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d
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1.
I
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D
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W
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ter
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I
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ap
p
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tr
i
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s
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O
f
f
lo
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in
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w
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tas
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to
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m
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b
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s
ed
to
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w
h
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n
co
m
p
ar
ed
to
clo
u
d
co
m
p
u
tin
g
[
1
]
,
[
2
]
.
Mo
b
ile
ed
g
e
co
m
p
u
ti
n
g
(
ME
C
)
h
a
s
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m
er
g
ed
as a
p
r
o
m
i
s
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n
g
ap
p
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b
r
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co
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clo
s
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to
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ev
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t
y
p
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it
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in
t
h
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s
s
n
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w
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p
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t
en
s
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v
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k
s
[
3
]
.
W
h
en
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g
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w
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w
ar
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a
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ag
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m
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co
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f
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at
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[
4
]
,
[
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
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tr
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Hyb
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n
efficien
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s
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fflo
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in
mo
b
ile
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g
e
…
(
F
a
tima
Z.
C
h
erh
a
b
il
)
515
Desp
ite
th
ese
ad
v
a
n
tag
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s
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e
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y
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a
m
ic
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l
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o
m
ial
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ti
m
e
h
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d
(
NP
-
h
ar
d
)
[
6
]
,
esp
ec
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w
h
en
t
h
e
n
u
m
b
er
o
f
u
s
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s
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cr
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.
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s
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m
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f
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ess
i
n
g
s
u
c
h
p
r
o
b
le
m
s
[
7
]
.
T
h
ey
ar
e
em
er
g
i
n
g
as
a
d
o
m
i
n
a
n
t
ap
p
r
o
ac
h
d
u
e
to
t
h
eir
ab
ilit
y
to
h
an
d
le
NP
-
h
ar
d
p
r
o
b
lem
s
.
Fo
r
i
n
s
ta
n
ce
,
g
en
e
t
ic
alg
o
r
ith
m
s
(
G
A
)
h
av
e
b
ee
n
u
s
ed
to
o
p
ti
m
ize
tr
an
s
m
i
s
s
io
n
p
o
w
er
a
n
d
e
x
ec
u
t
i
o
n
f
r
eq
u
e
n
c
y
[
8
]
an
d
to
m
i
n
i
m
ize
ta
s
k
o
v
er
h
ea
d
in
i
n
ter
n
et
o
f
v
e
h
icles
(
I
o
V
)
s
y
s
te
m
s
[
9
]
.
Z
h
u
an
d
W
en
[
1
0
]
,
an
im
p
r
o
v
ed
v
er
s
io
n
o
f
G
A
(
I
G
A
)
u
s
in
g
k
n
o
w
led
g
e
-
b
a
s
ed
cr
o
s
s
o
v
er
was
in
tr
o
d
u
ce
d
.
T
h
e
p
ap
er
f
o
cu
s
ed
o
n
co
m
p
ar
i
n
g
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
i
th
th
e
m
o
s
t
co
m
m
o
n
l
y
u
s
ed
b
en
ch
m
ar
k
s
,
w
h
ic
h
ar
e
all
lo
ca
l
an
d
all
o
f
f
lo
ad
ed
ex
ec
u
t
io
n
s
t
r
ateg
ies.
Ho
w
e
v
er
,
GAs o
f
te
n
s
u
f
f
er
f
r
o
m
h
ig
h
co
m
p
u
tat
io
n
al
co
m
p
le
x
it
y
[
1
1
]
.
A
b
i
n
ar
y
v
er
s
io
n
o
f
t
h
e
c
u
ck
o
o
s
ea
r
ch
al
g
o
r
ith
m
(
C
S)
w
a
s
p
r
o
p
o
s
ed
in
[
1
2
]
f
o
r
o
f
f
lo
ad
in
g
d
ec
is
io
n
-
m
ak
in
g
,
f
o
cu
s
in
g
o
n
m
i
n
i
m
izi
n
g
ti
m
e,
e
n
er
g
y
,
a
n
d
co
s
t.
T
h
e
s
tu
d
y
h
i
g
h
li
g
h
ted
h
o
w
m
u
l
tip
le
p
ar
am
e
ter
s
,
s
u
c
h
as
th
e
n
u
m
b
er
o
f
m
o
b
ile
d
ev
ic
es
an
d
tas
k
s
,
in
f
l
u
en
ce
th
e
e
f
f
ec
tiv
e
n
ess
o
f
t
h
e
o
f
f
lo
ad
in
g
p
r
o
ce
s
s
.
Me
an
w
h
ile
,
A
b
b
as
et
a
l.
[
1
3
]
co
m
p
ar
ed
g
r
e
y
w
o
lv
e
s
o
p
ti
m
izatio
n
(
G
W
O)
,
an
t
co
lo
n
y
o
p
ti
m
izatio
n
(
AC
O)
an
d
w
h
ale
o
p
tim
izatio
n
a
lg
o
r
it
h
m
(
W
OA
)
to
f
i
n
d
an
o
p
ti
m
al
s
elec
ti
o
n
o
f
o
f
f
lo
ad
in
g
ta
s
k
s
.
Si
m
u
l
atio
n
r
es
u
lts
s
h
o
w
ed
th
at
t
h
e
p
er
f
o
r
m
an
ce
o
f
GW
O
is
r
elati
v
el
y
m
u
c
h
b
etter
t
h
a
n
AC
O
a
n
d
W
O
A
.
Ho
w
e
v
er
,
b
o
th
w
o
r
k
s
f
o
c
u
s
ed
th
eir
ex
p
er
i
m
e
n
tal
test
s
o
n
l
y
o
n
an
en
v
ir
o
n
m
en
t
o
f
a
s
in
g
le
e
d
g
e
n
o
d
e
an
d
a
n
u
m
b
er
o
f
en
d
-
d
ev
ice
s
.
A
r
ti
f
icial
b
ee
co
lo
n
y
(
A
B
C
)
alg
o
r
it
h
m
[
1
4
]
h
av
e
d
em
o
n
s
tr
ated
ef
f
e
ctiv
e
n
ess
i
n
b
alan
cin
g
late
n
c
y
a
n
d
en
er
g
y
i
n
a
p
r
o
p
o
s
ed
th
r
ee
-
tier
ed
g
e
-
c
lo
u
d
in
te
g
r
atio
n
f
r
a
m
e
w
o
r
k
.
P
ar
ticle
s
w
ar
m
o
p
ti
m
iza
tio
n
(
P
SO)
w
a
s
u
s
ed
i
n
[
1
5
]
to
ad
d
r
ess
task
d
ep
en
d
en
cie
s
in
j
o
b
-
d
iv
id
ed
co
m
p
u
tatio
n
o
f
f
lo
ad
in
g
w
it
h
m
u
ltip
le
u
s
er
s
an
d
ME
C
s
er
v
er
s
a
n
d
m
u
lti
-
p
o
p
u
latio
n
co
o
p
er
ativ
e
elite
al
g
o
r
ith
m
(
M
C
E
-
P
SO)
.
