
alan
ce
b
etwe
en
two
ap
p
r
o
ac
h
es.
T
r
o
jo
v
s
k
y
a
n
d
Deh
g
h
an
i
[
2
1
]
,
p
r
o
p
o
s
ed
a
b
io
lo
g
y
-
s
tim
u
l
ated
m
etah
eu
r
is
tic
tech
n
iq
u
e
k
n
o
wn
as
walr
u
s
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
aO
A)
.
T
h
e
alg
o
r
ith
m
ar
ch
itectu
r
e
is
b
ased
o
n
th
e
n
atu
r
al
b
eh
av
i
o
r
s
o
f
walr
u
s
,
wh
ich
in
clu
d
e
f
ee
d
i
n
g
o
r
n
o
u
r
is
h
in
g
s
in
g
les,
m
ig
r
atio
n
s
,
f
ig
h
ts
wit
h
p
r
ed
at
o
r
s
o
r
escap
in
g
th
em
.
Sh
an
ay
an
d
R
ah
ee
m
[
2
2
]
,
d
escr
ib
es
ar
tific
ial
b
ee
co
lo
n
y
(
AB
C
)
alg
o
r
ith
m
an
d
b
ee
co
lo
n
y
o
p
tim
izatio
n
(
B
C
O)
alg
o
r
ith
m
f
o
r
s
o
lv
in
g
tr
av
ellin
g
s
alesm
an
co
m
b
in
ato
r
ial
p
r
o
b
lem
s
.
Sab
er
et
a
l.
[
2
3
]
,
g
av
e
o
u
tlin
e
o
f
v
ar
io
u
s
m
etah
e
u
r
is
tic
alg
o
r
ith
m
s
an
d
its
ap
p
lica
tio
n
s
in
en
g
in
ee
r
i
n
g
f
ie
ld
.
Fo
r
ex
am
p
le,
GA,
is
wid
ely
u
s
ed
in
cir
cu
it
d
esig
n
in
g
an
d
m
ac
h
in
e
lear
n
in
g
.
AC
O,
is
em
p
lo
y
ed
in
r
o
u
tin
g
a
n
d
s
ch
ed
u
lin
g
p
r
o
b
lem
s
,
in
tr
an
s
p
o
r
tatio
n
p
lan
n
in
g
,
an
d
telec
o
m
m
u
n
icatio
n
s
.
PS
O,
u
s
ed
in
p
o
wer
s
y
s
tem
o
p
tim
izatio
n
,
r
o
b
o
tics
,
an
d
im
ag
e
p
r
o
ce
s
s
in
g
.
R
ez
k
et
a
l.
[
2
4
]
,
d
escr
ib
es v
ar
io
u
s
m
etah
e
u
r
is
tic
tech
n
iq
u
es e
m
p
lo
y
ed
f
o
r
s
o
lv
in
g
r
ea
l
-
tim
e
elec
tr
ical
an
d
civ
il
en
g
in
ee
r
in
g
ap
p
licatio
n
s
,
s
u
ch
as
elec
tr
ic
v
eh
icle
ch
ar
g
in
g
s
ch
e
d
u
lin
g
p
r
o
b
lem
,
s
tr
u
ctu
r
a
l
d
esig
n
o
p
tim
izatio
n
p
r
o
b
lem
.
Mo
s
taf
a
an
d
Als
alm
an
[
2
5
]
,
u
s
es
d
o
lp
h
in
s
war
m
alg
o
r
ith
m
f
o
r
s
o
lv
in
g
r
ea
l
-
tim
e
s
o
f
twar
e
p
r
o
ject
s
ch
ed
u
lin
g
p
r
o
b
lem
.
3.
M
E
T
H
O
D
E
v
er
y
m
etah
eu
r
is
tic
alg
o
r
ith
m
m
ain
ly
co
n
s
is
ts
o
f
two
s
tates:
ex
p
ed
itio
n
an
d
co
n
v
er
g
en
ce
.
Fo
r
f
i
n
d
in
g
th
e
g
lo
b
al
o
p
tim
a,
it
is
d
if
f
icu
l
t
to
estab
lis
h
ed
a
b
alan
ce
b
etw
ee
n
ex
p
ed
itio
n
an
d
co
n
v
er
g
e
n
ce
s
tate.
E
x
p
e
d
itio
n
s
tate
f
o
cu
s
o
n
th
e
g
lo
b
al
s
ea
r
ch
ar
ea
an
d
co
n
v
er
g
e
n
ce
s
tate
f
o
cu
s
o
n
th
e
lo
ca
l
s
ea
r
ch
ar
ea
.
So
,
in
th
e
“
h
y
b
r
id
DGH
-
B
W
O”
alg
o
r
ith
m
,
d
a
r
ts
g
am
e
h
y
p
o
th
esis
(
DGH)
,
s
ec
tio
n
em
p
h
asizes
ex
p
ed
itio
n
b
y
m
im
ick
i
n
g
th
e
th
r
o
win
g
o
f
d
ar
ts
to
war
d
s
th
e
t
ar
g
et
in
th
e
s
ea
r
ch
s
p
ac
e
wh
ich
m
ar
k
s
th
e
b
o
u
n
d
ar
ies
f
o
r
th
e
e
x
p
ed
itio
n
.
