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ttp
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BE
R and po
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ticle
his
to
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R
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Ma
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1
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ev
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Dec
4
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ted
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5
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2
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2
4
Qu
a
li
ty
o
f
se
rv
ice
(
Qo
S
)
o
f
wir
e
les
s
c
e
ll
u
lar
n
e
two
r
k
s
a
ffe
c
t
d
u
e
to
m
o
re
p
o
we
r
c
o
n
s
u
m
p
ti
o
n
,
m
a
x
imu
m
b
it
e
rro
r
ra
te
(
BER
)
,
m
in
im
u
m
t
h
ro
u
g
h
p
u
t
a
n
d
imp
ro
p
e
r
re
so
u
rc
e
a
ll
o
c
a
ti
o
n
.
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p
ro
v
e
m
e
n
t
i
n
Q
o
S
c
a
n
b
e
d
o
n
e
b
y
re
d
u
c
in
g
p
o
we
r
c
o
n
su
m
p
ti
o
n
,
BER
a
n
d
e
n
h
a
n
c
in
g
t
h
ro
u
g
h
p
u
t.
H
e
n
c
e
th
e
re
is
a
n
e
e
d
to
a
d
d
re
ss
th
e
a
p
p
r
o
a
c
h
e
s
fo
r
re
d
u
c
ti
o
n
in
p
o
we
r
c
o
n
su
m
p
ti
o
n
,
BER,
e
n
h
a
n
c
e
m
e
n
t
in
th
r
o
u
g
h
p
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t
a
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p
r
o
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e
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rc
e
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s.
In
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iza
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tec
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e
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te
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t
d
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iza
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n
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m
(G
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It
is
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m
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term
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tec
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e
s.
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n
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th
e
Qo
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o
f
th
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wire
les
s
c
e
ll
u
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n
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two
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k
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g
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u
r
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p
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se
d
sc
h
e
m
e
.
K
ey
w
o
r
d
s
:
B
E
R
GA
GW
O
PSO
Qo
S
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Gaja
n
an
Uttam
Patil
Dep
ar
tm
en
t o
f
E
an
d
T
C
E
n
g
i
n
ee
r
in
g
,
HSM
’
S,
SS
GB
C
OE
&
T
,
B
h
u
s
awa
l
,
DB
AT
U
L
o
n
er
e,
Ma
h
ar
ash
tr
a,
I
n
d
ia
E
m
ail: g
ajan
an
.
b
s
l@
g
m
a
il.c
o
m
1.
I
NT
RO
D
UCT
I
O
N
Qu
ality
o
f
s
er
v
ice
(
Qo
S)
d
ep
en
d
s
u
p
o
n
m
an
y
f
ac
to
r
s
o
r
p
ar
am
eter
s
in
wir
eless
ce
llu
lar
n
etwo
r
k
s
.
Mo
s
t
o
f
th
e
r
esear
ch
er
s
wo
r
k
o
n
m
an
y
p
a
r
am
eter
s
in
th
eir
r
esear
ch
to
im
p
r
o
v
e
t
h
e
Qo
S.
Dif
f
er
en
t
o
p
tim
izatio
n
alg
o
r
ith
m
s
u
s
ed
to
o
p
tim
ize
th
e
s
er
v
ices
i
n
wi
r
eless
ce
llu
lar
n
etwo
r
k
s
t
o
en
h
an
ce
th
e
wir
eless
ce
llu
lar
n
etwo
r
k
s
’
p
e
r
f
o
r
m
an
c
e
an
d
p
r
o
v
id
i
n
g
th
e
b
etter
Qo
S.
Du
e
to
m
o
r
e
p
o
wer
c
o
n
s
u
m
p
tio
n
,
m
ax
i
m
u
m
b
it
er
r
o
r
r
ate
(
B
E
R
)
,
m
in
im
u
m
th
r
o
u
g
h
p
u
t
an
d
im
p
r
o
p
er
r
eso
u
r
ce
allo
ca
tio
n
th
e
Qo
S
o
f
wir
eless
ce
llu
lar
n
etwo
r
k
s
d
e
g
r
ad
es
an
d
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
wir
eless
c
ellu
lar
n
etwo
r
k
s
af
f
ec
ted
.
So
as
a
g
o
o
d
s
er
v
ice
p
r
o
v
id
e
r
o
r
wir
eless
ce
llu
lar
n
etwo
r
k
s
m
u
s
t
h
av
e
th
e
g
o
o
d
Qo
S.
Hen
ce
Qo
S
ca
n
b
e
im
p
r
o
v
ed
o
r
en
h
ac
e
b
y
r
ed
u
cin
g
th
e
m
in
im
al
B
E
R
an
d
lo
w
p
o
wer
u
s
ag
e,
b
y
en
h
an
cin
g
t
h
e
m
ax
im
u
m
th
r
o
u
g
h
p
u
t
a
n
d
u
s
in
g
th
e
p
r
o
p
er
r
eso
u
r
s
e
allo
ca
tio
n
in
t
h
e
n
etwo
r
k
.
I
n
th
is
wo
r
k
we
u
s
e
th
e
g
r
ey
wo
lf
o
p
tim
izatio
n
(
GW
O)
tech
n
iq
u
e
as
esti
m
ated
m
eth
o
d
f
o
r
f
itn
e
s
s
f
u
n
ctio
n
o
p
tim
izatio
n
f
o
r
m
in
im
u
m
B
E
R
an
d
m
in
im
u
m
p
o
wer
co
n
s
u
m
p
tio
n
s
o
th
at
th
e
B
E
R
an
d
p
o
wer
c
o
n
s
u
m
p
tio
n
m
u
s
t
b
e
m
in
im
u
m
to
en
h
an
ce
th
e
Qo
S.
GW
O
alg
o
r
ith
m
r
esem
b
les
g
r
ey
wo
lv
es
’
h
u
n
tin
g
s
tr
ateg
y
an
d
m
an
ag
e
m
en
t
h
ier
ar
c
h
y
in
wir
eless
ce
l
lu
lar
n
etwo
r
k
en
v
ir
o
n
m
en
t.
Alp
h
a,
b
eta,
d
elta,
an
d
o
m
e
g
a
ar
e
t
h
e
f
o
u
r
v
ar
ieties
o
f
g
r
ey
wo
lv
es
th
at
ar
e
u
s
ed
to
r
esem
b
le
th
e
m
an
ag
em
e
n
t
h
ier
ar
ch
y
.