A
b
in
ar
y
v
er
s
io
n
o
f
P
SO
w
as
also
ap
p
lied
in
[
1
6
]
f
o
r
r
eso
u
r
ce
allo
ca
tio
n
a
n
d
o
f
f
lo
ad
in
g
s
tr
ate
g
y
o
p
ti
m
izati
o
n
in
m
u
lti
-
tier
m
u
lti
-
MEC
-
s
er
v
er
ar
ch
itect
u
r
es
w
it
h
i
n
5
G
h
eter
o
g
e
n
eo
u
s
n
et
wo
r
k
s
.
W
OA
,
k
n
o
w
n
f
o
r
its
ex
p
l
o
itatio
n
ca
p
ab
ilit
ies,
h
as
b
ee
n
in
te
g
r
ated
w
it
h
o
th
e
r
ev
o
lu
tio
n
ar
y
tech
n
iq
u
es
to
en
h
a
n
ce
p
er
f
o
r
m
an
ce
.
L
i
et
a
l.
[
1
7
]
,
W
OA
was
i
n
teg
r
ated
w
i
th
d
if
f
er
en
tial
ev
o
lu
tio
n
(
DE
)
a
n
d
i
m
m
u
n
e
s
y
s
te
m
to
i
m
p
r
o
v
e
th
e
s
ea
r
ch
i
n
g
s
tr
ate
g
y
o
f
t
h
e
w
h
ale
an
d
en
h
a
n
ce
th
e
ef
f
icie
n
c
y
o
f
d
ep
en
d
en
t
ta
s
k
o
f
f
lo
ad
in
g
in
ME
C
en
v
ir
o
n
m
en
ts
,
w
h
ile
in
[
1
8
]
au
th
o
r
s
d
e
co
m
p
o
s
ed
th
e
p
r
o
b
lem
o
f
co
m
p
u
tat
io
n
o
f
f
lo
ad
in
g
in
n
o
n
-
o
r
th
o
g
o
n
al
m
u
ltip
le
ac
ce
s
s
(
NOM
A
)
b
ased
ME
C
s
y
s
te
m
in
to
s
u
b
-
p
r
o
b
le
m
s
.
T
h
e
y
s
o
lv
ed
t
h
e
m
u
s
i
n
g
co
n
v
e
x
o
p
ti
m
izatio
n
an
d
th
e
u
s
e
o
f
a
g
r
ad
ien
t
-
f
r
ee
s
w
ar
m
i
n
tel
lig
e
n
ce
ap
p
r
o
ac
h
o
f
W
OA
.
Ho
w
e
v
er
,
th
e
s
i
m
u
lat
io
n
s
w
er
e
co
n
d
u
cted
w
it
h
a
s
i
n
g
le
s
er
v
er
an
d
a
v
e
r
y
s
m
all
n
u
m
b
er
o
f
u
s
er
s
(
5
-
2
5
)
.
Z
h
an
g
an
d
T
u
o
[
1
9
]
,
au
th
o
r
s
h
ad
m
er
g
ed
W
OA
w
it
h
L
év
y
f
li
g
h
t
an
d
G
W
O
to
i
m
p
r
o
v
e
th
e
p
o
p
u
lat
i
o
n
in
i
tializatio
n
an
d
alp
h
a
-
w
o
l
f
s
e
lectio
n
s
tep
s
o
f
th
e
GW
O
al
g
o
r
ith
m
.
T
h
e
s
y
s
t
e
m
u
s
ed
a
m
u
lti
-
s
er
v
er
an
d
m
u
lti
-
u
s
er
v
e
h
ic
u
lar
task
o
f
f
lo
ad
in
g
w
it
h
a
s
elec
t
i
v
e
o
f
f
lo
a
d
in
g
,
w
h
ic
h
i
s
,
i
n
t
h
eir
ca
s
e,
t
h
e
ab
ilit
y
to
e
x
ec
u
te
th
e
tas
k
lo
ca
ll
y
,
o
n
ed
g
e
s
er
v
er
,
o
r
an
id
le
v
eh
ic
le.
Oth
er
e
v
o
lu
tio
n
ar
y
a
n
d
s
war
m
i
n
telli
g
e
n
ce
alg
o
r
it
h
m
s
h
a
v
e
b
ee
n
e
x
p
lo
r
ed
.
Go
r
illa
tr
o
o
p
s
o
p
tim
izatio
n
(
GT
O)
w
a
s
p
r
o
p
o
s
ed
an
d
i
m
p
r
o
v
ed
i
n
[
2
0
]
to
s
o
lv
e
t
h
e
d
ep
en
d
e
n
t
tas
k
-
o
f
f
lo
ad
in
g
p
r
o
b
le
m
i
n
a
m
u
lti
-
s
er
v
er
ME
C
en
v
ir
o
n
m
en
t
w
i
th
th
e
s
a
m
e
t
h
r
ee
o
b
j
ec
tiv
es,
n
a
m
el
y
,
t
h
e
co
m
p
l
etio
n
ti
m
e,
e
n
er
g
y
co
n
s
u
m
p
tio
n
,
an
d
m
o
n
etar
y
co
s
t.
Fu
r
th
er
m
o
r
e,
b
io
g
eo
g
r
ap
h
y
-
b
ased
o
p
ti
m
izatio
n
(
B
B
O)
w
as
e
m
p
lo
y
ed
in
[
2
1
]
to
s
o
lv
e
th
e
tas
k
o
f
f
lo
ad
i
n
g
i
s
s
u
es
f
o
r
ed
g
e
s
er
v
er
s
a
n
d
co
n
s
id
er
in
g
th
e
ce
n
tr
al
clo
u
d
.
I
t
is
w
o
r
th
n
o
tin
g
t
h
at
th
e
ar
ch
itectu
r
al
s
et
u
p
s
d
if
f
er
,
m
ak
i
n
g
d
ir
ec
t
n
u
m
er
ical
c
o
m
p
ar
is
o
n
s
ch
alle
n
g
i
n
g
ev
en
f
o
r
ex
p
er
im
en
tal
ev
al
u
atio
n
.
A
s
s
u
m
m
ar
ized
in
T
ab
le
1
,
th
e
m
aj
o
r
it
y
o
f
p
r
io
r
w
o
r
k
s
o
p
tim
ized
o
n
l
y
e
n
er
g
y
a
n
d
d
ela
y
o
b
j
ec
tiv
es
an
d
t
h
e
y
d
id
n
o
t
co
n
s
id
er
th
e
p
a
y
m
e
n
t
co
s
t
o
b
j
ec
tiv
e.
T
h
e
y
u
s
ed
a
b
in
ar
y
o
f
f
lo
ad
in
g
,
w
h
ic
h
m
ak
e
s
it
h
ar
d
er
to
co
m
p
ar
e
w
it
h
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
th
at
u
s
e
s
a
p
ar
ti
al
o
f
f
lo
ad
in
g
.
T
h
is
ap
p
r
o
ac
h
en
ab
les
o
p
tim
izatio
n
o
f
all
t
h
e
o
b
j
ec
tiv
es
b
y
ex
p
lo
itin
g
t
h
e
b
en
ef
its
o
f
b
o
th
s
id
es,
ed
g
e
s
er
v
er
s
a
n
d
en
d
-
d
ev
ice
s
.
Mo
r
eo
v
er
,
w
h
ile
ef
f
ec
ti
v
e,
t
h
e
ab
o
v
e
s
t
u
d
ies
o
f
te
n
s
tr
u
g
g
le
w
ith
ex
p
lo
r
atio
n
-
ex
p
lo
itatio
n
b
alan
ce
o
r
p
r
em
a
tu
r
e
c
o
n
v
er
g
e
n
ce
,
esp
ec
iall
y
i
n
m
u
lti
-
o
b
j
ec
tiv
e
co
n
tex
ts
.