W
h
er
ea
s
B
W
O,
s
ec
t
io
n
em
p
h
asizes
co
n
v
er
g
e
n
ce
b
y
u
s
in
g
its
ec
h
o
l
o
ca
tio
n
tech
n
iq
u
e.
B
y
co
m
b
i
n
in
g
b
o
th
s
tr
ateg
ies,
h
y
b
r
id
DGH
-
B
W
O
ac
h
iev
es
a
b
alan
ce
b
etwe
en
e
x
p
ed
itio
n
an
d
co
n
v
er
g
en
ce
,
allo
win
g
it
t
o
e
f
f
icien
tly
n
av
i
g
ate
th
r
o
u
g
h
th
e
s
o
lu
tio
n
s
p
ac
e
wh
i
le
ex
p
lo
itin
g
p
r
o
m
is
in
g
r
eg
io
n
s
.
3
.
1
.
D
a
r
t
s
g
a
m
e
hy
po
t
hes
is
DGH
is
b
ased
o
n
th
e
c
o
n
ce
p
t
o
f
DGO.
Dar
ts
,
a
co
m
p
etitiv
e
s
h
o
o
tin
g
s
p
o
r
t
in
w
h
ich
tw
o
o
r
m
o
r
e
p
lay
er
s
th
r
o
w
d
a
r
ts
at
a
d
ar
tb
o
ar
d
.
Dar
ts
ar
e
th
e
s
m
all
s
h
ar
p
-
p
o
in
te
d
p
r
o
jectiles
an
d
d
ar
t
b
o
ar
d
is
a
cir
c
u
la
r
s
h
ap
ed
tar
g
et
h
av
in
g
n
u
m
e
r
o
u
s
co
n
ce
n
tr
ic
r
in
g
s
an
d
is
d
iv
id
ed
in
to
2
0
r
ad
ial
s
ec
tio
n
s
,
ea
ch
s
ec
tio
n
h
av
in
g
d
if
f
er
en
t
ass
ig
n
ed
p
o
in
ts
,
as
s
h
o
wn
in
Fig
u
r
e
1
.
E
ac
h
s
ec
tio
n
is
f
u
r
th
er
s
u
b
-
d
iv
id
e
d
in
to
o
th
er
s
ec
tio
n
s
-
s
in
g
le,
d
o
u
b
le
an
d
tr
ip
le
s
co
r
in
g
s
ec
tio
n
s
-
b
y
co
n
ce
n
tr
ic
m
etal
wir
e
r
in
g
s
.
At
th
e
ce
n
ter
o
f
t
h
e
d
ar
t
b
o
ar
d
th
er
e
a
r
e
two
cir
cles k
n
o
wn
as in
n
e
r
b
u
ll a
n
d
o
u
ter
b
u
ll,
h
a
v
in
g
c
o
lo
r
r
ed
an
d
g
r
ee
n
r
esp
ec
tiv
ely
.
Fig
u
r
e
1
.
Dar
ts
,
d
a
r
tb
o
ar
d
an
d
s
co
r
e
d
is
tr
ib
u
tio
n
o
n
d
ar
tb
o
a
r
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
1
1
9
5
-
1
2
0
7
1198
3
.
2
.
B
WO
A
s
war
m
in
tellig
en
ce
,
m
etah
e
u
r
is
tic
o
p
tim
izatio
n
alg
o
r
ith
m
.
T
h
e
B
W
O
alg
o
r
ith
m
is
ex
ec
u
t
ed
in
th
r
ee
s
tates,
wh
ich
ar
e,
ex
p
ed
itio
n
,
co
n
v
er
g
en
ce
,
a
n
d
wh
ale
d
r
o
p
s
.
3
.
3
.
Dev
el
o
pin
g
“
hy
brid DG
H
-
B
WO
”
m
o
del
T
h
e
DGH
s
ec
tio
n
o
f
th
e
p
r
o
p
o
s
ed
“Hy
b
r
id
DGH
-
B
W
O”
alg
o
r
ith
m
is
in
s
p
ir
ed
b
y
th
e
co
n
ce
p
t
o
f
g
am
e
o
f
d
ar
ts
.
T
h
e
d
ar
ts
th
em
s
elv
es
r
ep
r
esen
ts
th
e
in
d
iv
id
u
als
o
r
p
o
ten
tial
s
o
lu
tio
n
s
th
r
o
wn
b
y
th
e
p
lay
er
s
.
Play
er
s
ar
e
o
n
ly
th
e
ag
e
n
ts
th
at
ar
e
th
r
o
win
g
th
e
d
ar
ts
.
T
h
eir
s
p
ec
if
ic
p
o
s
itio
n
s
o
r
ac
tio
n
s
ar
e
n
o
t
r
elev
an
t
to
th
e
alg
o
r
ith
m
'
s
ca
lcu
latio
n
s
.