Fu
r
th
er
m
o
r
e,
h
u
n
tin
g
co
n
s
is
ts
o
f
th
r
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im
ar
y
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tep
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in
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e
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r
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tak
en
in
t
o
co
n
s
id
er
ati
o
n
wh
ile
im
p
lem
tin
g
th
e
m
eth
o
d
o
lo
g
y
[
1
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
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f
&
C
o
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T
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I
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N:
2252
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B
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p
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min
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h
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p
timiz
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tio
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…
(
Ga
ja
n
a
n
Utta
m
P
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til)
587
Fo
r
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in
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10
(
0
.
5
)
l
o
g
10
(
P
be
)
(
2
)
T
h
er
e
is
a
ten
s
io
n
b
etwe
en
t
h
e
two
s
ig
n
al
-
o
b
jectiv
e
f
itn
ess
f
u
n
ctio
n
s
;
f
o
r
in
s
tan
ce
,
m
in
im
izin
g
p
o
wer
u
s
ag
e
o
f
ten
r
aises
th
e
B
E
R
.
As
a
r
es
u
lt,
th
e
ac
tu
al
o
p
tim
izatio
n
r
esu
lts
s
h
o
u
ld
b
alan
ce
th
ese
s
ig
n
al
-
o
b
jectiv
e
f
itn
ess
f
u
n
ctio
n
s
in
o
r
d
e
r
to
s
atis
f
y
th
e
s
y
s
tem
’
s
co
m
m
u
n
icatio
n
n
ee
d
s
wh
ile
m
ax
im
izin
g
o
th
er
p
er
f
o
r
m
an
ce
.
C
o
n
s
eq
u
en
tly
,
th
e
m
u
ltip
le
-
o
b
jectiv
e
f
itn
ess
f
u
n
ctio
n
o
f
th
e
wh
o
le
n
etwo
r
k
m
ay
b
e
d
ef
i
n
e
d
as:
f
=
ω
1
×
f
m
i
n
−
p
o
w
er
+
ω
2
×
f
m
i
n
−
B
ER
(
3
)
wh
er
e
th
e
t
h
r
ee
c
o
n
tr
o
l
o
b
ject
iv
es’,
r
elativ
e
s
ig
n
if
ican
ce
is
d
eter
m
in
ed
b
y
th
e
weig
h
t
v
ec
to
r
W
,
wh
ich
m
u
s
t
s
atis
f
y
th
e
ω
1
+
ω
2
=
1
[
2
]
.
Sectio
n
2
p
r
esen
ts
a
liter
atu
r
e
r
ev
iew
to
s
u
p
p
o
r
t
an
d
h
elp
t
h
e
p
r
o
p
o
s
ed
w
o
r
k
.
I
n
s
ec
tio
n
3
o
u
tlin
es
th
e
p
r
o
p
o
s
ed
r
esear
ch
m
eth
o
d
o
lo
g
y
u
s
in
g
GW
O
alg
o
r
ith
m
an
d
h
ier
a
r
ch
y
o
f
th
e
s
am
e
an
d
its
co
m
p
lete
wo
r
k
in
g
alg
o
r
ith
m
.
T
h
e
o
b
tain
ed
r
esu
lts
in
ter
m
s
o
f
m
in
im
u
m
B
E
R
an
d
m
in
im
u
m
p
o
wer
co
n
s
u
m
p
tio
n
an
d
th
eir
co
m
p
ar
is
io
n
with
th
e
PS
O
an
d
g
en
etic
alg
o
r
ith
m
(
GA
)
is
d
is
cu
u
s
ed
i
n
s
ec
tio
n
4
.
Fin
ally
,
s
ec
tio
n
5
co
n
clu
d
es p
r
esen
ted
wo
r
k
an
d
it o
f
f
er
s
s
o
m
e
id
ea
s
f
o
r
f
u
tu
r
e
r
esear
ch
ar
ea
s
.
2.
RE
L
AT
E
D
WO
RK
Ko
u
ts
o
r
o
d
i
et
a
l.
[
3
]
,
i
n
tr
o
d
u
c
ed
a
m
o
b
ile
ter
m
i
n
al
s
tr
u
ctu
r
a
l
d
esig
n
tailo
r
ed
f
o
r
d
ev
ices
o
p
er
atin
g
in
h
eter
o
g
en
e
o
u
s
e
n
v
ir
o
n
m
en
ts
.
T
h
ey
p
r
o
p
o
s
ed
a
s
tr
ateg
y
th
at
in
d
ep
e
n
d
en
tl
y
id
e
n
tifie
d
t
h
e
o
p
tim
al
attac
h
m
e
n
t
p
o
in
t
a
n
d
lo
ca
l
in
ter
f
ac
e
wh
il
e
ac
co
u
n
tin
g
f
o
r
f
ac
to
r
s
s
u
c
h
as
u
s
er
p
r
e
f
er
en
ce
s
,
n
etwo
r
k
co
n
d
itio
n
s
,
s
er
v
ice
r
eq
u
ir
em
e
n
ts
,
an
d
r
eso
u
r
ce
av
ailab
ilit
y
.
T
o
ad
d
r
ess
th
e
m
u
lti
-
cr
iter
ia
ac
ce
s
s
n
etwo
r
k
s
elec
tio
n
(
ANS)
p
r
o
b
lem
,
[
4
]
u
tili
ze
d
f
u
zz
y
lo
g
ic
(
FL)
an
d
GAs.
T
h
is
ap
p
r
o
ac
h
in
co
r
p
o
r
ated
a
m
u
lti
-
m
etr
i
c
s
o
f
twar
e
ass
is
tan
t
(
SA)
to
s
u
p
p
o
r
t
b
o
th
u
s
er
s
an
d
n
etwo
r
k
o
p
er
at
o
r
s
in
d
eliv
e
r
in
g
t
h
e
r
eq
u
ir
e
d
Qo
S
wh
ile
e
n
s
u
r
in
g
f
lex
ib
ilit
y
,
s
ca
lab
ilit
y
,
an
d
s
im
p
licity
.
R
esu
lts
d
em
o
n
s
tr
ated
th
at
th
e
S
A
an
d
th
e
p
r
o
p
o
s
ed
s
ch
em
e
w
er
e
m
o
r
e
r
o
b
u
s
t
an
d
d
eliv
er
ed
b
etter
p
er
f
o
r
m
a
n
ce
c
o
m
p
ar
ed
to
r
a
n
d
o
m
-
b
ased
s
elec
tio
n
m
eth
o
d
s
.