T
h
u
s
,
th
er
e
is
s
till
a
n
ee
d
f
o
r
an
al
g
o
r
ith
m
t
h
at
e
f
f
ec
tiv
el
y
b
alan
ce
s
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
w
h
i
le
s
i
m
u
lta
n
eo
u
s
l
y
o
p
ti
m
izi
n
g
all
th
r
ee
p
er
f
o
r
m
a
n
ce
m
etr
ic
s
.
T
h
e
cu
r
r
en
t
w
o
r
k
d
i
s
ti
n
g
u
i
s
h
es
its
e
lf
b
y
p
r
o
p
o
s
in
g
a
n
o
v
el
h
y
b
r
id
al
g
o
r
ith
m
th
at
co
m
b
in
es
t
h
e
s
tr
en
g
th
s
o
f
P
SO
an
d
W
OA
,
e
n
h
a
n
ce
d
w
it
h
cr
o
s
s
o
v
er
,
m
u
ta
tio
n
,
an
d
L
é
v
y
f
li
g
h
t
(
C
M
L
)
o
p
er
ato
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Evaluation Warning : The document was created with Spire.PDF for Python.
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24
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20
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[
8
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GA
✓
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[
9
]
GA
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B
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[
1
0
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GA
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1
2
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C
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1
3
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4
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A
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1
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1
6
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2
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T
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Evaluation Warning : The document was created with Spire.PDF for Python.
T
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[
8
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.
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1
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h
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b
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ee
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s
m
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a
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s
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p
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G
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t
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p
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[
6
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,
ex
ac
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m
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h
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s
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e
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p
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ac
tical
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o
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e
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s
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s
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h
u
s
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m
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s
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ith
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it
h
i
n
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ea
s
o
n
ab
le
c
o
m
p
u
tatio
n
ti
m
e.
2
.
5
.
P
r
o
po
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ed
t
a
s
k
o
f
f
lo
a
din
g
a
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s
o
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lg
o
rit
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m
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o
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tio
n
ar
y
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h
es,
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ter
to
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e
n
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h
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d
o
f
an
o
p
ti
m
al
s
o
lu
tio
n
a
n
d
ca
n
b
e
v
er
y
ef
f
ec
ti
v
e
if
th
e
d
o
m
ai
n
k
n
o
w
led
g
e
is
ex
p
lo
ited
[
2
4
]
.
Am
o
n
g
t
h
e
m
W
O
A
,
w
h
ic
h
is
in
s
p
ir
ed
b
y
th
e
b
u
b
b
le
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n
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n
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s
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ate
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ales,
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ate
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y
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n
d
f
in
e
-
t
u
n
ed
e
x
p
lo
itatio
n
[
2
5
]
.
On
t
h
e
o
th
er
h
a
n
d
,
P
SO
e
m
u
lates
th
e
co
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r
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f
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ir
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lo
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is
h
s
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h
o
o
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g
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w
h
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e
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(
s
o
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s
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s
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ased
o
n
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er
s
o
n
al
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g
r
o
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ests
,
f
ac
ilit
ati
n
g
e
f
f
icie
n
t
s
ea
r
ch
e
s
[
2
6
]
.
T
h
e
h
y
b
r
id
P
SO
-
W
OA
(
C
M
L
)
alg
o
r
it
h
m
co
m
b
in
es
P
SO
’
s
s
o
cial
lear
n
i
n
g
an
d
W
OA
’
s
s
p
ir
al/e
n
cir
cli
n
g
to
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alan
ce
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
.
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t
m
ai
n
tai
n
s
d
iv
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s
it
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m
u
tatio
n
/cr
o
s
s
o
v
er
an
d
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es
l
o
ca
l
o
p
tim
a
u
s
in
g
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S
L
é
v
y
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lig
h
t
j
am
p
s
,
w
h
ile
d
y
n
a
m
icall
y
ad
ap
ts
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ar
am
e
ter
s
f
o
r
co
n
v
er
g
e
n
c
e
s
p
ee
d
.
E
ac
h
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ar
ticle
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u
p
d
ated
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s
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o
th
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SO
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d
W
OA
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ato
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s
,
f
o
llo
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b
y
C
M
L
ev
o
l
u
tio
n
ar
y
o
p
er
at
o
r
s
(
m
u
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n
,
cr
o
s
s
o
v
er
,
L
év
y
f
li
g
h
t)
p
r
o
b
a
b
ilis
ticall
y
.
T
h
e
alg
o
r
it
h
m
w
o
r
k
f
lo
w
is
s
u
m
m
ar
ized
as
f
o
llo
w
s
.
2
.
5
.
1
.
I
nitia
liza
t
io
n a
nd
s
o
lutio
n r
epre
s
en
t
a
t
io
n
T
h
e
p
r
o
ce
s
s
b
eg
in
s
b
y
i
n
itial
izin
g
a
p
o
p
u
latio
n
o
f
p
ar
ticles
an
d
v
elo
cities.
E
ac
h
p
ar
tic
le
r
ep
r
esen
ts
a
p
o
ten
tial
s
o
lu
t
io
n
an
d
is
co
m
p
o
s
ed
o
f
t
w
o
d
i
s
tin
c
t
co
m
p
o
n
en
t
s
,
r
ef
lecti
n
g
th
e
m
i
x
ed
-
v
ar
iab
le
n
atu
r
e
o
f
t
h
e
p
r
o
b
le
m
:
−
Of
f
lo
ad
in
g
d
ec
is
io
n
s
(
alp
h
a)
:
a
co
n
tin
u
o
u
s
v
ec
to
r
.
=
(
1
,
…
,
)
,
w
h
er
e
∈
[
0
,
1
]
r
ep
r
esen
ts
t
h
e
p
o
r
tio
n
o
f
task
to
b
e
o
f
f
lo
ad
ed
.
−
Ser
v
er
ass
ig
n
m
en
ts
(
b
eta)
:
a
d
is
cr
ete
v
ec
to
r
.
=
(
1
,
…
,
)
,
w
h
er
e
∈
{
1
,
.
.
.
,
}
is
th
e
in
te
g
er
id
en
ti
f
ier
o
f
th
e
ME
C
s
er
v
er
a
s
s
i
g
n
ed
to
tas
k
.
T
h
is
ex
p
licit
s
ep
ar
atio
n
allo
ws
f
o
r
th
e
co
r
r
ec
t
ap
p
licatio
n
o
f
co
n
tin
u
o
u
s
an
d
d
is
cr
ete
o
p
ti
m
izat
io
n
o
p
er
ato
r
s
to
th
e
r
esp
ec
tiv
e
p
ar
ts
o
f
t
h
e
s
o
lu
t
io
n
.
2
.
5
.
2
.
H
y
bridi
za
t
io
n str
a
t
eg
y
E
ac
h
p
ar
ticle
’
s
v
e
lo
cit
y
is
ad
j
u
s
ted
b
y
co
n
s
id
er
i
n
g
P
SO
th
r
ee
co
m
p
o
n
e
n
ts
[
2
6
]
:
th
e
in
f
l
u
en
ce
o
f
its
p
r
ev
io
u
s
v
e
lo
cit
y
(
)
,
its
p
er
s
o
n
al
b
est
p
o
s
itio
n
(
)
,
an
d
th
e
g
lo
b
al
b
est
p
o
s
itio
n
(
)
.
