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o
n
tem
p
o
r
ar
y
al
g
o
r
ith
m
s
.
Far
ab
y
et
a
l.
[
1
7
]
in
tr
o
d
u
ce
d
a
s
y
n
th
esis
o
f
o
p
tim
izatio
n
s
t
r
ateg
ies
f
o
r
s
im
u
ltan
eo
u
s
d
is
tr
ib
u
tio
n
g
en
e
r
atio
n
p
lace
m
en
t,
o
p
tim
al
co
n
tr
o
l
p
r
o
b
lem
,
an
d
o
p
tim
al
n
etwo
r
k
r
ec
o
n
f
ig
u
r
atio
n
to
m
in
im
ize
lo
s
s
es
an
d
v
o
ltag
e
d
r
o
p
s
wh
ile
co
n
s
id
er
i
n
g
h
a
r
m
o
n
ic
d
is
to
r
tio
n
f
r
o
m
n
o
n
lin
ea
r
lo
ad
s
.
T
h
e
p
r
o
p
o
s
ed
s
tu
d
y
i
s
v
alid
ated
b
y
th
e
ev
alu
atio
n
o
f
th
e
I
E
E
E
3
3
-
b
u
s
test
s
tan
d
ar
d
s
y
s
tem
ac
r
o
s
s
m
u
ltip
le
MA
T
L
AB
-
b
ased
s
ce
n
ar
io
s
,
th
e
r
ea
f
ter
co
r
r
o
b
o
r
ated
b
y
co
m
p
ar
is
o
n
s
with
th
e
s
im
u
lated
an
n
ea
lin
g
an
d
f
i
r
ef
ly
(
SAF)
alg
o
r
it
h
m
s
.
T
h
e
f
in
d
in
g
s
in
d
icate
th
e
ef
f
icac
y
o
f
t
h
e
PS
O
m
eth
o
d
in
o
p
tim
izin
g
th
e
o
b
jectiv
e
f
u
n
ctio
n
u
n
d
er
s
p
ec
if
i
ed
lim
itatio
n
s
.
Su
ltan
a
an
d
R
o
y
[
1
8
]
s
u
g
g
es
ted
a
co
m
p
u
tatio
n
ally
ef
f
icie
n
t
m
eth
o
d
u
tili
zin
g
th
e
k
r
ill
h
er
d
(
KH)
alg
o
r
ith
m
to
i
d
en
tify
o
p
tim
al
OC
P
an
d
ONR
p
lace
m
e
n
t
s
aim
ed
at
m
in
im
izin
g
ac
tu
al
p
o
wer
lo
s
s
es
in
d
is
tr
ib
u
tio
n
n
etwo
r
k
s
.
Mo
r
eo
v
er
,
th
e
co
n
ce
p
t
o
f
o
p
p
o
s
itio
n
-
b
ased
lear
n
in
g
(
OB
L
)
is
in
teg
r
ated
with
th
e
p
r
o
p
o
s
ed
KH
tech
n
iq
u
e
t
o
e
n
h
an
ce
t
h
e
co
n
v
er
g
en
ce
s
p
e
ed
o
u
tc
o
m
es.
T
h
e
s
tan
d
ar
d
KH
an
d
th
e
n
o
v
el
o
p
p
o
s
itio
n
al
KH
(
OKH)
ap
p
r
o
ac
h
es a
r
e
ass
es
s
ed
o
n
3
3
-
b
u
s
an
d
6
9
-
b
u
s
s
y
s
tem
s
to
illu
s
t
r
ate
th
eir
ef
f
icac
y
an
d
s
u
p
er
io
r
ity
.
T
h
is
illu
s
tr
ates
t
h
e
ef
f
icac
y
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
f
o
r
a
d
d
r
ess
in
g
ONR
co
n
ce
r
n
s
.
Hu
s
s
ain
et
a
l.