T
h
e
B
W
O
s
ec
tio
n
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
is
in
s
p
ir
ed
b
y
th
e
b
eh
av
io
r
o
f
b
elu
g
a
wh
ales’
p
o
p
u
latio
n
.
I
n
th
e
B
W
O
p
o
p
u
latio
n
,
ea
ch
b
elu
g
a
wh
ale
th
em
s
elv
es
r
ep
r
esen
ts
th
e
in
d
i
v
id
u
als
o
r
p
o
ten
tial
s
o
lu
tio
n
s
.
T
h
er
ef
o
r
e,
in
th
e
“
h
y
b
r
id
DGH
-
B
W
O”
a
lg
o
r
ith
m
,
wo
r
d
“e
x
p
lo
r
e
r
s
”
r
ep
r
esen
t
th
e
an
alo
g
y
o
f
p
lay
er
s
th
r
o
win
g
th
e
d
ar
ts
,
a
n
d
b
elu
g
a
wh
ales.
T
h
e
ter
m
i
n
o
lo
g
y
“e
x
p
l
o
r
er
”
s
er
v
es
as
a
co
n
ce
p
tu
al
f
r
am
ewo
r
k
to
u
n
d
e
r
s
tan
d
h
o
w
t
h
e
p
r
o
p
o
s
e
d
alg
o
r
ith
m
wo
r
k
s
.
3
.
3
.
1
.
Dev
elo
pin
g
hy
bridi
ze
d
ph
a
s
e
Me
th
o
d
o
lo
g
y
:
se
q
u
en
tial
h
y
b
r
id
s
ea
r
ch
s
tr
ateg
y
is
u
s
ed
f
o
r
th
e
h
y
b
r
id
izatio
n
o
f
DGO
an
d
B
W
O
alg
o
r
ith
m
wh
ich
lead
s
to
th
e
d
ev
elo
p
m
en
t
o
f
“
h
y
b
r
id
DGH
-
B
W
O”
alg
o
r
ith
m
.
I
n
s
eq
u
e
n
tial
h
y
b
r
id
s
ea
r
ch
s
tr
ateg
y
,
th
e
p
h
ase
-
b
ased
ap
p
r
o
ac
h
is
u
s
ed
f
o
r
ac
h
iev
in
g
h
y
b
r
id
izatio
n
,
wh
er
e
th
e
alg
o
r
ith
m
ca
n
s
witch
b
etwe
en
DGO
an
d
B
W
O
p
h
ases
b
ased
o
n
p
r
e
d
ef
in
ed
c
o
n
d
itio
n
s
(
ch
ec
k
co
n
d
itio
n
s
)
.
I
n
th
is
ap
p
r
o
ac
h
,
DGO
is
em
p
lo
y
ed
f
o
r
in
itial e
x
p
e
d
itio
n
an
d
B
W
O
is
em
p
lo
y
ed
f
o
r
f
in
e
-
tu
n
in
g
s
o
lu
tio
n
s
.
3
.
3
.
2
.
P
ro
ce
du
re
o
f
hy
bridi
za
t
io
n
I
n
itial
e
x
p
ed
itio
n
p
h
ase
(
DGO
)
:
−
I
n
itial
ex
p
ed
itio
n
is
d
o
n
e
b
y
t
h
e
co
n
ce
p
t
o
f
DGO
alg
o
r
ith
m
,
wh
er
e
ex
p
ed
itio
n
is
g
u
id
e
d
b
y
th
e
c
o
n
ce
p
ts
o
f
r
a
n
d
o
m
d
a
r
ts
(
also
n
a
m
ed
as
in
d
iv
id
u
als
o
r
p
lay
er
s
)
,
wh
i
ch
ar
e
r
an
d
o
m
ly
th
r
o
wn
to
wa
r
d
s
th
e
tar
g
ets
(
o
p
tim
al
s
o
lu
tio
n
)
in
th
e
s
ea
r
c
h
s
p
ac
e.
−
T
ar
g
et
-
o
r
ie
n
ted
ad
ju
s
tm
en
t
is
p
er
f
o
r
m
ed
in
wh
ich
t
h
e
in
d
i
v
id
u
als'
p
o
s
itio
n
s
ar
e
ad
ju
s
te
d
to
war
d
s
th
e
cu
r
r
en
t
b
est s
o
lu
tio
n
b
y
u
s
in
g
DGO
tech
n
iq
u
e.
C
o
n
v
er
g
en
ce
p
h
ase
(
B
W
O)
:
−
E
x
p
ed
itio
n
r
ate
o
f
B
W
O
is
co
n
s
tan
tly
r
ed
u
ce
d
,
th
u
s
f
o
cu
s
in
g
m
o
r
e
o
n
c
o
n
v
e
r
g
en
ce
p
h
ase.
−
B
W
O
's ec
h
o
lo
ca
tio
n
m
ec
h
a
n
is
m
is
u
s
ed
to
r
ef
in
e
th
e
s
o
l
u
tio
n
s
.