T
h
is
is
ex
p
r
ess
ed
as:
(
+
1
)
=
.
(
)
+
1
.
1
.
(
−
(
)
)
+
2
.
2
.
(
−
(
)
)
(
1
3
)
w
h
er
e:
is
t
h
e
in
er
t
ia
w
e
ig
h
t
,
c
1
co
g
n
itiv
e
f
ac
to
r
,
c
2
s
o
cial
f
ac
to
r
,
an
d
b
o
th
(
1
,
2
)
ar
e
r
an
d
o
m
n
u
m
b
er
s
i
n
[
0
,
1
]
to
in
tr
o
d
u
ce
s
to
ch
as
tic
b
eh
a
v
i
o
r
.
T
h
e
p
a
r
ticle
’
s
p
o
s
itio
n
is
th
e
n
u
p
d
ated
u
s
in
g
W
O
A
-
in
s
p
ir
ed
m
ec
h
an
i
s
m
s
[
2
5
]
,
s
elec
ted
b
ased
o
n
a
r
an
d
o
m
v
al
u
e
∈
[
0
,
1
]
:
−
Sh
r
i
n
k
i
n
g
en
cir
cli
n
g
m
ec
h
a
n
is
m
(
if
<
0
.
5
)
: u
p
d
ate
th
e
p
o
s
itio
n
u
s
i
n
g
t
h
e
f
o
r
m
u
la:
(
)
=
−
⋅
(
1
4
)
w
h
er
e
=
2
⋅
−
is
a
co
n
tr
o
l
p
ar
a
m
eter
,
=
|
⋅
−
(
)
|
,
=
2
an
d
d
ec
r
ea
s
es
f
r
o
m
2
to
0
o
v
er
iter
atio
n
s
.
−
Sp
ir
al
u
p
d
atin
g
Me
ch
a
n
is
m
(
i
f
≥
0
.
5
)
: u
s
e
th
e
s
p
ir
al
eq
u
atio
n
to
u
p
d
ate
th
e
p
o
s
itio
n
:
(
)
=
′
.
.
(
2
)
+
(
1
5
)
w
h
er
e
′
=
|
−
(
)
|
,
is
a
co
n
s
ta
n
t,
an
d
is
a
r
an
d
o
m
n
u
m
b
er
i
n
[
−1
,
1
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
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tr
o
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Hyb
r
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P
S
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-
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a
p
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ch
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o
r
a
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efficien
t ta
s
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o
fflo
a
d
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n
g
in
mo
b
ile
ed
g
e
…
(
F
a
tima
Z.
C
h
erh
a
b
il
)
519
Nex
t,
th
e
u
p
d
ated
v
elo
cit
y
i
s
a
p
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lied
to
f
u
r
th
er
r
ef
i
n
e
th
e
p
ar
ticle
’
s
p
o
s
itio
n
:
(
+
1
)
=
(
)
+
(
+
1
)
(
1
6
)
2
.
5
.
3
.
C
M
L
enha
nce
m
ent
o
p
er
a
t
o
rs
T
o
en
h
an
ce
th
e
s
ea
r
c
h
s
p
ac
e,
th
e
alg
o
r
ith
m
f
u
r
t
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er
i
m
p
r
o
v
e
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lu
tio
n
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p
ac
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ex
p
lo
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atio
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y
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p
l
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n
g
o
n
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th
e
t
h
r
ee
C
M
L
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er
ato
r
s
b
ased
o
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p
r
ed
ef
in
ed
p
r
o
b
ab
il
ities
(
_
,
_
,
an
d
_
):
−
C
r
o
s
s
o
v
er
:
co
m
b
i
n
es
t
h
e
p
o
s
itio
n
s
o
f
t
w
o
p
ar
ticles
to
g
e
n
er
ate
n
e
w
ca
n
d
id
ate
s
o
lu
t
io
n
s
u
s
i
n
g
b
len
d
cr
o
s
s
o
v
er
f
o
r
th
e
co
n
ti
n
u
o
u
s
α
v
ec
to
r
s
an
d
a
u
n
i
f
o
r
m
cr
o
s
s
o
v
er
f
o
r
d
is
cr
ete
v
ec
to
r
s
.
−
Mu
tatio
n
:
s
li
g
h
tl
y
m
o
d
if
ie
s
a
p
ar
ticle
’
s
p
o
s
itio
n
to
ex
p
lo
r
e
lo
ca
l
r
eg
io
n
s
b
y
u
s
i
n
g
Ga
u
s
s
ia
n
m
u
tatio
n
to
v
ec
to
r
an
d
r
an
d
o
m
-
r
e
s
e
t
m
u
ta
tio
n
f
o
r
v
ec
to
r
.
−
L
é
v
y
f
li
g
h
t:
en
ab
li
n
g
o
cc
asio
n
al
lo
n
g
-
d
is
tan
ce
j
u
m
p
s
i
n
th
e
s
ea
r
ch
s
p
ac
e
to
h
elp
th
e
alg
o
r
ith
m
e
s
ca
p
in
g
f
r
o
m
lo
ca
l o
p
ti
m
a.
T
h
u
s
,
a
L
é
v
y
s
tep
is
ca
lcu
lated
an
d
ad
d
ed
to
th
e
v
ec
to
r
.
2
.
5
.
4
.
Dy
na
m
ic
pa
ra
m
et
er
t
un
ing
T
o
av
o
id
s
tatic
b
eh
a
v
io
r
ac
r
o
s
s
g
e
n
er
atio
n
s
,
k
e
y
P
SO
p
ar
a
m
eter
s
ar
e
ad
ap
ted
d
y
n
a
m
ical
l
y
o
v
er
th
e
iter
atio
n
s
:
−
T
h
e
in
er
tia
w
ei
g
h
t
is
li
n
ea
r
l
y
d
ec
r
ea
s
ed
u
s
i
n
g
a
d
a
m
p
i
n
g
v
alu
e
d
to
s
h
i
f
t
th
e
s
ea
r
ch
f
r
o
m
ex
p
lo
r
atio
n
to
ex
p
lo
itatio
n
.
−
T
h
e
co
g
n
itiv
e
1
is
g
r
ad
u
all
y
d
e
cr
ea
s
ed
w
h
i
le
th
e
s
o
cial
co
ef
f
icie
n
t
2
is
in
cr
ea
s
ed
to
s
h
i
f
t
t
h
e
s
ea
r
ch
f
o
cu
s
f
r
o
m
i
n
d
i
v
id
u
al
e
x
p
er
ien
ce
s
to
g
lo
b
al
lear
n
i
n
g
.
2
.
5
.
5
.
F
it
nes
s
ev
a
lua
t
io
n a
nd
o
pti
m
a
l so
lutio
n
up
da
t
e
Du
r
in
g
e
v
er
y
iter
atio
n
,
b
o
u
n
d
ar
y
co
n
s
tr
ai
n
ts
f
o
r
ar
e
en
f
o
r
ce
d
b
y
cli
p
p
in
g
v
al
u
es
to
t
h
e
r
an
g
e
[
0
,
1
]
,
w
h
i
le
v
alu
es
ar
e
r
o
u
n
d
ed
to
th
e
n
ea
r
est
in
te
g
er
an
d
clip
p
ed
to
th
e
r
an
g
e
[
0
,
M]
.