T
h
e
f
u
zz
y
n
e
u
r
al
f
r
am
ewo
r
k
p
r
esen
ted
in
[
5
]
em
p
lo
y
ed
a
m
u
lti
-
cr
iter
ia
d
ec
is
io
n
-
m
a
k
in
g
ap
p
r
o
ac
h
th
at
co
n
s
id
er
e
d
s
u
b
jectiv
e
cr
iter
ia
an
d
o
p
e
r
ato
r
p
o
licies.
Fo
r
ex
am
p
le,
ce
r
tai
n
r
a
d
io
a
cc
ess
tech
n
o
lo
g
ie
s
(
R
AT
s
)
co
u
ld
b
e
p
r
i
o
r
itized
o
v
er
o
th
er
s
,
o
r
tr
af
f
ic
co
u
ld
b
e
b
alan
ce
d
ac
r
o
s
s
m
u
ltip
le
R
A
T
s
.
Kh
a
n
et
a
l.
[
6
]
,
a
u
s
er
-
ce
n
tr
ic
n
etwo
r
k
s
elec
tio
n
m
ec
h
an
is
m
b
ased
o
n
a
n
au
ctio
n
s
y
s
tem
was
p
r
o
p
o
s
ed
.
T
h
is
ap
p
r
o
ac
h
in
v
o
lv
ed
n
eg
o
tiatio
n
s
b
etwe
e
n
u
s
er
s
an
d
n
etwo
r
k
o
p
er
ato
r
s
,
tak
in
g
in
to
ac
co
u
n
t
s
er
v
ice
q
u
ality
m
etr
ics
s
u
ch
as
tr
u
th
f
u
l
n
etwo
r
k
b
eh
a
v
io
r
,
u
s
er
co
n
s
u
m
p
tio
n
p
atter
n
s
,
a
n
d
t
h
e
p
r
ici
n
g
o
f
f
er
ed
b
y
n
et
wo
r
k
o
p
e
r
ato
r
s
.
FL
was u
tili
ze
d
to
r
ed
u
ce
th
e
f
r
eq
u
en
cy
o
f
h
a
n
d
o
v
er
s
,
en
h
a
n
cin
g
n
etwo
r
k
ef
f
icien
cy
.
T
h
e
au
to
n
o
m
ic
in
ter
f
ac
e
s
elec
tio
n
ar
ch
itectu
r
e
(
AI
SA)
,
in
tr
o
d
u
ce
d
in
[
7
]
,
e
m
p
lo
y
e
d
f
u
zz
y
co
n
tr
o
l
to
d
y
n
am
ically
an
d
au
to
m
atica
lly
s
elec
t
th
e
m
o
s
t
s
u
itab
le
in
te
r
f
ac
e
f
r
o
m
m
u
ltip
le
a
v
ailab
le
o
p
tio
n
s
.
AI
SA
u
s
ed
d
iv
er
s
e
au
to
n
o
m
ic
e
v
alu
atio
n
r
u
les
to
ad
a
p
t
to
c
h
an
g
in
g
n
et
wo
r
k
co
n
d
itio
n
s
a
n
d
u
s
er
d
e
m
an
d
s
.
T
h
e
s
y
s
tem
was
d
esig
n
ed
to
s
en
s
e
an
d
in
ter
p
r
et
r
elatio
n
al
ch
a
n
g
es
with
in
th
e
n
etwo
r
k
,
e
n
s
u
r
in
g
a
d
ap
tab
ilit
y
to
d
y
n
am
i
c
en
v
ir
o
n
m
en
ts
.
B
y
a
p
p
ly
in
g
f
u
zz
y
s
et
th
eo
r
y
,
AI
SA
lev
er
a
g
e
d
h
u
m
an
-
lik
e
r
ea
s
o
n
i
n
g
to
a
u
to
n
o
m
o
u
s
ly
id
e
n
tify
th
e
o
p
tim
al
in
ter
f
ac
e
f
o
r
s
p
ec
if
ic
ap
p
licatio
n
s
.
A
u
s
er
p
r
ef
er
en
ce
-
o
r
ien
te
d
n
et
wo
r
k
s
elec
tio
n
(
UPNS)
alg
o
r
it
h
m
was
d
e
v
elo
p
ed
in
[
7
]
b
y
i
n
teg
r
atin
g
th
e
f
u
zz
y
an
aly
tic
h
ier
a
r
ch
y
p
r
o
ce
s
s
(
FAHP)
with
en
tr
o
p
y
th
eo
r
y
.
T
h
is
ap
p
r
o
ac
h
en
h
an
ce
d
ef
f
icien
cy
b
y
alig
n
in
g
with
in
d
iv
i
d
u
al
u
s
er
p
r
ef
e
r
en
ce
s
,
th
e
r
eb
y
im
p
r
o
v
i
n
g
u
s
er
s
atis
f
ac
tio
n
.
Sin
g
h
r
o
v
a
an
d
Pra
k
ash
[
8
]
,
a
n
eu
r
o
-
f
u
zz
y
m
u
l
ti
-
f
ac
to
r
v
er
tical
h
an
d
o
f
f
d
ec
is
io
n
alg
o
r
ith
m
(
VHDA
)
was
p
r
o
p
o
s
ed
.
T
h
is
m
eth
o
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
14
,
No
.
2
,
A
u
g
u
s
t
20
25
:
586
-
593
588
ev
alu
ated
s
ix
m
etr
ics
an
d
em
p
lo
y
ed
a
r
u
le
-
b
ased
f
r
am
ewo
r
k
to
m
ak
e
v
er
tical
h
an
d
o
f
f
d
e
cisi
o
n
s
.
Simu
latio
n
r
esu
lts
s
h
o
wed
th
at
th
e
alg
o
r
ith
m
s
ig
n
if
ican
tly
r
e
d
u
ce
d
t
h
e
n
u
m
b
er
o
f
v
e
r
tical
h
a
n
d
o
f
f
s
wh
ile
o
f
f
er
in
g
s
u
p
er
io
r
Qo
S c
o
m
p
ar
ed
to
ex
i
s
tin
g
tech
n
iq
u
es,
as r
ef
lecte
d
i
n
its
h
an
d
o
f
f
q
u
ality
in
d
icato
r
.
T
h
e
alg
o
r
ith
m
in
[
8
]
also
in
co
r
p
o
r
ated
GAs,
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
a
n
d
FL
c
o
n
tr
o
ller
s
f
o
r
d
ec
is
io
n
-
m
a
k
in
g
b
ased
o
n
in
p
u
t
s
s
u
ch
as
u
s
er
v
elo
city
,
s
er
v
ice
co
s
t,
Qo
S
p
ar
am
eter
s
,
s
er
v
ice
ty
p
e,
an
d
cu
s
to
m
er
r
eq
u
ir
em
e
n
ts
.