[
1
9
]
in
tr
o
d
u
ce
d
v
ar
io
u
s
o
p
tim
izatio
n
s
tr
ateg
ies
to
d
eter
m
in
e
th
e
allo
ca
ti
o
n
o
f
th
e
ONR
an
d
OC
P
b
y
s
e
lectin
g
th
e
o
p
tim
al
o
p
en
s
witch
es
an
d
p
o
s
itio
n
in
g
OC
Ps
in
b
o
th
s
o
lo
an
d
d
u
al
R
DS
d
esig
n
s
.
T
h
e
em
p
lo
y
ed
s
tr
ateg
ies
wer
e
e
v
alu
ated
o
n
two
p
r
e
v
alen
t
n
etwo
r
k
s
(
I
E
E
E
3
3
-
bus
a
n
d
I
E
E
E
6
9
-
b
u
s
)
.
Su
b
s
eq
u
en
tly
,
a
co
m
p
ar
is
o
n
o
f
th
e
p
r
o
p
o
s
ed
s
tr
ateg
ies
was
co
n
d
u
cte
d
,
r
e
v
ea
lin
g
th
a
t
th
e
m
o
d
if
ied
b
io
g
eo
g
r
ap
h
y
-
b
ased
o
p
tim
izatio
n
(
MBB
O)
m
eth
o
d
is
th
e
m
o
s
t
ef
f
ec
tiv
e
an
d
r
ap
id
s
tr
ateg
y
f
o
r
attain
in
g
o
p
tim
al
lo
ca
tio
n
s
.
Z
h
ao
et
a
l.
[
2
0
]
o
f
f
er
ed
two
m
et
h
o
d
o
lo
g
i
es:
in
d
iv
id
u
al
ONR
an
d
ONR
s
u
cc
ee
d
ed
b
y
OC
P,
wh
ich
h
av
e
b
ee
n
em
p
lo
y
e
d
to
id
en
tif
y
th
e
o
p
tim
al
alg
o
r
ith
m
th
at
d
eliv
er
s
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
.
C
o
n
s
eq
u
en
tly
,
th
r
ee
alg
o
r
ith
m
ic
p
r
o
ce
d
u
r
es
wer
e
em
p
lo
y
ed
to
ac
h
iev
e
t
h
e
o
p
tim
al
d
esig
n
in
b
o
th
t
h
e
in
d
iv
id
u
al
an
d
d
u
al
m
eth
o
d
o
lo
g
ies.
Fu
r
th
er
m
o
r
e
,
two
p
r
ev
a
len
t
I
E
E
E
ca
s
e
s
tu
d
ies
(
3
3
-
b
u
s
an
d
6
9
-
bus
)
wer
e
em
p
lo
y
ed
to
ass
ess
th
e
o
p
ti
m
al
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
es.
T
h
e
r
ea
l
p
o
wer
lo
s
s
es
an
d
th
e
v
o
ltag
e
o
f
th
e
b
u
s
es
wer
e
co
m
p
u
ted
u
s
in
g
th
e
d
ir
ec
t
b
ac
k
wa
r
d
f
o
r
war
d
s
wee
p
m
eth
o
d
(
DB
FS
M)
.
T
h
e
r
esu
lts
in
d
icate
th
at
th
e
s
u
g
g
ested
d
u
al
tech
n
iq
u
e
ef
f
ec
tiv
ely
i
d
e
n
tifie
s
th
e
o
p
tim
al
s
o
lu
tio
n
f
o
r
s
ig
n
if
ican
t
lo
s
s
r
ed
u
ctio
n
an
d
e
n
h
an
ce
m
e
n
t
o
f
th
e
v
o
ltag
e
p
r
o
f
ile
t
h
r
o
u
g
h
th
e
MBB
O
alg
o
r
ith
m
.
T
h
is
s
tu
d
y
im
p
lem
en
ts
SS
A
an
d
W
OA
f
o
r
ONR
in
R
DS,
u
s
in
g
MA
T
L
AB
to
v
alid
ate
th
eir
p
e
r
f
o
r
m
an
ce
o
n
I
E
E
E
3
3
-
an
d
6
9
-
b
u
s
n
etwo
r
k
s
.