T
h
en
,
th
e
f
it
n
es
s
o
f
ea
ch
n
e
w
p
ar
ticle
is
e
v
al
u
ated
b
ased
o
n
th
e
t
h
r
ee
o
b
j
ec
tiv
es
(
en
er
g
y
,
d
ela
y
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a
n
d
m
o
n
e
tar
y
co
s
t)
w
ith
w
eig
h
t
s
s
et
r
e
s
p
ec
tiv
e
l
y
a
s
0
.
3
5
,
0
.
3
5
,
an
d
0
.
3
.
T
h
en
,
th
e
an
d
v
alu
es
ar
e
u
p
d
ated
u
n
t
il
a
m
ax
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
is
r
ea
ch
e
d
.
2
.
5
.
6
.
P
s
eudo
co
de
o
f
t
he
pro
po
s
ed
a
lg
o
rit
h
m
T
h
e
co
m
p
lete
s
tr
u
ct
u
r
e
o
f
P
SO
-
W
O
A
(
C
M
L
)
al
g
o
r
ith
m
is
s
u
m
m
ar
ized
i
n
th
e
p
s
e
u
d
o
co
d
e
b
elo
w
:
−
I
n
p
u
ts
:
task
p
ar
a
m
eter
s
(
,
,
)
,
d
ev
ice/ser
v
er
ca
p
ac
ities
(
,
)
,
an
d
n
et
w
o
r
k
co
n
d
itio
n
s
(
,
ℎ
,
σ
2
)
.
−
Ou
tp
u
ts
:
o
p
ti
m
al
tas
k
o
f
f
lo
ad
in
g
d
ec
is
io
n
(
v
ec
to
r
∗
)
an
d
s
er
v
er
ass
ig
n
m
e
n
t
s
(
v
ec
to
r
∗
)
m
in
i
m
izi
n
g
en
er
g
y
,
d
ela
y
,
an
d
m
o
n
etar
y
c
o
s
t.
A
l
g
o
r
ith
m
1
.
P
SO
-
W
O
A
(
C
M
L
)
f
o
r
ME
C
ta
s
k
o
f
f
lo
ad
i
n
g
1.
//
-
-
-
I
n
itializa
tio
n
-
-
-
2.
I
n
itialize
p
o
p
u
latio
n
(
P
)
o
f
n
P
o
p
p
ar
ticles w
ith
r
a
n
d
o
m
α
a
n
d
β v
ec
to
r
s
3.
I
n
itialize
e
m
p
t
y
v
elo
cit
y
v
ec
to
r
s
f
o
r
ea
ch
p
ar
ticle
4.
FOR
ea
c
h
p
ar
ticle
Xi
in
P
DO
5.
C
alcu
late
f
it
n
e
s
s
F(X
i)
=
(
0
.
3
5
*
T
o
tal_
E
n
er
g
y
+
0
.
3
5
*
T
o
tal_
Dela
y
+
0
.
3
*
T
o
tal_
C
o
s
t)
6.
I
n
itialize
p
er
s
o
n
al
b
est p
B
est_
i
w
ith
X
i a
n
d
F(X
i)
7.
E
ND
FOR
8.
Fin
d
g
lo
b
al
b
est (
g
B
est)
p
ar
ticle
f
r
o
m
P
// th
e
m
in
i
m
u
m
v
a
lu
e
o
f
F(X
i)
9.
//
-
-
-
Ma
i
n
L
o
o
p
-
-
-
10.
FOR
t =
1
T
O
Ma
x
I
ter
DO
11.
FOR
ea
ch
p
ar
ticle
Xi
in
P
DO
12.
//
-
-
-
C
o
r
e
h
y
b
r
id
u
p
d
at
e
-
-
-
13.
//
--
C
alc
u
late
P
SO
v
el
o
cit
y
v
ec
to
r
--
14.
Vi(
t
)
=
ca
lcu
late_
p
s
o
_
v
elo
cit
y
(
X
i,
p
B
est_
i,
g
B
est,
w
,
c1
,
c2
)
w
ith
eq
.
1
3
15.
//
--
W
O
A
s
tr
ate
g
ic
r
ep
o
s
itio
n
i
n
g
--
16.
r
=
r
an
d
(
)
17.
I
F r
<
0
.
5
T
HE
N
18.
Xi_
n
e
w
=
s
h
r
i
n
k
i
n
g
_
en
cir
cli
n
g
(
X
i,
g
B
est,
a)
u
s
i
n
g
eq
.
1
4
19.
E
L
SE
20.
Xi_
n
e
w
=
s
p
ir
al_
u
p
d
atin
g
m
ec
h
a
n
i
s
m
(
X
i,
g
B
est,
a)
u
s
in
g
eq
.
1
5
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
2
,
A
p
r
il
20
26
:
5
1
4
-
526
520
21.
E
NDI
F
22.
//
--
Fi
n
al
p
o
s
itio
n
u
p
d
ate
w
it
h
P
SO
v
e
lo
cit
y
(
R
e
f
i
n
e
m
en
t)
--
23.
Xi
=
Xi_
n
e
w
+
V
i(
t)
24.
//
---
C
M
L
en
h
a
n
ce
m
e
n
t o
p
er
ato
r
s
-
-
-
25.
r
=
r
an
d
(
)
26.
I
F r
<
p
_
Mu
t T
HE
N
27.
A
p
p
l
y
m
u
tatio
n
to
Xi
(
Gau
s
s
ian
f
o
r
α
,
r
an
d
o
m
-
r
e
s
et
f
o
r
β)
28.
E
L
SE
I
F r
<
p
_
Mu
t +
p
_
C
r
o
s
s
T
HE
N
29.
S
elec
t t
w
o
p
ar
en
ts
P
1
,
P2
f
r
o
m
P
30.
A
p
p
l
y
c
r
o
s
s
o
v
er
to
Xi,
P
1
,
P
2
(
b
len
d
f
o
r
α
,
u
n
i
f
o
r
m
f
o
r
β)
31.
E
L
SE
32.
A
p
p
l
y
L
é
v
y
f
lig
h
t to
th
e
α
v
ec
to
r
o
f
Xi
33.
E
ND
I
F
34.
//
-
-
-
E
v
al
u
atio
n
a
n
d
u
p
d
ates
-
-
-
35.
E
n
f
o
r
ce
b
o
u
n
d
ar
y
co
n
s
tr
ain
ts
o
n
Xi.
α
a
n
d
Xi.
β
36.
E
v
alu
ate
f
i
tn
e
s
s
F(X
i)
37.
I
F F(
Xi)
<
F(p
B
est_
i)
T
HE
N
p
B
est_
i =
Xi
38.
I
F F(
Xi)
<
F(g
B
est)
T
HE
N
g
B
est =
Xi
39.
E
ND
FOR
40.
//
---
D
y
n
a
m
ic
p
ar
a
m
eter
t
u
n
i
n
g
---
41.
Dec
r
ea
s
e
w
,
c1
,
an
d
in
cr
ea
s
e
c2
42.