3
.
3
.
3
.
Def
ini
ng
t
he
chec
k
co
n
ditio
ns
T
h
e
ch
ec
k
c
o
n
d
itio
n
f
o
r
“Hy
b
r
id
DGH
-
B
W
O”
alg
o
r
ith
m
is
d
eter
m
i
n
ed
as
(
1
)
.
ℎ
=
(
(
1
)
,
&
.
(
2
)
ℎ
ℎ
)
(
1
)
w
h
er
e
,
r
ep
r
esen
ts
th
e
to
tal
n
u
m
b
er
o
f
r
ep
etitio
n
s
.
3
.
4
.
M
a
t
hema
t
ica
l
m
o
del o
f
hy
brid
DG
H
-
B
WO
A
m
atr
ix
o
f
e
x
p
lo
r
e
r
s
(
×
)
,
wh
er
e
‘
n
’
r
ep
r
esen
ts
ex
p
lo
r
er
s
’
p
o
p
u
latio
n
s
ize
an
d
‘
d
’
r
ep
r
esen
ts
d
im
en
s
io
n
al
p
o
s
itio
n
v
ec
to
r
s
,
i
s
r
ep
r
esen
ted
as
(
2
)
.
=
(
1
2
⋮
|
1
,
1
1
,
2
⋯
1
,
2
,
1
2
,
2
⋯
2
,
⋮
⋮
,
ℎ
⋮
,
1
,
2
⋯
,
)
ℎ
1
≤
≤
1
≤
ℎ
≤
(
2
)
A
b
o
v
e,
,
ℎ
r
ep
r
esen
ts
th
e
′
ℎ
′
ex
p
lo
r
e
r
at
′
ℎ
ℎ
′
,
d
im
en
s
io
n
al
lo
ca
tio
n
,
wh
er
e
1
≤
≤
1
≤
ℎ
≤
.
T
h
e
f
itn
ess
v
alu
es r
elate
d
to
ea
ch
e
x
p
lo
r
er
is
s
to
r
ed
in
th
e
f
o
r
m
o
f
m
atr
ix
,
an
d
is
r
ep
r
esen
ted
as
(
3
)
.
=
(
(
1
→
1
,
1
,
⋯
,
1
,
3
,
…
,
1
,
ℎ
,
⋯
,
1
,
)
(
2
→
2
,
1
,
⋯
,
2
,
3
,
…
,
2
,
ℎ
,
…
,
2
,
)
⋮
(
→
,
1
,
⋯
,
,
3
,
⋯
,
,
ℎ
,
⋯
,
,
)
⋮
(
→
,
1
,
⋯
,
,
3
,
…
,
,
ℎ
,
…
,
,
)
)
(
3
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Op
timiz
in
g
clo
u
d
ta
s
ks sch
ed
u
lin
g
b
a
s
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o
n
t
h
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h
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ti
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f d
a
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me
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(
Ma
n
is
h
C
h
h
a
b
r
a
)
1199
3
.
4
.
1
.
E
x
peditio
n pha
s
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(
DG
H
)
I
n
itial,
ex
p
e
d
itio
n
p
h
ase
is
d
o
n
e
b
y
t
h
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s
ec
tio
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o
f
“
h
y
b
r
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B
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r
ith
m
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b
y
ass
ig
n
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g
th
e
ex
p
lo
r
e
r
v
alu
es
to
th
e
f
itn
ess
f
u
n
ctio
n
,
b
est
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d
wo
r
s
t
f
itn
ess
f
u
n
ctio
n
v
alu
e
(
an
d
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an
d
b
est an
d
wo
r
s
t v
ar
iab
le
’
s
v
alu
es (
an
d
)
ar
e
estab
lis
h
ed
(
4
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-
(
7
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.
=
(
)
×
1
(
4
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=
(
)
×
1
(
5
)
=
(
min
(
)
,
1
:
)
(
6
)
=
(
ma
x
(
)
,
1
:
)
(
7
)
Fit
n
ess
f
u
n
ctio
n
n
o
r
m
alize
v
a
lu
e,
,
an
d
p
r
o
b
ab
ilit
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f
u
n
ctio
n
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alu
e,
,
f
o
r
ea
ch
ℎ
ex
p
lo
r
er
is
ca
lcu
lated
as
(
8
)
,
(
9
)
.
=
−
∑
(
−
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=
1
(
8
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=
m
ax
(
)
(
9
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Sin
ce
,
th
er
e
is
to
tal
8
2
s
ec
to
r
s
in
a
d
ar
tb
o
ar
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h
av
in
g
d
if
f
er
e
n
t
s
co
r
es.
As
s
u
m
e,
ev
er
y
ex
p
l
o
r
er
ca
n
th
r
o
w
o
n
l
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r
ee
d
ar
ts
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ea
ch
tu
r
n
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b
u
ild
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is
s
co
r
e
m
atr
ix
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ac
h
th
r
o
w
s
co
r
es c
an
b
e
ca
lcu
lated
in
(
1
0
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-
(
1
3
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.