R
ad
h
ik
a
an
d
R
ed
d
y
[
9
]
,
FL
with
m
u
lti
-
cr
iter
ia
d
ec
is
io
n
-
m
a
k
in
g
attr
ib
u
tes
was
in
tr
o
d
u
ce
d
to
o
p
tim
ize
n
etwo
r
k
s
elec
tio
n
.
T
h
e
n
etwo
r
k
s
elec
tio
n
f
u
n
ctio
n
(
NSF)
ev
al
u
at
ed
th
e
ca
p
ab
ilit
y
o
f
av
ailab
le
r
ad
io
r
eso
u
r
ce
s
in
d
i
v
id
u
ally
,
with
th
e
n
etwo
r
k
att
ain
in
g
th
e
h
i
g
h
est
NSF
v
alu
e
b
ein
g
ch
o
s
en
as
th
e
tar
g
et
n
etwo
r
k
f
o
r
h
an
d
o
v
er
.
A
r
ad
io
n
etwo
r
k
s
elec
tio
n
(
R
NS)
s
o
lu
tio
n
was
p
r
o
p
o
s
ed
i
n
[
1
0
]
,
co
m
b
i
n
in
g
p
ar
allel
FL
co
n
tr
o
l
an
d
m
u
lti
-
cr
iter
ia
d
ec
is
io
n
-
m
a
k
in
g
(
MCDM)
s
y
s
tem
s
.
T
h
is
ad
ap
tiv
e
ap
p
r
o
ac
h
d
em
o
n
s
tr
ated
h
ig
h
e
r
r
o
b
u
s
tn
ess
an
d
b
etter
p
e
r
f
o
r
m
an
ce
th
an
p
r
ev
io
u
s
ly
ex
p
lo
r
e
d
m
eth
o
d
s
.
San
g
wan
et
a
l.
[
1
1
]
,
a
u
s
er
-
s
p
ec
if
ic
in
tellig
en
t
v
er
tical
h
an
d
o
f
f
(
UI
VH)
alg
o
r
ith
m
was
in
tr
o
d
u
ce
d
,
u
tili
zin
g
th
e
ad
ap
tiv
e
n
e
u
r
o
-
f
u
zz
y
in
f
er
en
ce
s
y
s
tem
(
ANFI
S)
f
o
r
n
etwo
r
k
s
elec
tio
n
.
U
I
VH
ef
f
ec
tiv
el
y
id
e
n
tifie
d
th
e
m
o
s
t
s
u
itab
le
n
etwo
r
k
wh
ile
co
n
s
id
er
in
g
s
p
ec
if
ic
u
s
er
r
eq
u
ir
em
en
ts
.
R
esu
lts
in
d
icate
d
a
g
r
ad
u
al
in
cr
e
ase
in
r
elin
q
u
is
h
in
g
co
m
p
letio
n
tim
e
alo
n
g
s
id
e
a
r
is
e
in
th
e
n
u
m
b
er
o
f
h
a
n
d
o
v
e
r
s
.
A
Qo
S
-
awa
r
e
VHO
m
ec
h
an
is
m
lev
er
ag
in
g
f
u
zz
y
r
u
les
an
d
m
u
lti
-
cr
iter
ia
d
ec
is
io
n
-
m
ak
in
g
was
p
r
esen
ted
i
n
[
1
2
]
.
I
t
ef
f
icie
n
tly
m
et
th
e
d
iv
er
s
e
n
ee
d
s
o
f
ap
p
licatio
n
s
ac
r
o
s
s
v
ar
i
o
u
s
en
v
ir
o
n
m
en
ts
.
Ad
d
iti
o
n
ally
,
a
v
alu
atio
n
m
o
d
el
em
p
lo
y
in
g
a
n
o
n
-
b
ir
th
-
d
ea
th
Ma
r
k
o
v
ch
ain
d
em
o
n
s
tr
ated
im
p
r
o
v
e
d
p
er
f
o
r
m
an
ce
f
o
r
d
if
f
e
r
en
t
tr
af
f
ic
s
ce
n
ar
io
s
co
m
p
ar
ed
to
c
o
n
v
en
tio
n
al
alg
o
r
ith
m
s
.
Kale
em
e
t
a
l.
[
1
3
]
,
a
m
u
lti
-
cr
iter
ia
VHO
alg
o
r
ith
m
a
d
d
r
ess
ed
u
s
er
p
r
e
f
er
en
ce
s
,
tr
af
f
i
c
ty
p
es,
an
d
s
y
s
tem
m
etr
ics,
m
ain
tain
in
g
Qo
S
s
tan
d
ar
d
s
th
r
o
u
g
h
m
o
d
u
les
lik
e
th
e
tar
g
et
n
etwo
r
k
o
p
tio
n
a
n
d
VHO
n
ec
ess
ity
esti
m
at
io
n
(
VHON
E
)
.
A
f
u
zz
y
lin
g
u
is
tic
weig
h
tin
g
s
ch
em
e
w
as a
ls
o
p
r
o
p
o
s
ed
to
en
h
a
n
ce
d
ec
is
io
n
r
eliab
ilit
y
.
R
io
s
et
a
l.
[
1
4
]
an
d
L
a
h
b
y
et
a
l.
[
1
5
]
,
th
e
in
teg
r
atio
n
o
f
FL
with
g
r
ey
r
elatio
n
al
an
a
l
y
s
is
(
GR
A)
an
d
an
aly
tic
h
ier
ar
ch
y
p
r
o
ce
s
s
(
AHP)
d
ec
is
io
n
-
m
ak
in
g
m
eth
o
d
s
was
s
u
g
g
ested
.
T
h
ese
m
eth
o
d
s
p
r
o
v
ed
u
s
ef
u
l
in
p
r
io
r
itizin
g
alter
n
ativ
e
n
etwo
r
k
o
p
tio
n
s
,
with
a
r
tific
ial
in
tellig
en
ce
tech
n
i
q
u
es
f
u
r
t
h
er
r
ef
i
n
in
g
th
e
ac
cu
r
ac
y
o
f
r
esu
lts
.
A
r
an
k
in
g
alg
o
r
ith
m
c
o
m
b
in
in
g
m
ah
alan
o
b
is
d
is
tan
ce
an
d
m
u
lti
-
attr
ib
u
te
d
ec
is
io
n
-
m
ak
in
g
(
MA
DM
)
tech
n
iq
u
es
was
in
tr
o
d
u
ce
d
i
n
[
1
5
]
,
[
1
6
]
.