E
ND
FOR
43.
R
E
T
UR
N
g
B
est // T
h
e
o
p
ti
m
a
l so
lu
tio
n
2
.
5
.
7
.
Co
m
ple
x
it
y
a
na
ly
s
is
L
et
b
e
n
u
m
b
er
o
f
u
s
er
s
,
n
u
m
b
er
o
f
s
er
v
er
s
,
p
o
p
u
latio
n
s
ize
,
an
d
n
u
m
b
er
o
f
iter
atio
n
s
.
a.
Fit
n
e
s
s
e
v
alu
a
tio
n
(
p
er
p
ar
ticle)
:
a
n
aiv
e
ca
lc
u
latio
n
o
f
th
e
f
itn
es
s
f
u
n
ctio
n
i
n
(
1
1
)
is
d
o
m
i
n
ated
b
y
t
w
o
co
m
p
o
n
e
n
t
s
:
−
C
alcu
lati
n
g
a
ll
tr
an
s
m
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s
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ates
(
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w
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r
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in
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n
(
2
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co
m
p
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y
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−
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if
y
in
g
th
e
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er
v
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lo
ad
co
n
s
tr
ai
n
t
(
C
3
)
,
w
h
ich
,
i
f
i
m
p
l
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m
en
ted
b
y
c
h
ec
k
i
n
g
ea
ch
o
f
th
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s
er
v
er
s
,
r
eq
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ir
es s
u
m
m
i
n
g
u
s
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lo
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s
,
r
esu
ltin
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n
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×
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m
p
lex
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y
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I
n
o
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r
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m
p
le
m
en
tatio
n
,
w
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r
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d
u
ce
th
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s
co
s
t
s
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g
n
if
ican
tl
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with
t
w
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p
ti
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s
.
F
i
r
s
t,
t
h
e
(
2
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in
ter
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ce
ter
m
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(
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d
th
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in
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p
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f
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ter
f
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1
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s
u
b
tr
ac
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co
n
d
,
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(
×
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lo
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-
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en
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p
d
atin
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in
a
s
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g
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p
ass
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all
N
u
s
er
s
(
(
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o
n
s
eq
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t
l
y
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h
e
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ti
m
ized
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it
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ev
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lu
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t
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i
n
cl
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d
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g
al
l
o
b
j
ec
tiv
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an
d
co
n
s
tr
ain
t
c
h
ec
k
s
)
f
o
r
a
s
in
g
le
p
ar
ticle
is
(
+
(
+
)
)
=
(
+
)
.
Giv
en
th
at
≪
in
o
u
r
s
ce
n
ar
io
s
,
th
is
co
s
t
s
i
m
p
li
f
ie
s
to
(
)
.
b.
P
SO/W
OA
u
p
d
ates
: u
p
d
atin
g
v
elo
cities a
n
d
p
o
s
itio
n
s
o
f
all
p
ar
ticles
tak
es
(
⋅
)
.
c.
C
M
L
o
p
er
ato
r
s
:
m
u
tatio
n
,
cr
o
s
s
o
v
er
,
an
d
L
év
y
f
li
g
h
t
m
o
d
i
f
y
p
ar
ticle
p
o
s
iti
o
n
s
an
d
th
e
y
r
eq
u
ir
e
(
⋅
)
.
d.
Up
d
atin
g
b
est p
o
s
itio
n
s
:
(
)
e.
T
o
tal
co
m
p
lex
it
y
:
c
o
n
s
id
er
in
g
all
i
ter
atio
n
s
,
t
h
e
p
r
ac
tical
co
m
p
u
tatio
n
al
co
m
p
le
x
it
y
o
f
t
h
e
al
g
o
r
ith
m
is
(
⋅
⋅
)
.
Fu
r
th
er
m
o
r
e,
s
ca
lab
ilit
y
to
v
er
y
lar
g
e
-
s
ca
le
s
y
s
te
m
s
ca
n
b
e
i
m
p
r
o
v
ed
t
h
r
o
u
g
h
p
ar
allelize
d
ev
alu
atio
n
,
as
f
it
n
es
s
co
m
p
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tatio
n
s
f
o
r
p
ar
ticles
ar
e
in
d
ep
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d
en
t.
P
ar
am
eter
tu
n
i
n
g
also
ca
n
r
ed
u
ce
co
n
s
tan
ts
.
f.
Me
m
o
r
y
co
m
p
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x
it
y
:
t
h
e
alg
o
r
ith
m
m
u
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t sto
r
e
th
e
p
o
s
itio
n
,
v
elo
cit
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an
d
f
o
r
all
p
ar
ticles.
Sin
ce
ea
c
h
p
ar
ticle
’
s
r
ep
r
esen
t
atio
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(
an
d
v
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to
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s
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is
o
f
s
ize
N
,
th
e
to
tal
m
e
m
o
r
y
co
m
p
lex
it
y
is
(
⋅
)
,
w
h
ic
h
r
e
m
ai
n
s
m
o
d
est
f
o
r
ty
p
ica
l M
E
C
s
et
tin
g
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
er
f
o
r
m
an
ce
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f
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i
x
s
tate
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of
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h
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ar
t
m
eta
h
e
u
r
is
tic
al
g
o
r
ith
m
s
(
G
A
s
[
8
]
,
[
9
]
,
[
1
0
]
,
C
S
[
1
2
]
,
A
B
C
[
1
4
]
,
GW
O
[
1
3
]
,
P
SO
[
1
5
]
,
an
d
W
OA
[
2
5
]
)
is
ev
al
u
at
ed
an
d
co
m
p
ar
ed
ag
ain
s
t
t
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
alg
o
r
ith
m
.
T
h
e
o
b
j
ec
tiv
e
is
to
m
i
n
i
m
ize
a
co
m
p
o
s
i
te
co
s
t
(
C
-
C
o
s
t
)
f
u
n
ctio
n
th
at
in
co
r
p
o
r
ates
en
er
g
y
co
n
s
u
m
p
tio
n
,
ex
ec
u
tio
n
d
ela
y
,
an
d
m
o
n
etar
y
co
s
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
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3
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x
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m
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up
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m
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b
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[
0
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1
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.
5
]
C
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27
B
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ss c
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50
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0
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5
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ated
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s
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3.
3
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St
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t
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Sin
ce
m
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eu
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n
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p
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ic
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w
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g
t
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m
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d
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lo
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in
[
2
7
]
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T
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Frie
d
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h
t
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P
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C
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L
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T
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m
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r
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ith
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est
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a
n
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b
y
W
O
A
a
n
d
PS
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ile
G
A
a
n
d
A
B
C
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te
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t.
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te
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ll
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al
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0
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0
1
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ir
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g
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f
ican
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f
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ce
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m
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ith
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s
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ig
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d
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t
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s
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am
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l
e
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s
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A
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0
1
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s
.
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B
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0
1
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,
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n
d
v
s
.
C
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s
h
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w
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d
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g
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ig
n
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Dif
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e
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ith
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l
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b
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t
s
t
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ll
s
t
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ti
s
t
i
c
al
ly
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e
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g
f
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l
(
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5
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h
e
s
e
f
i
n
d
i
n
g
s
s
t
a
ti
s
ti
c
a
lly
s
u
b
s
t
an
t
i
a
te
th
e
ef
f
e
c
tiv
en
es
s
o
f
th
e
p
r
o
p
o
s
e
d
h
y
b
r
i
d
i
z
at
i
o
n
an
d
i
ts
d
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e
r
s
i
ty
-
e
n
h
an
c
in
g
o
p
e
r
a
t
o
r
s
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av
o
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d
in
g
p
r
em
a
tu
r
e
c
o
n
v
e
r
g
en
c
e
.