R
esu
lts
d
em
o
n
s
tr
ate
s
u
p
er
io
r
p
o
wer
l
o
s
s
r
ed
u
ctio
n
an
d
v
o
ltag
e
im
p
r
o
v
e
m
en
t
co
m
p
ar
ed
t
o
co
n
v
en
tio
n
al
m
eth
o
d
s
.
2.
M
E
T
H
O
D
2
.
1
.
M
ini
m
izing
re
a
l po
wer
lo
s
s
es
wit
h O
NR
T
h
e
ONR
tech
n
iq
u
e
m
in
im
i
ze
s
r
ea
l
p
o
wer
lo
s
s
es
wh
ile
k
ee
p
in
g
v
o
ltag
es
with
in
s
a
f
e
lim
i
ts
,
s
ig
n
if
ican
tly
im
p
r
o
v
i
n
g
R
DS
r
eliab
ilit
y
.
C
o
m
p
ar
ed
to
b
aselin
e
s
ce
n
ar
io
s
,
ONR
r
ed
u
ce
s
a
ctiv
e
p
o
wer
lo
s
s
es
an
d
b
o
o
s
ts
b
u
s
v
o
ltag
es.
T
h
is
s
tu
d
y
f
o
cu
s
es
o
n
lo
s
s
m
in
i
m
izatio
n
as
in
(
1
)
,
with
th
e
f
o
llo
win
g
o
b
jectiv
e
f
u
n
ctio
n
[
2
0
]
:
min
1
=
,
=
min
∑
2
=
1
(
1
)
W
h
er
e,
F
1
:
T
h
is
r
ep
r
esen
ts
th
e
f
i
r
s
t
o
b
jectiv
e
f
u
n
ctio
n
i
n
a
n
o
p
t
im
izatio
n
p
r
o
b
lem
.
I
n
th
is
co
n
tex
t,
it
is
r
elate
d
to
p
o
wer
lo
s
s
m
in
im
izatio
n
;
P
T
,
l
o
s
s
:
T
h
is
is
th
e
to
p
o
wer
lo
s
s
in
th
e
elec
tr
ical
d
is
tr
ib
u
tio
n
s
y
s
tem
,
th
e
g
o
al
is
to
m
in
im
ize
th
is
q
u
a
n
tity
;
∑
I
i
2
Nbr
i
=
1
R
i
:
T
h
is
is
a
s
u
m
m
atio
n
o
v
e
r
all
b
r
an
ch
es
(
o
r
li
n
es)
in
th
e
n
etwo
r
k
,
wh
er
e
I
in
d
ex
ea
c
h
b
r
an
ch
:
−
N
br
is
th
e
to
tal
n
u
m
b
e
r
o
f
b
r
an
c
h
e
s
in
th
e
s
y
s
tem
.
−
is
th
e
cu
r
r
en
t
f
lo
win
g
th
r
o
u
g
h
b
r
an
ch
i.
−
is
th
e
r
esis
tan
ce
o
f
b
r
a
n
ch
i.
−
2
r
ep
r
esen
ts
th
e
p
o
wer
lo
s
s
in
b
r
an
ch
i
d
u
e
to
jo
u
le
h
ea
ti
n
g
e
f
f
ec
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
16
,
No
.
4
,
Dec
em
b
er
20
25
:
2582
-
2
5
9
1
2584
W
h
er
e
,
r
ep
r
esen
ts
th
e
to
tal
lo
s
s
es
o
f
th
e
ac
tiv
e
p
o
wer
in
,
r
ep
r
esen
ts
th
e
n
o
.
o
f
th
e
b
r
a
n
c
h
es,
r
ep
r
esen
ts
th
e
cu
r
r
en
t
f
lo
w
in
th
e
b
r
an
ch
,
an
d
is
th
e
b
r
an
ch
’
s
r
esis
tan
ce
.
T
h
e
s
ec
o
n
d
o
b
jectiv
e
f
u
n
ctio
n
is
th
e
v
o
ltag
e
p
r
o
f
ile
en
h
an
ce
m
en
t
,
wh
er
e
th
e
v
o
ltag
e
m
u
s
t
b
e
k
ep
t
with
in
s
af
e
lim
its
.