T
h
is
alg
o
r
ith
m
ca
teg
o
r
ized
cr
iter
ia
in
to
co
n
s
is
ten
t
cla
s
s
es,
d
eter
m
in
ed
i
n
tr
a
-
c
lass
an
d
i
n
ter
-
class
weig
h
ts
u
s
in
g
M
ADM
an
d
f
u
zz
y
AHP
m
et
h
o
d
s
,
an
d
ap
p
lied
Ma
h
alan
o
b
is
d
is
tan
ce
f
o
r
r
a
n
k
in
g
.
T
h
e
a
p
p
r
o
ac
h
ef
f
ec
tiv
e
ly
r
ed
u
ce
d
r
an
k
in
g
ir
r
eg
u
lar
i
ties
an
d
o
p
tim
ized
n
etwo
r
k
s
elec
tio
n
.
T
h
u
m
th
awa
two
r
n
et
a
l.
[
1
6
]
a
n
d
Go
el
et
a
l.
[
1
7
]
,
a
h
a
n
d
o
v
er
d
ec
is
io
n
s
y
s
tem
(
HDS)
was
p
r
o
p
o
s
ed
u
s
in
g
FL
t
o
a
d
d
r
ess
n
etwo
r
k
s
elec
tio
n
ch
alle
n
g
es.
T
h
e
s
y
s
tem
d
em
o
n
s
tr
ated
s
ig
n
if
ican
t
im
p
r
o
v
em
en
ts
in
ex
ec
u
tio
n
tim
e
an
d
n
etwo
r
k
s
elec
tio
n
ac
cu
r
ac
y
.
Fu
r
th
er
n
etwo
r
k
s
elec
tio
n
m
ec
h
an
is
m
s
wer
e
d
is
cu
s
s
ed
in
[
1
7
]
-
[
2
0
]
f
o
r
s
ea
m
less
v
er
tic
al
h
an
d
o
f
f
in
h
eter
o
g
e
n
eo
u
s
wir
eless
n
etwo
r
k
s
.
T
h
ese
ap
p
r
o
ac
h
es
f
ac
ilit
ated
m
o
b
ilit
y
ac
r
o
s
s
tech
n
o
lo
g
ies
s
u
ch
as
W
iMa
x
,
W
i
-
Fi
h
o
ts
p
o
ts
,
an
d
ce
llu
lar
n
etwo
r
k
s
.
T
r
ad
itio
n
al
m
eth
o
d
s
r
ely
in
g
o
n
g
en
er
al
m
et
r
ic
ev
al
u
atio
n
s
o
f
te
n
f
ail
to
d
eliv
er
ef
f
icien
t
n
etwo
r
k
s
elec
tio
n
d
ec
i
s
io
n
s
.
T
o
o
v
er
c
o
m
e
th
ese
lim
itatio
n
s
,
we
p
r
o
p
o
s
e
a
n
o
v
el
alg
o
r
ith
m
b
ased
o
n
GW
O
,
wh
ich
o
f
f
er
s
a
m
o
r
e
r
o
b
u
s
t
an
d
ef
f
icien
t
s
o
lu
tio
n
f
o
r
n
etwo
r
k
s
elec
tio
n
in
h
eter
o
g
e
n
eo
u
s
e
n
v
ir
o
n
m
en
t
s
.
3.
P
RO
P
O
SE
D
RE
SE
ARCH
M
E
T
H
O
D
T
h
e
alp
h
a
wo
lf
s
er
v
es
as
th
e
lead
er
o
f
th
e
p
ac
k
an
d
ca
n
b
e
eith
er
m
ale
o
r
f
em
ale.
T
h
is
wo
lf
is
r
esp
o
n
s
ib
le
f
o
r
m
a
k
in
g
k
ey
d
e
cisi
o
n
s
r
eg
ar
d
in
g
h
u
n
tin
g
,
s
elec
tin
g
s
leep
in
g
lo
ca
tio
n
s
,
an
d
d
eter
m
in
in
g
wak
e
-
u
p
tim
es.
All
o
th
er
wo
lv
es
in
th
e
p
ac
k
ar
e
r
e
q
u
ir
ed
to
ad
h
e
r
e
to
t
h
e
alp
h
a
’
s
c
o
m
m
an
d
s
,
m
ak
in
g
t
h
is
r
o
le
th
e
h
ig
h
est
p
o
s
itio
n
in
th
e
h
ie
r
ar
ch
y
.
B
elo
w
th
e
alp
h
a
is
th
e
b
eta
wo
lf
,
wh
ich
ass
is
ts
in
d
ec
is
io
n
-
m
ak
in
g
a
n
d
en
s
u
r
es th
e
alp
h
a
’
s
d
ir
ec
tiv
es a
r
e
f
o
llo
wed
b
y
th
e
r
est o
f
th
e
p
ac
k
.
T
h
e
b
eta
wo
lf
,
also
m
ale
o
r
f
em
ale,
p
lay
s
a
cr
u
cial
r
o
le
in
m
ain
tain
in
g
d
is
cip
lin
e
with
in
th
e
g
r
o
u
p
.
I
n
th
e
ev
en
t
o
f
th
e
alp
h
a
’
s
ab
s
en
ce
o
r
d
ea
th
,
th
e
b
eta
is
th
e
m
o
s
t lik
ely
s
u
cc
ess
o
r
to
tak
e
o
n
th
e
lead
er
s
h
ip
r
o
le
[
2
1
]
.
At
th
e
b
o
tto
m
o
f
th
e
h
ier
a
r
ch
y
is
th
e
o
m
e
g
a
wo
lf
,
wh
ich
is
s
u
b
o
r
d
i
n
ate
to
all
o
th
er
w
o
lv
es.
Om
eg
a
wo
lv
es
o
f
ten
y
ield
t
o
th
e
d
o
m
in
an
t
m
em
b
e
r
s
o
f
th
e
p
ac
k
an
d
ar
e
th
e
last
to
ea
t
af
ter
o
th
er
s
h
av
e
f
in
is
h
ed
.
Alth
o
u
g
h
th
ey
ap
p
ea
r
to
h
av
e
a
less
s
ig
n
if
ican
t
r
o
le,
o
m
eg
a
wo
lv
es
ar
e
ess
en
tial
f
o
r
r
ed
u
c
in
g
ten
s
io
n
s
with
in
th
e
p
ac
k
b
y
ac
tin
g
as
s
ca
p
eg
o
ats
o
r
o
u
tlets
f
o
r
ag
g
r
ess
io
n
.