T
ab
le
4
.
Frie
d
m
an
r
a
n
k
s
an
d
W
ilco
x
o
n
test
r
es
u
lt
s
A
l
g
o
r
i
t
h
m
M
e
a
n
f
i
n
a
l
C
-
C
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st
F
r
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e
d
man
r
a
n
k
W
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l
c
o
x
o
n
v
s.
C
M
L
(
p
-
v
a
l
u
e
)
PSO
-
W
O
A
(
C
M
L
)
7
.
2
9
6
1
.
2
2
–
W
O
A
7
.
4
7
4
2
.
1
0
0
.
0
4
3
(
<
0
.
0
5
)
PSO
7
.
3
7
5
2
.
4
5
0
.
0
3
8
(
<
0
.
0
5
)
G
W
O
7
.
4
1
5
3
.
0
5
0
.
0
1
8
(
<
0
.
0
1
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CS
7
.
5
1
7
3
.
4
8
0
.
0
0
7
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0
1
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GA
8
.
6
0
0
5
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<
0
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0
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A
B
C
8
.
6
1
5
5
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0
.
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1
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
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o
m
p
u
t E
l
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tr
o
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Hyb
r
id
P
S
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-
W
OA
a
p
p
r
o
a
ch
f
o
r
a
n
efficien
t ta
s
k
o
fflo
a
d
i
n
g
in
mo
b
ile
ed
g
e
…
(
F
a
tima
Z.
C
h
erh
a
b
il
)
523
3
.
4
.
Sca
la
bil
it
y
a
na
ly
s
is
T
o
ass
ess
s
ca
lab
ili
t
y
,
t
h
e
alg
o
r
ith
m
s
w
er
e
test
ed
u
n
d
er
an
in
cr
ea
s
in
g
n
u
m
b
er
o
f
u
s
er
s
(
2
0
–
1
0
0
)
w
it
h
th
r
ee
ME
C
s
er
v
er
s
.
Fi
g
u
r
e
2
illu
s
tr
ates
th
e
ev
o
l
u
tio
n
o
f
m
e
an
f
i
n
al
C
-
C
o
s
t
a
n
d
E
E
T
av
er
ag
ed
o
v
er
1
0
r
u
n
s
.
As th
e
n
u
m
b
er
o
f
u
s
er
s
i
n
cr
ea
s
es f
r
o
m
2
0
to
1
0
0
,
all
alg
o
r
ith
m
s
s
h
o
w
a
n
at
u
r
al
r
is
e
i
n
th
e
m
ea
n
C
-
C
o
s
t d
u
e
to
th
e
h
ea
v
ier
co
m
p
u
tatio
n
a
l
d
em
an
d
.
Ho
w
e
v
er
,
th
e
p
r
o
p
o
s
ed
P
SO
-
W
O
A
C
M
L
al
g
o
r
ith
m
c
o
n
s
is
ten
tl
y
d
eli
v
er
s
th
e
lo
w
e
s
t
co
s
t
ac
r
o
s
s
all
s
ca
l
es,
s
tar
tin
g
a
t
7
.
2
9
6
(
2
0
u
s
er
s
)
an
d
r
is
in
g
s
m
o
o
th
l
y
to
5
4
.
5
0
8
(
1
0
0
u
s
er
s
)
.
I
n
co
n
tr
ast,
o
th
er
m
et
h
o
d
s
lik
e
G
A
an
d
A
B
C
ex
h
ib
it
m
u
c
h
s
tee
p
er
g
r
o
w
t
h
,
ex
ce
ed
i
n
g
1
2
9
an
d
1
3
1
,
r
esp
ec
tiv
ely
,
at
1
0
0
u
s
er
s
.
T
h
is
h
ig
h
li
g
h
ts
th
e
s
u
p
er
io
r
s
ca
lab
ilit
y
o
f
th
e
h
y
b
r
id
d
esig
n
,
w
h
ich
m
a
i
n
ta
in
s
co
s
t
e
f
f
icie
n
c
y
u
n
d
er
in
cr
ea
s
i
n
g
s
y
s
te
m
lo
ad
.
No
tab
ly
,
P
SO
-
W
O
A
C
M
L
also
o
u
tp
er
f
o
r
m
s
th
e
p
u
r
e
h
y
b
r
id
P
SO
-
W
OA
,
d
em
o
n
s
tr
ati
n
g
th
e
s
i
g
n
i
f
ica
n
t
b
en
ef
it
s
o
f
i
n
co
r
p
o
r
atin
g
cr
o
s
s
o
v
er
,
m
u
ta
tio
n
,
an
d
L
év
y
f
li
g
h
t o
p
er
ato
r
s
.
Fig
u
r
e
2
.
Scalab
ilit
y
b
eh
a
v
io
r
o
f
p
r
o
p
o
s
ed
h
y
b
r
id
alg
o
r
ith
m
v
s
.
s
ta
n
d
ar
d
m
eta
h
eu
r
i
s
tics
I
n
ter
m
s
o
f
E
E
T
,
th
e
p
r
o
p
o
s
e
d
P
SO
-
W
OA
C
ML
al
g
o
r
ith
m
s
h
o
w
s
n
ea
r
l
y
co
n
s
tan
t
r
u
n
ti
m
e
(
≈
1
s
ec
)
as
u
s
er
n
u
m
b
er
s
g
r
o
w
co
m
p
ar
ed
to
o
th
er
alg
o
r
ith
m
s
s
u
c
h
as
A
B
C
(
4
.
0
7
4
s
ec
at
8
0
u
s
er
s
)
o
r
C
S
(
3
.
8
1
1
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ec
at
8
0
u
s
er
s
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.
T
h
is
s
u
g
g
es
ts
th
at
w
h
ile
th
e
p
r
o
b
lem
s
ize
i
n
cr
ea
s
es,
th
e
en
h
an
ce
d
s
ea
r
ch
m
ec
h
an
i
s
m
o
f
th
e
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M
L
o
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er
ato
r
s
allo
w
s
t
h
e
alg
o
r
it
h
m
to
f
in
d
h
ig
h
-
q
u
alit
y
s
o
l
u
tio
n
s
ev
e
n
f
a
s
ter
,
w
h
ic
h
i
m
p
lie
s
g
o
o
d
s
ca
lab
ilit
y
i
n
ti
m
e
co
m
p
le
x
it
y
.
I
n
ter
esti
n
g
l
y
,
W
OA
an
d
p
u
r
e
P
SO
-
W
O
A
r
ep
o
r
t
lo
w
er
E
E
T
v
alu
es
at
lar
g
er
s
ca
les,
b
u
t
t
h
eir
p
er
f
o
r
m
a
n
ce
is
u
n
s
tab
le
an
d
p
air
ed
w
it
h
h
i
g
h
er
co
s
ts
,
r
ef
lect
in
g
p
r
e
m
at
u
r
e
co
n
v
er
g
en
c
e.