=
(
82
∗
(
1
−
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1
≤
≤
(
10)
=
{
(
1
:
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<
(
+
1
:
82
)
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,
me
a
n
s
ℎ
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xpl
or
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r
s
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or
e
c
a
n
dida
te
s
.
(
1
1
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=
(
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&
1
≤
≤
82
,
r
e
p
r
e
s
e
n
ts
e
ve
r
y
thro
w
s
c
or
e
va
l
ue
.
(
1
2
)
=
∑
ℎ
3
ℎ
=
1
180
,
de
n
o
te
s
the
n
or
ma
l
ize
d
s
c
or
e
va
l
ue
.
(
1
3
)
So
,
th
e
n
ew
u
p
d
ated
s
tate
o
f
e
v
er
y
ex
p
lo
r
er
is
estab
lis
h
ed
as
(
1
4
)
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=
+
(
1
,
)
(
−
3
)
(
1
4
)
W
ith
th
e
ad
v
an
ce
m
en
t
o
f
e
ac
h
r
e
p
etitio
n
,
t
h
e
p
r
ed
ef
i
n
e
d
ch
ec
k
c
o
n
d
itio
n
d
eter
m
in
e
s
th
e
ex
ec
u
tio
n
o
f
co
n
v
er
g
en
ce
p
h
ase,
wh
ich
is
c
o
n
d
u
cte
d
b
y
B
W
O
s
ec
tio
n
o
f
“
h
y
b
r
id
DGH
-
B
W
O”
alg
o
r
ith
m
.
3
.
4
.
2
.
Co
nv
er
g
ence
ph
a
s
e
(
B
WO
)
First,
b
alan
ce
f
ac
to
r
,
is
ca
lcu
lated
wh
ich
d
eter
m
in
es
th
e
s
witch
in
g
f
r
o
m
ex
p
e
d
itio
n
to
co
n
v
er
g
en
ce
s
ta
te,
an
d
is
ex
p
r
ess
ed
as
(
1
5
)
.
=
0
(
1
−
2
⁄
)
(
1
5
)
Ab
o
v
e,
0
is
th
e
b
alan
ce
f
ac
to
r
wh
ich
ch
a
n
g
es r
a
n
d
o
m
ly
b
etwe
en
(
0
,
1
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at
ea
c
h
r
ep
etitio
n
.
′
′
r
e
p
r
esen
ts
th
e
p
r
esen
t
r
ep
etitio
n
an
d
r
ep
r
ese
n
ts
th
e
m
ax
im
al
r
ep
etitio
n
s
.
I
f
>
0
.
5
,
E
x
p
ed
itio
n
s
tate
tak
e
p
lace
.
If
≤
0
.
5
,
co
n
v
e
r
g
en
ce
s
tate
h
ap
p
e
n
s
.
As
th
e
r
ep
etitio
n
s
′
′
in
cr
ea
s
es,
th
e
v
alu
e
r
ed
u
ce
s
f
r
o
m
(
0
,
1
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to
(
0
,
0
.
5
)
.
T
h
e
ex
p
lo
r
er
d
r
o
p
s
p
r
o
b
ab
ilit
y
is
also
ca
lcu
lated
,
wh
ich
is
u
s
ed
f
o
r
th
e
r
ef
in
em
e
n
t
o
f
o
p
t
im
al
s
o
lu
tio
n
an
d
is
d
e
f
in
ed
as
(
1
6
)
.
=
0
.
1
−
0
.
05
⁄
(
1
6
)
T
h
e
p
r
o
b
a
b
ilit
y
o
f
e
x
p
lo
r
e
r
d
r
o
p
r
e
d
u
ce
s
f
r
o
m
0
.
1
0
.
05
,
with
th
e
ad
v
a
n
ce
m
en
t
o
f
r
ep
etitio
n
s
.
Sin
ce
,
m
ajo
r
ity
o
f
ex
p
ed
itio
n
is
alr
ea
d
y
d
o
n
e
b
y
th
e
DGH
s
ec
tio
n
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
,
th
e
B
W
O’
s
ex
p
ed
itio
n
p
h
ase
r
ed
u
ce
s
to
an
ex
ten
t a
n
d
m
o
r
e
f
o
cu
s
is
o
n
co
n
v
er
g
en
ce
p
h
ase
o
f
B
W
O
s
ec
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
1
1
9
5
-
1
2
0
7
1200
a.
E
x
p
ed
itio
n
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tate
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wim
m
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eh
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v
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o
f
th
ese
ex
p
lo
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er
s
(
b
el
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g
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es
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e
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ed
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h
e
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air
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m
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g
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lo
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s
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eter
m
in
e
t
h
e
n
ew
u
p
d
ated
p
o
s
itio
n
s
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d
is
co
m
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ted
as
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o
llo
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,
ℎ
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(
,
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n
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f
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e
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ted
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r
o
m
ℎ
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ar
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er
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a
r
b
itr
a
r
y
n
umb
e
r
s
.