T
h
e
d
elta
wo
lv
es
r
an
k
b
elo
w
th
e
alp
h
a
a
n
d
b
eta
b
u
t
ar
e
s
u
p
e
r
io
r
to
th
e
o
m
eg
a.
T
h
is
g
r
o
u
p
in
clu
d
es
s
co
u
ts
,
h
u
n
ter
s
,
ca
r
etak
er
s
,
a
n
d
eld
e
r
s
,
ea
ch
with
s
p
ec
if
ic
r
esp
o
n
s
ib
ilit
ies
lik
e
s
af
eg
u
ar
d
in
g
th
e
p
a
ck
an
d
en
s
u
r
in
g
its
well
-
b
ein
g
b
y
m
an
ag
in
g
f
o
o
d
an
d
h
ea
lth
-
r
elate
d
task
s
.
Delta
wo
lv
es
m
u
s
t
s
u
b
m
it
to
th
e
au
th
o
r
ity
o
f
th
e
alp
h
a
an
d
b
eta
wh
ile
m
ain
tain
in
g
d
o
m
in
an
ce
o
v
er
th
e
o
m
eg
a
wo
lv
es.
Fig
u
r
e
1
illu
s
tr
ates th
e
h
ier
ar
ch
ical
s
tr
u
ctu
r
e
o
f
g
r
ey
w
o
lf
p
ac
k
s
,
h
i
g
h
lig
h
ti
n
g
th
e
d
is
tin
ct
r
o
les
an
d
r
esp
o
n
s
ib
ilit
ies o
f
ea
ch
g
r
o
u
p
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
B
E
R
a
n
d
p
o
w
er c
o
n
s
u
mp
tio
n
min
imiz
a
tio
n
th
r
o
u
g
h
o
p
timiz
a
tio
n
…
(
Ga
ja
n
a
n
Utta
m
P
a
til)
589
I
n
th
e
GW
O,
th
e
alp
h
a
(
α
)
wo
lf
r
ep
r
esen
ts
th
e
b
est
s
o
lu
tio
n
,
s
er
v
in
g
as
th
e
lead
er
in
th
e
s
o
cial
h
ier
ar
ch
y
.
T
h
e
b
eta
(
β)
an
d
d
elta
(
δ)
wo
l
v
es
s
ig
n
if
y
th
e
s
ec
o
n
d
a
n
d
th
i
r
d
b
est
s
o
lu
tio
n
s
,
r
esp
ec
tiv
ely
.
Dec
is
io
n
-
m
ak
in
g
r
eg
a
r
d
in
g
t
h
e
h
u
n
tin
g
p
r
o
ce
s
s
is
p
r
im
ar
ily
led
b
y
α
\
alp
h
aα
,
β
\
b
etaβ,
an
d
δ
\
d
eltaδ.
T
h
e
r
em
ain
in
g
wo
lv
es,
r
e
f
er
r
ed
to
as
o
m
eg
a
(
ω
)
,
f
o
llo
w
t
h
e
d
i
r
ec
tio
n
s
o
f
t
h
e
to
p
th
r
ee
wo
lv
es.
Fig
u
r
e
2
illu
s
tr
ates
th
e
p
o
ten
tial
h
u
n
tin
g
p
o
s
it
io
n
s
an
d
th
e
en
cir
clin
g
b
eh
av
i
o
r
o
f
th
e
wo
lv
es.
T
h
e
p
r
o
ce
s
s
o
f
wo
lv
es
en
cir
clin
g
th
eir
p
r
ey
ca
n
b
e
d
escr
ib
ed
u
s
in
g
(
4
)
an
d
(
5
)
.
D
⃗
⃗
=
|
C
⃗
.
X
⃗
⃗
P
t
−
X
⃗
⃗
(
t
)
|
(
4
)
(
)
(
+
1
)
=
_
−
.
(
5
)
wh
er
e,
X
is
p
o
s
itio
n
v
ec
to
r
o
f
wo
lf
.
A
an
d
C
ar
e
co
e
f
f
icien
t v
ec
t
o
r
s
an
d
t
is
cu
r
r
en
t iter
atio
n
.
Fig
u
r
e
1
.
Hier
ar
c
h
y
o
f
g
r
e
y
wo
l
f
[
22
]
Fig
u
r
e
2
.
Po
s
s
ib
le
h
u
n
tin
g
lo
c
atio
n
s
an
d
en
cir
clin
g
b
eh
av
io
u
r
o
f
wo
lv
es [
22
]
3
.
1
.
G
re
y
wo
lf
o
ptim
izer
a
l
g
o
rit
hm
1)
Gr
ey
wo
lv
es
ex
h
ib
it
a
s
tr
ateg
i
c
h
u
n
tin
g
b
eh
a
v
io
r
th
at
r
ev
o
lv
es
ar
o
u
n
d
t
h
e
p
o
s
itio
n
s
o
f
th
e
alp
h
a,
b
eta,
a
n
d
d
elta
wo
lv
es.
Du
r
in
g
th
e
s
ea
r
c
h
p
h
ase,
th
e
y
d
is
p
er
s
e
(
d
iv
e
r
g
en
ce
)
to
ex
p
lo
r
e
th
e
en
v
ir
o
n
m
en
t
f
o
r
p
o
ten
tial
p
r
e
y
.
O
n
c
e
t
h
e
p
r
e
y
i
s
l
o
c
a
t
e
d
,
t
h
e
w
o
l
v
e
s
r
e
g
r
o
u
p
(
c
o
n
v
e
r
g
e
n
c
e
)
t
o
c
o
o
r
d
i
n
a
t
e
t
h
e
i
r
at
t
a
c
k
e
f
f
e
c
t
i
v
e
l
y
[
23
]
.
=
2
.
.
_
1
−
(
6
)
=
2
.