T
h
e
v
o
ltag
e
o
b
jectiv
e
f
u
n
ctio
n
is
wr
itten
as in
(
2
)
[
2
1
]
.
ma
x
2
=
+
(
2
)
W
h
er
e
,
r
ep
r
esen
ts
th
e
b
u
s
v
o
l
tag
e
lim
its
,
is
th
e
lim
it
o
f
th
e
b
r
an
ch
cu
r
r
en
t,
is
th
e
r
etr
ib
u
ti
o
n
f
ac
t
o
r
o
f
th
e
b
u
s
v
o
ltag
e.
T
h
is
co
n
s
tan
t
b
ec
o
m
es
ze
r
o
w
h
en
th
e
v
o
ltag
e
o
f
th
e
b
u
s
is
with
in
p
er
m
is
s
ib
le
lim
it
s
,
is
th
e
r
etr
ib
u
tio
n
f
ac
to
r
o
f
t
h
e
cu
r
r
e
n
t
b
r
a
n
ch
,
wh
e
r
e
if
th
e
b
r
an
c
h
o
f
th
e
cu
r
r
en
t
d
o
es
n
o
t
ab
o
v
e
th
e
r
estrictio
n
s
,
it e
q
u
als ze
r
o
.
2
.
2
.
Co
ns
t
ra
ints
T
h
e
co
n
s
tr
ain
ts
th
at
s
u
b
s
tan
tiate
th
e
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
o
f
th
e
R
DS
ar
e
ca
teg
o
r
ized
in
to
tech
n
o
lo
g
ical
an
d
o
p
e
r
atio
n
al
lim
itatio
n
s
.
T
h
e
s
u
b
s
eq
u
en
t
p
ar
am
eter
s
d
elin
ea
te
th
e
tec
h
n
ical
co
n
s
tr
ain
ts
f
o
r
b
u
s
v
o
ltag
e
a
n
d
b
r
an
ch
cu
r
r
e
n
t a
s
in
(
3
)
an
d
(
4
)
[
2
1
]
.
≤
|
|
≤
(
3
)
|
|
≤
,
(
4
)
W
h
er
e
r
ep
r
esen
ts
th
e
v
o
ltag
e
’
s
m
ag
n
itu
d
e
f
o
r
th
e
b
u
s
,
an
d
ar
e
th
e
m
ax
im
u
m
a
n
d
m
in
im
u
m
v
o
ltag
es,
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95
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r
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r
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th
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c
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ly
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itio
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tire
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R
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as sh
o
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(
5
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≤
(
5
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On
th
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d
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atio
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eter
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ix
[
A]
as
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n
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ir
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[
2
0
]
,
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2
1
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o
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P
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(
6
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=
+
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(
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3.
NE
T
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RK
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NF
I
G
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RATI
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P
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M
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T
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3
.
1
.
Wha
le
o
pti
m
iza
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io
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l
g
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rit
hm
(
WO
A)
o
pti
m
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io
n
T
h
e
W
OA
is
a
h
eu
r
is
tic
m
eth
o
d
o
lo
g
y
th
at
e
m
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lates
th
e
h
u
n
tin
g
tactics
o
f
h
u
m
p
b
ac
k
w
h
ales
[
2
2
]
.
T
h
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alg
o
r
ith
m
o
f
f
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ad
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n
ta
g
es
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ch
as
th
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av
o
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lo
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tim
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ap
id
co
n
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er
g
en
ce
[
2
2
]
,
[
2
3
]
.
I
n
itially
,
th
e
s
ea
r
ch
ag
en
ts
a
r
e
d
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p
atch
ed
to
lo
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r
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g
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h
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x
p
lo
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p
h
ase,
af
ter
wh
ich
th
eir
p
lace
m
en
ts
ar
e
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ju
s
ted
to
alig
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with
th
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n
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r
est
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u
p
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to
th
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tim
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m
.