O
v
er
all,
P
SO
-
W
O
A
C
M
L
m
ai
n
tai
n
s
a
n
ea
r
-
lin
ea
r
s
ca
lab
ilit
y
tr
en
d
,
ac
h
ie
v
i
n
g
t
h
e
b
est
b
alan
ce
b
et
w
ee
n
co
m
p
u
tatio
n
a
l
ef
f
icie
n
c
y
an
d
o
p
ti
m
izatio
n
q
u
alit
y
,
w
h
ic
h
is
es
s
en
tial f
o
r
lar
g
e
-
s
ca
le
M
E
C
tas
k
o
f
f
lo
ad
in
g
en
v
ir
o
n
m
e
n
ts
.
3.
5
.
Dis
cus
s
io
n
T
h
e
P
SO
-
W
O
A
C
M
L
clea
r
l
y
d
em
o
n
s
tr
ate
s
s
u
p
er
io
r
r
o
b
u
s
tn
ess
,
r
ap
id
c
o
n
v
er
g
e
n
ce
,
an
d
h
ig
h
-
q
u
a
lit
y
s
o
lu
tio
n
s
ac
r
o
s
s
d
iv
er
s
e
s
ce
n
a
r
io
s
.
I
ts
h
y
b
r
id
s
tr
u
ct
u
r
e
in
te
g
r
ates
co
m
p
le
m
e
n
tar
y
a
n
d
d
iv
er
s
e
s
ea
r
ch
s
tr
ateg
ies
an
d
m
ec
h
a
n
i
s
m
s
.
P
SO
’
s
s
o
ci
al
lear
n
in
g
m
ec
h
an
i
s
m
d
r
i
v
es
th
e
p
o
p
u
lat
io
n
to
w
ar
d
p
r
o
m
i
s
in
g
r
eg
io
n
s
,
w
h
ile
W
OA
’
s
d
y
n
a
m
ic
s
p
ir
al
u
p
d
at
e
p
r
o
v
id
es
p
o
w
er
f
u
l
lo
ca
l
ex
p
lo
itatio
n
.
C
r
u
c
iall
y
,
t
h
e
in
c
lu
s
io
n
o
f
L
é
v
y
f
li
g
h
ts
allo
w
s
t
h
e
al
g
o
r
ith
m
to
m
ak
e
o
cc
asio
n
al
lar
g
e
j
u
m
p
s
,
a
p
r
o
v
en
s
tr
ate
g
y
f
o
r
escap
i
n
g
lo
ca
l
o
p
ti
m
a
w
h
er
e
s
i
m
p
ler
al
g
o
r
ith
m
s
m
i
g
h
t
s
ta
g
n
ate.
L
i
k
e
w
i
s
e,
th
e
g
e
n
etic
o
p
er
ato
r
s
o
f
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
m
ai
n
tai
n
p
o
p
u
latio
n
d
iv
er
s
it
y
,
p
r
ev
en
t
i
n
g
p
r
e
m
a
tu
r
e
co
n
v
er
g
e
n
ce
an
d
en
s
u
r
i
n
g
a
r
o
b
u
s
t
e
x
p
lo
r
atio
n
o
f
th
e
s
o
l
u
tio
n
s
p
ac
e.
Fin
al
l
y
,
t
h
e
d
y
n
a
m
ic
p
ar
a
m
eter
tu
n
i
n
g
g
r
ad
u
a
ll
y
m
o
v
es
t
h
e
s
ea
r
ch
f
r
o
m
e
x
p
lo
r
ati
o
n
to
ex
p
lo
itatio
n
,
ac
ce
ler
atin
g
co
n
v
er
g
en
ce
.
T
h
e
s
u
p
er
io
r
it
y
o
f
t
h
e
h
y
b
r
id
a
p
p
r
o
ac
h
o
b
s
er
v
ed
in
o
u
r
r
esu
lt
s
alig
n
s
w
it
h
f
i
n
d
in
g
s
r
ep
o
r
ted
in
r
ec
en
t
liter
atu
r
e.
Fo
r
in
s
ta
n
ce
,
[
1
7
]
d
em
o
n
s
tr
ated
th
a
t
in
te
g
r
ati
n
g
W
O
A
w
it
h
DE
s
i
g
n
i
f
ica
n
tl
y
i
m
p
r
o
v
e
s
tas
k
o
f
f
lo
ad
in
g
e
f
f
icie
n
c
y
co
m
p
a
r
ed
to
s
tan
d
ar
d
alg
o
r
ith
m
s
.
Si
m
ilar
l
y
,
o
u
r
r
es
u
lts
co
n
f
ir
m
t
h
at
h
y
b
r
id
m
ec
h
a
n
i
s
m
s
,
s
p
ec
i
f
icall
y
th
e
in
cl
u
s
io
n
o
f
C
M
L
o
p
er
ato
r
s
,
p
r
ev
en
t
t
h
e
p
r
e
m
atu
r
e
co
n
v
er
g
en
ce
o
f
ten
s
ee
n
i
n
s
tan
d
alo
n
e
W
O
A
i
m
p
le
m
e
n
ta
tio
n
s
.
F
u
r
t
h
er
m
o
r
e,
w
h
ile
[
2
0
]
u
tili
ze
d
GT
O
to
m
in
i
m
ize
en
er
g
y
,
d
ela
y
,
a
n
d
co
s
t,
o
u
r
p
r
o
p
o
s
ed
P
SO
-
W
OA
C
M
L
ac
h
iev
e
s
co
m
p
ar
ab
le
s
tab
ilit
y
in
co
n
v
er
g
en
ce
b
u
t
o
f
f
er
s
i
m
p
r
o
v
ed
s
ca
lab
ilit
y
f
o
r
lar
g
er
u
s
er
s
e
ts
(
u
p
to
1
0
0
u
s
er
s
)
.
T
h
is
s
u
g
g
es
ts
t
h
at
h
y
b
r
id
e
v
o
lu
t
io
n
ar
y
s
tr
ateg
ies
ar
e
in
cr
ea
s
i
n
g
l
y
es
s
e
n
tial
f
o
r
h
an
d
lin
g
t
h
e
h
i
g
h
-
d
i
m
e
n
s
io
n
a
l
s
ea
r
ch
s
p
ac
es
t
y
p
ica
l
o
f
d
en
s
e
SDN
-
ME
C
en
v
ir
o
n
m
e
n
t
s
,
a
co
n
clu
s
io
n
al
s
o
s
u
p
p
o
r
ted
b
y
th
e
m
u
lti
-
u
s
er
m
u
l
ti
-
s
er
v
er
a
n
al
y
s
is
i
n
[
2
8
]
,
[
2
9
]
.
4.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
a
d
d
r
ess
ed
th
e
j
o
i
n
t
tas
k
o
f
f
lo
ad
in
g
an
d
r
eso
u
r
ce
allo
ca
tio
n
p
r
o
b
lem
in
SD
N
-
e
n
ab
led
ME
C
en
v
ir
o
n
m
en
ts
f
o
r
laten
c
y
-
s
e
n
s
iti
v
e
I
o
T
ap
p
licatio
n
s
.
A
h
y
b
r
id
P
SO
-
W
O
A
alg
o
r
it
h
m
e
n
h
an
ce
d
w
it
h
Evaluation Warning : The document was created with Spire.PDF for Python.