_
2
(
7
)
W
h
er
e,
r
_
1
an
d
r
_
2
ar
e
r
an
d
o
m
v
ec
t
o
r
s
:
2)
T
h
e
in
itializatio
n
o
f
GW
O
p
o
p
u
latio
n
is
g
iv
en
b
y
at
c
o
u
n
te
r
iter
atio
n
t=0
:
_
=
(
1
,
2
,
3
…
…
…
…
)
(
8
)
3)
Fu
r
th
er
A,
C
an
d
a
ar
e
also
in
i
tialized
4)
No
w
th
e
f
itn
ess
f
u
n
ctio
n
f
o
r
e
ac
h
s
ea
r
ch
in
g
a
g
en
t is ev
alu
at
ed
an
d
is
r
e
p
r
esen
ted
as:
X_
α
d
en
o
tes b
est s
ea
r
ch
in
g
ag
en
t
X_
β
d
en
o
tes 2
nd
b
est s
ea
r
ch
in
g
ag
en
t
X_
δ
d
en
o
tes 3
rd
b
est s
ea
r
ch
in
g
ag
e
nt
5)
I
f
th
e
to
tal
n
o
.
o
f
iter
atio
n
s
is
g
iv
en
as t=n
,
th
e
n
Fo
r
(
t=1
; t≤
n
)
Usi
n
g
ab
o
v
e
e
q
u
atio
n
s
u
p
d
ate
th
e
p
o
s
itio
n
o
f
s
ea
r
ch
in
g
ag
en
t
s
E
n
d
f
o
r
6)
Up
d
ate
A
an
d
C
co
ef
f
icien
ts
7)
E
v
alu
ate
f
itn
ess
f
u
n
ctio
n
f
o
r
e
ac
h
s
ea
r
ch
in
g
a
g
en
t
8)
Up
d
ate
X_
α
,
X_
β,
X
_
δ
9)
Set t=
t+1
(
iter
atio
n
co
u
n
ter
in
cr
ea
s
in
g
)
10)
R
etu
r
n
b
est s
o
lu
tio
n
X_
α
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
14
,
No
.
2
,
A
u
g
u
s
t
20
25
:
586
-
593
590
3
.
2
.
Wo
r
k
ing
princip
le
o
f
g
r
ey
wo
lf
o
pti
m
izer
T
h
e
f
o
llo
win
g
s
tep
s
o
u
tlin
e
th
e
f
u
n
ctio
n
i
n
g
o
f
th
e
GW
O
[
2
4
]
,
[
2
5
]
:
1.
Pro
b
lem
s
o
lv
in
g
t
h
r
o
u
g
h
iter
a
tio
n
s
:
GW
O
tack
les
o
p
tim
izat
io
n
p
r
o
b
lem
s
b
y
iter
ativ
ely
r
e
f
in
in
g
p
o
ten
tial
s
o
lu
tio
n
s
to
id
en
tify
t
h
e
m
o
s
t
o
p
tim
al
o
n
e.
2.
E
n
cir
clin
g
b
e
h
av
io
r
:
alg
o
r
ith
m
s
im
u
lates
wo
lv
es
en
cir
clin
g
th
eir
p
r
ey
,
r
e
p
r
esen
tin
g
a
s
ea
r
ch
s
p
ac
e
n
eig
h
b
o
r
h
o
o
d
.
T
h
is
b
eh
av
i
o
r
c
an
b
e
co
n
ce
p
tu
ally
e
x
ten
d
e
d
t
o
a
s
p
h
er
ical
r
e
g
io
n
,
as
illu
s
tr
a
ted
in
Fig
u
r
e
3
.
3.
R
o
le
o
f
co
ef
f
icien
t
v
ec
to
r
s
(
AAA
an
d
C
C
C
)
:
AAA
an
d
C
C
C
co
ef
f
icien
t
v
ec
to
r
s
e
n
ab
le
t
h
e
alg
o
r
ith
m
to
cr
ea
te
r
an
d
o
m
h
y
p
er
s
p
h
er
es
o
f
v
ar
y
in
g
r
ad
ii
ar
o
u
n
d
p
o
ten
tial
s
o
lu
tio
n
s
,
en
h
an
cin
g
th
e
ex
p
lo
r
atio
n
p
r
o
ce
s
s
.
4.
Hu
n
tin
g
m
ec
h
a
n
is
m
:
h
u
n
ti
n
g
s
tr
ateg
y
allo
ws
th
e
alg
o
r
ith
m
t
o
ap
p
r
o
x
im
ate
th
e
p
r
ey
’
s
(
o
p
tim
al
s
o
lu
tio
n
’
s
)
ex
ac
t lo
ca
tio
n
th
r
o
u
g
h
co
o
r
d
in
ated
m
o
v
em
e
n
ts
in
f
lu
en
ce
d
b
y
th
e
to
p
-
p
er
f
o
r
m
in
g
wo
lv
es.
5.
E
x
p
lo
itatio
n
a
n
d
ex
p
lo
r
atio
n
co
n
tr
o
l:
v
alu
es
o
f
p
a
r
am
eter
s
aa
a
an
d
AAA
p
lay
a
cr
itical
r
o
le
in
b
alan
cin
g
ex
p
lo
r
atio
n
(
g
lo
b
al
s
ea
r
ch
)
a
n
d
ex
p
lo
itatio
n
(
lo
ca
l r
e
f
in
em
e
n
t)
o
f
t
h
e
s
o
lu
tio
n
s
p
ac
e.
6.
I
ter
atio
n
d
y
n
a
m
ics
b
ased
o
n
AAA:
As
AAA
d
ec
r
ea
s
es
o
v
er
th
e
c
o
u
r
s
e
o
f
iter
atio
n
s
,
th
e
alg
o
r
ith
m
d
y
n
am
ically
tr
a
n
s
itio
n
s
f
r
o
m
ex
p
lo
r
atio
n
t
o
ex
p
l
o
itatio
n
,
a
llo
ca
tin
g
eq
u
al
iter
atio
n
s
f
o
r
b
o
th
p
h
ases
to
en
s
u
r
e
ef
f
ec
tiv
e
o
p
tim
izatio
n
.
Fig
u
r
e
3
.
E
x
p
an
s
io
n
o
f
s
u
r
r
o
u
n
d
in
g
f
o
r
m
i
n
to
a
s
p
h
e
r
e
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Her
e
we
h
a
v
e
tak
e
n
th
e
tab
u
lar
ap
p
ea
r
an
ce
f
o
r
co
m
p
ar
is
o
n
o
f
th
e
o
b
tain
ed
r
esu
lts
th
r
o
u
g
h
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
with
an
o
th
er
ex
is
tin
g
m
eth
o
d
s
s
o
th
at
we
ca
n
ea
s
ily
m
ak
e
co
m
p
ar
a
tiv
e
an
aly
s
is
o
f
th
e
r
esu
lts
an
d
ab
le
to
c
o
m
m
en
t
o
n
ef
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
f
o
r
r
esear
ch
wo
r
k
.