(
1
9
)
Σ
=
(
γ
(
1
+
Β
)
×
s
i
n
(
ΠΒ
2
)
γ
(
(
1
+
Β
)
2
)
×
Β
×
2
(
Β
−
1
)
2
)
1
Β
,
ℎ
Β
‘
b
e
t
a
’
de
fa
ul
t
va
l
ue
s
e
t
to
Β
=
1
.
5
(
2
0
)
So
,
th
e
an
aly
tical
m
o
d
el
f
o
r
co
n
v
er
g
e
n
ce
s
tate
is
d
ef
in
ed
as
(
2
1
)
.
+
1
=
3
−
4
+
1
.
.
(
−
)
(
2
1
)
wh
er
e
r
ep
r
esen
ts
th
e
b
est p
o
s
itio
n
am
o
n
g
ex
p
lo
r
er
s
.
r
ep
r
ese
n
ts
a
r
an
d
o
m
ℎ
ex
p
lo
r
er
.
c.
E
x
p
lo
r
er
d
r
o
p
s
s
tate
E
x
p
lo
r
er
s
a
r
e
v
u
ln
er
ab
le
s
p
ec
ies
an
d
s
o
m
e
e
x
p
lo
r
e
r
s
ca
n
’
t
es
ca
p
e
attac
k
an
d
p
e
r
is
h
ed
in
th
e
b
o
tto
m
less
o
ce
an
b
ed
.
T
h
is
is
k
n
o
wn
as “
E
x
p
lo
r
er
Dr
o
p
s
”.
Ass
u
m
ed
th
at
th
e
ex
p
lo
r
er
d
r
o
p
s
tim
u
lates a
s
m
all
ch
an
g
e
an
d
th
e
p
o
p
u
latio
n
s
ize
is
alm
o
s
t
r
em
ain
ed
co
n
s
tan
t.
Def
in
e
th
e
,
wh
ich
is
th
e
s
tep
s
ize
o
f
ex
p
l
o
r
er
d
r
o
p
an
d
is
ex
p
r
ess
ed
as
(
2
2
)
.
=
(
−
)
e
xp
(
−
2
⁄
)
(
2
2
)
W
h
er
e
an
d
ar
e
th
e
u
p
p
er
a
n
d
th
e
lo
wer
lim
it
o
f
v
a
r
iab
les.
2
d
ef
in
es
th
e
s
tep
f
ac
to
r
wh
ic
h
is
r
elate
d
to
ex
p
l
o
r
er
d
r
o
p
an
d
p
o
p
u
latio
n
s
ize
an
d
is
d
ef
in
ed
a
s
(
2
3
)
.
2
=
2
×
(
2
3
)
T
h
e
ex
p
lo
r
er
d
r
o
p
s
p
r
o
b
a
b
ilit
y
is
d
ef
in
ed
i
n
(
1
6
)
.
So
,
t
h
e
n
e
w
u
p
d
ated
p
o
s
itio
n
is
co
m
p
u
te
d
as
(
2
4
)
.
+
1
=
5
−
6
+
7
(
2
4
)
W
h
er
e
5
,
6
7
ar
e
th
e
ar
b
itra
r
y
n
u
m
b
er
s
an
d
th
eir
v
alu
es lies b
etwe
en
(
0
,
1
)
.
3
.
5
.
P
r
o
po
s
ed
a
lg
o
rit
hm
o
f
hy
br
id
DG
H
-
B
WO
Seq
u
en
tial
h
y
b
r
i
d
izatio
n
allo
ws
th
e
alg
o
r
ith
m
to
m
ain
tain
a
b
alan
ce
b
etwe
en
ex
p
e
d
itio
n
an
d
co
n
v
er
g
en
ce
p
h
ase.
C
h
ec
k
co
n
d
itio
n
d
eter
m
in
e
th
e
s
witch
es
b
etwe
en
th
e
p
h
ases
.
T
h
e
“H
y
b
r
id
DGH
-
B
W
O”
alg
o
r
ith
m
ca
n
b
e
s
ee
n
in
Alg
o
r
ith
m
1
.
Alg
o
r
ith
m
1
.
Hy
b
r
id
DGH
-
B
W
O
alg
o
r
ith
m
1:
Define the population siz
e n and maximal count
of
repetitions Z_max. Initia
lize current
repetition value Z=1.
2:
Set the check condition variables value ‘A’ & ‘B’ using(1).
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Op
timiz
in
g
clo
u
d
ta
s
ks sch
ed
u
lin
g
b
a
s
ed
o
n
t
h
e
h
yb
r
id
iz
a
ti
o
n
o
f d
a
r
ts
g
a
me
…
(
Ma
n
is
h
C
h
h
a
b
r
a
)
1201
3:
Starting
positions
of
al
l
explorers
are
arbitrar
y
generated
and
fitness
values
ar
e
established using(2), (3) on the basis of objective function, defined in(25).
4:
While Z≤Z_max, Do
5:
For each explorer E_g, 1≤g≤n, Do
6:
If check condition! =True
7:
Calculate
〖
Fit
〗
_Best,
〖
F
it
〗
_Worst
,
E
_B
es
t
an
d
E
_Worst
us
in
g
(4
),
(5
),
(6
),
an
d
(7
).
8:
Compute
the
fitness
fun
ction
normalized
value
〖
Fit
〗
_g^Normal,
and
pr
obability
function
〖
Prob
〗
_g, for explorer E_g, using (8) and (9) respectively.
9:
Calculate the normalized
score value S_g^Normal, f
or
explorer E_g, using (1
0), (11),
(12), and (13).
10:
Explorer E_g, new state is updated using (14).
11:
Ch
ec
k
ne
w
lo
ca
ti
on
bo
un
da
r
ie
s,
co
mp
ut
e
th
e
fi
tn
es
s
v
al
ue
s
an
d
so
rt
ed
th
em
to
d
et
er
mi
ne
the optimal solution.
12:
Else //check condition
is True
13:
Calculate
〖
bal
〗
_Factor, using (15), and E_drop, using (16).
14:
If
〖
bal
〗
_Factor>0.5 //then it is Expedition phase
15:
Generate d_h,where 1≤h≤d, randomly from dimension.
16:
Select a random explorer E_r.
17:
Explorer E_g, new loca
tion is updated using (17).
18:
ElseIf
〖
bal
〗
_Factor≤0.5 // then it is Convergence phase
19:
Update arbitrary jump str
ength J_1 using (18) and
compute Levy Fight operat
ion using
(19), (20).
20:
Explorer E_g, new location is updated using (21).
21
:
End If. //balance factor.
22:
Ch
ec
k
ne
w
lo
ca
ti
on
bo
un
da
ri
es
,
co
mp
ut
e
fi
tn
es
s
va
l
ue
s
an
d
so
rt
ed
th
em
to
d
et
er
mi
ne
the optimal solution.
23:
If
〖
bal
〗
_Factor<E_drop //Explorer Drops phase
24:
Up
da
te
th
e
st
ep
fa
ct
or
J
_2
,
us
in
g
(2
2)
&
Co
mp
ut
e
ex
pl
or
er
st
ep
si
ze
E_
s
t
ep
,
us
in
g
(23).
25:
Explorer E_g, new location is updated using (24).
26:
Ch
ec
k
ne
w
lo
ca
ti
on
bo
un
da
r
ie
s,
co
mp
ut
e
th
e
fi
tn
es
s
v
al
ue
s
an
d
so
rt
ed
th
em
to
d
et
er
mi
ne
the optimal solution.
27:
End If. //Explorer D
rops phase
28:
End If. //check condition
29:
Increment value of 'g', for next explorer E_g.
30:
End For.
31:
Determine the latest best solution.
32:
Z=Z+1.
33:
End While.
34:
Output the latest best optimal
solution.
3.
6
.
F
l
o
wcha
rt
Flo
wch
ar
t
o
f
h
y
b
r
id
DGH
-
B
W
O
alg
o
r
ith
m
is
s
h
o
wn
in
Fig
u
r
e
2
.
T
r
a
n
s
itio
n
f
r
o
m
DGH’
s
ex
p
ed
itio
n
p
h
ase
to
B
W
O’
s
r
ef
in
em
en
t
p
h
ase
allo
ws
th
e
alg
o
r
ith
m
to
m
ain
tain
a
b
alan
ce
d
s
ea
r
ch
.
C
h
ec
k
co
n
d
itio
n
s
tr
ateg
ically
d
ec
id
e
th
e
s
witch
b
etw
ee
n
th
e
p
h
ases
,
p
r
ev
en
tin
g
len
g
th
e
n
f
o
c
u
s
o
n
s
u
b
o
p
tim
a
l a
r
ea
s
.
3.
7
.
O
bje
c
t
iv
e
f
un
ct
io
n
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
f
o
r
clo
u
d
task
s
ch
ed
u
lin
g
is
m
o
d
elle
d
as:
(
)
=
1
×
(
1
−
)
+
2
×
(
1
÷
ℎ
ℎ
)
+
3
×
(
1
−
)
+
4
×
+
5
×
.
(
2
5
)
wh
er
e,
R
U
is
r
eso
u
r
ce
u
tili
za
t
io
n
,
T
GR
is
ta
s
k
g
u
ar
an
tee
r
atio
,
MRT
is
m
ea
n
r
esp
o
n
s
e
tim
e
,
C
E
is
co
n
s
u
m
ed
en
er
g
y
.
Ab
o
v
e,
1
,
2
,
3
,
4
,
5
,
ar
e
t
h
e
n
o
n
-
n
eg
ativ
e
weig
h
ts
.
′
′
(
)
,
s
ig
n
if
ies
a
s
o
lu
tio
n
v
ec
to
r
re
p
r
esen
tin
g
th
e
task
-
to
-
v
ir
t
u
a
l m
ac
h
in
e
ass
ig
n
m
en
t.
4.