E
ac
h
ce
llu
lar
n
etwo
r
k
s
h
o
u
l
d
b
e
ab
le
to
m
o
d
if
y
t
h
et
r
an
s
m
is
s
io
n
p
ar
a
m
eter
s
in
an
ad
ap
tiv
e
m
a
n
n
er
in
r
esp
o
n
s
e
to
en
v
ir
o
n
m
en
tald
ata.
T
h
e
r
ef
o
r
e
,
it
is
im
p
o
r
tan
t
to
d
ef
in
e
th
e
cr
u
cial
p
ar
am
eter
s
,
s
u
ch
as
th
ee
n
v
ir
o
n
m
en
t
an
d
tr
an
s
m
is
s
io
n
p
ar
am
eter
s
.
I
n
o
r
d
er
to
ac
h
iev
e
o
p
tim
al
Qo
S,
GW
O
b
ased
ap
p
r
o
ac
h
is
p
r
o
p
o
s
ed
.
I
n
th
is
wo
r
k
GW
O
tech
n
iq
u
e
is
e
x
am
in
ed
with
d
if
f
er
e
n
t
d
atab
ase
f
u
n
cti
o
n
s
to
f
in
d
th
e
b
est
s
u
itab
le
f
itn
ess
f
u
n
ctio
n
f
o
r
m
in
im
u
m
B
E
R
an
d
m
in
im
u
m
p
o
wer
c
o
n
s
u
m
p
tio
n
an
d
its
o
u
tco
m
e
is
co
n
t
r
asted
with
alter
n
ativ
e
m
eth
o
d
s
lik
e
PS
O
an
d
GA
as lis
ted
b
elo
w.
A
co
m
p
ar
ativ
e
r
esu
lt
in
b
alan
ce
d
m
o
d
el,
m
u
ltime
d
ia
m
o
d
e,
em
er
g
en
cy
m
o
d
e,
a
n
d
lo
w
p
o
wer
m
o
d
el
at
v
ar
io
u
s
s
u
b
ca
r
r
ier
i
n
d
ex
in
T
ab
le
s
1
to
4
s
h
o
ws
th
at
th
e
B
E
R
an
d
p
o
wer
co
n
s
u
m
p
tio
n
is
m
in
im
ized
b
y
GW
O
alg
o
r
ith
m
as
co
m
p
ar
ed
to
th
e
PS
O
alg
o
r
ith
m
an
d
GA.
Hen
ce
f
r
o
m
th
ese
o
b
tain
ed
r
esu
lts
we
ca
n
s
ay
th
at
th
e
p
r
o
p
o
s
ed
GW
O
alg
o
r
ith
m
wo
r
k
s
v
er
y
ef
f
ec
tiv
el
y
f
o
r
ac
h
ie
v
in
g
m
in
im
u
m
B
E
R
an
d
also
r
ed
u
ce
th
e
p
o
wer
co
n
s
u
m
p
tio
n
as
co
m
p
a
r
ed
to
th
e
GA
an
d
PS
O.
T
h
er
ef
o
r
e,
we
af
f
ir
m
th
at
im
p
r
o
v
e
m
en
t
in
Qo
S
ca
n
b
e
ac
h
iev
ed
th
r
o
u
g
h
th
is
o
b
tain
e
d
m
in
im
u
m
B
E
R
an
d
r
ed
u
ce
d
p
o
wer
co
n
s
u
m
p
tio
n
i
n
wir
eless
ce
llu
lar
n
etwo
r
k
s
.
T
ab
le
1
.
C
o
m
p
a
r
ativ
e
r
esu
lt in
lo
w
p
o
wer
m
o
d
e
at
v
ar
io
u
s
s
u
b
ca
r
r
ier
in
d
ex
es
S
u
b
c
a
r
r
i
e
r
i
n
d
e
x
B
ER
P
o
w
e
r
c
o
n
su
mp
t
i
o
n
PSO
GA
G
W
O
PSO
GA
G
W
O
1
0
.
0
0
1
9
5
0
.
0
0
1
8
0
0
.
0
0
0
7
1
0
.
0
3
5
0
0
0
.
0
3
0
0
0
0
.
0
2
5
0
0
2
0
.
0
0
5
1
5
0
.
0
0
4
5
7
0
.
0
0
2
1
5
0
.
0
3
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0
0
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.
0
3
0
0
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0
2
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0
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8
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0
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0
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7
0
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0
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9
8
0
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CO
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SI
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h
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ith
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to
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GA.
Fo
r
th
e
f
u
t
u
r
e
s
tu
d
y
o
r
wo
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k
,
t
h
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en
h
a
n
ce
m
en
t o
f
th
e
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g
h
p
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d
p
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r
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s
e
allo
ca
tio
n
m
u
s
t
b
e
t
ak
en
in
to
ac
co
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n
t
to
s
u
p
p
o
r
t
th
e
b
etter
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S
in
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ce
llu
lar
n
etwo
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k
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o
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u
g
h
d
if
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er
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t
s
ch
em
es
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r
tech
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iq
u
es f
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r
g
u
a
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th
e
g
o
o
d
Q
o
S.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
No
f
u
n
d
in
g
is
in
v
o
lv
ed
in
th
is
s
tu
d
y
/p
ap
er
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
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I
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&
C
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m
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T
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,
Vo
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14
,
No
.
2
,
A
u
g
u
s
t
20
25
:
586
-
593
592
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
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M
E
N
T
Na
m
e
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f
Aut
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C
M
So
Va
Fo
I
R
D
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E
Vi
Su
P
Fu
Gaja
n
an
Uttam
Patil
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
An
ilk
u
m
ar
Du
lich
a
n
d
Vis
h
wak
ar
m
a
✓
✓
✓
✓
✓
✓
✓
✓
Priti Su
b
r
am
an
iu
m
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
T
u
s
h
ar
Hr
is
h
ik
esh
J
awa
r
e
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
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:
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:
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f
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Va
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Va
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t
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Fo
:
Fo
r
mal
a
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l
y
s
i
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:
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n
v
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s
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a
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:
R
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so
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c
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s
D
:
D
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c
a
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tac
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m
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in
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Dig
it
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fr
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s
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telleg
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c
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v
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a
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m
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fr
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m
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]
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h
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re
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S
h
e
c
a
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b
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c
o
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tac
ted
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:
p
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h
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sh
tra
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lg
a
o
n
,
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a
h
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ra
sh
tra,
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su
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tr
a
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a
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Dr.
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b
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b
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b
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k
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.
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rm
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b
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Tele
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m
m
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,
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a
lg
a
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n
,
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a
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a
ra
sh
tra,
In
d
ia.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
t
u
s
h
a
rjaw
a
re
@g
m
a
il
.
c
o
m
.
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