E
XP
E
R
I
M
E
N
T
A
L
SE
T
UP
Py
th
o
n
p
latf
o
r
m
is
u
s
ed
to
test
th
e
p
r
o
p
o
s
ed
“Hy
b
r
id
D
GH
-
B
W
O”
task
s
ch
ed
u
lin
g
m
o
d
el.
Fo
r
ex
am
in
atio
n
,
f
o
llo
win
g
ass
u
m
p
tio
n
s
ar
e
co
n
s
id
er
ed
:
1)
T
h
e
ex
p
lo
r
er
s
co
u
n
t,
=
10
.
2)
Ma
x
im
al
co
u
n
t
o
f
r
e
p
etitio
n
s
,
=
250
.
3)
Fo
r
ch
ec
k
c
o
n
d
itio
n
s
in
(
1
)
,
th
e
two
o
d
d
p
r
im
e
n
u
m
b
e
r
s
tak
e
n
ar
e:
‘
A’
=7
an
d
‘
B
’
=9
.
4)
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
is
co
m
p
a
r
ed
with
DGO
[
1
4
]
,
E
SOA
[
2
0
]
,
B
W
O
[
9
]
,
an
d
W
aOA
[
2
1
]
.
5)
Fiv
e
d
if
f
er
en
t c
o
n
f
ig
u
r
atio
n
s
a
r
e
tak
en
f
o
r
s
er
v
e
r
s
’
an
d
task
s
’
to
b
e
s
ch
e
d
u
led
,
f
o
r
t
h
e
clo
u
d
en
v
i
r
o
n
m
en
t
an
d
is
s
h
o
wn
in
T
a
b
le
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
1
1
9
5
-
1
2
0
7
1202
Fig
u
r
e
2
.
Flo
wch
ar
t
o
f
h
y
b
r
i
d
DGH
-
B
W
O
alg
o
r
ith
m
T
ab
le
1
.
Fiv
e
d
i
f
f
er
en
t c
o
n
f
ig
u
r
atio
n
s
f
o
r
s
er
v
er
s
an
d
task
s
f
o
r
th
e
clo
u
d
en
v
ir
o
n
m
en
t
C
o
n
f
i
g
u
r
a
t
i
o
n
n
u
mb
e
r
(
C
N
)
S
e
r
v
e
r
s’
c
o
n
f
i
g
u
r
a
t
i
o
n
T
a
sk
s’
c
o
n
f
i
g
u
r
a
t
i
o
n
T
o
t
a
l
s
e
r
v
e
r
s
M
e
m
o
r
y
si
z
e
C
P
U
T
o
t
a
l
t
a
s
k
s
M
e
m
o
r
y
si
z
e
R
e
q
u
i
r
e
d
C
P
U
C
N
1
10
5
-
7
GB
1
5
0
-
180
GB
90
9
5
0
MB
-
1
.
9
GB
25
-
35
GB
C
N
2
25
13
-
18
GB
3
1
0
-
340
GB
1
8
0
4
-
8
GB
78
-
98
GB
C
N
3
45
30
-
34
GB
6
4
5
-
685
GB
2
8
5
11
-
14
GB
1
1
8
-
1
3
8
GB
C
N
4
73
60
-
65
GB
8
0
0
-
825
GB
3
8
5
18
-
23
GB
1
4
8
-
150
GB
C
N
5
95
78
-
83
GB
5
-
7
TB
4
9
0
28
-
32
GB
1
7
3
-
178
GB
5.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
W
e
p
er
f
o
r
m
ed
t
h
e
co
s
t
f
u
n
cti
o
n
,
an
d
p
er
f
o
r
m
a
n
ce
an
aly
s
is
o
n
th
e
b
asis
o
f
r
eso
u
r
ce
u
tili
z
atio
n
,
task
g
u
ar
an
tee
r
atio
,
s
ec
u
r
ity
an
d
th
r
o
u
g
h
p
u
t,
an
d
ar
e
d
is
cu
s
s
ed
in
s
u
b
s
ec
tio
n
5
.
1
a
n
d
5
.
2
r
esp
e
ctiv
ely
.
Su
b
s
ec
tio
n
5
.
3
s
h
o
ws
th
e
p
er
f
o
r
m
an
ce
an
aly
s
is
b
ased
o
n
co
n
s
u
m
ed
e
n
er
g
y
an
d
m
ea
n
r
esp
o
n
s
e
tim
e.
I
n
ad
d
itio
n
,
s
tatis
tical
an
aly
s
is
at
d
if
f
er
en
t
co
n
f
ig
u
r
atio
n
s
,
as
s
h
o
wn
in
T
ab
le
1
,
is
p
r
esen
ted
in
s
u
b
s
ec
tio
n
5
.
4
.
T
h
e
p
ap
e
r
u
s
es
g
r
ap
h
s
to
v
is
u
ally
co
m
p
ar
e
t
h
e
alg
o
r
i
th
m
s
.
T
h
e
g
r
ap
h
s
s
h
o
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
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7
52
In
d
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N1
Evaluation Warning : The document was created with Spire.PDF for Python.