C
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eq
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e
ex
p
lo
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p
h
ase
m
ig
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t b
e
ar
ticu
lated
as in
(
7
)
-
(
10
)
[
2
3
]
.
⃗
⃗
=
|
∗
(
)
−
(
)
|
(
7
)
(
+
1
)
=
∗
(
)
−
.
⃗
⃗
(
8
)
=
2
.
−
(
9
)
=
2
(
1
0
)
T
h
e
k
e
y
p
ar
am
eter
s
i
n
clu
d
e:
∗
(
)
(
b
est
ag
e
n
t
lo
ca
tio
n
)
,
A
⊕
C
(
co
ef
f
icien
t
v
ec
to
r
s
)
,
t
(
cu
r
r
en
t
iter
atio
n
)
,
∗
(
)
(
p
o
s
itio
n
v
ec
to
r
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,
(
lin
ea
r
d
ec
r
ea
s
e
2
→0
)
,
an
d
(
r
an
d
o
m
v
ec
to
r
[
0
,
1
]
)
.
as
i
n
(
11
)
an
d
(
12
)
.
T
h
e
ex
p
lo
itatio
n
p
h
ase
u
s
es
b
u
b
b
le
-
n
et
f
o
r
ag
in
g
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ia
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m
ec
h
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is
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s
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as
s
h
o
w
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in
Fig
u
r
e
1
:
s
h
r
in
k
in
g
en
cir
clin
g
an
d
s
p
ir
al
u
p
d
atin
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
E
n
h
a
n
ce
d
vo
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g
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s
ta
b
ilit
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p
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ks t
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Mo
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2585
(
+
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1
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1
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ab
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b
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⃗
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[
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b
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b
b
le
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d
,
wh
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iv
e
d
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b
elo
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s
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f
f
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s
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u
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les
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e
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ale
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er
ally
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e
ef
f
o
r
t
,
f
o
llo
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b
y
th
e
r
est
o
f
th
e
g
r
o
u
p
.
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h
e
lead
er
will
u
s
u
ally
b
e
r
esp
o
n
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ib
le
f
o
r
b
lo
win
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th
e
b
u
b
b
les
,
an
d
th
e
o
th
er
m
em
b
er
s
will
s
u
r
r
o
u
n
d
th
e
f
is
h
,
f
o
llo
win
g
t
h
em
to
th
e
s
u
r
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ac
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b
y
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s
p
ir
al
p
atter
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to
k
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f
is
h
tr
a
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ed
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Hu
m
p
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ar
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o
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in
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th
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e
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itter
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t
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g
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s
h
f
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t
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s
c
h
o
o
l
t
h
ey
h
a
v
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r
r
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.
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w
o
m
ath
em
atica
l
m
o
d
els
h
av
e
b
ee
n
p
r
o
p
o
s
ed
to
m
im
ic
th
e
wh
ale
p
er
f
o
r
m
a
n
ce
wh
ile
attac
k
in
g
th
eir
p
r
a
y
s
:
T
h
e
s
h
r
in
k
in
g
e
n
c
ir
clin
g
m
ec
h
a
n
is
m
an
d
Sp
ir
al
u
p
d
atin
g
p
o
s
itio
n
.
T
o
u
p
d
ate
t
h
e
wh
ales’
p
o
s
itio
n
ar
o
u
n
d
th
e
b
est
s
o
lu
tio
n
in
th
e
s
ea
r
ch
s
p
ac
e,
th
e
s
h
r
in
k
in
g
en
cir
clin
g
m
ec
h
a
n
is
m
m
im
ics
th
is
p
r
o
ce
s
s
.
T
o
m
o
d
el
th
e
s
h
r
in
k
in
g
,
en
cir
cli
n
g
,
an
d
s
p
ir
al
s
wim
m
in
g
b
eh
av
io
r
s
,
a
p
r
o
b
a
b
ilit
y
o
f
5
0
%
is
ass
u
m
ed
to
s
elec
t
b
etwe
en
th
ese
two
b
eh
av
io
r
s
th
r
o
u
g
h
o
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t
th
e
c
o
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r
s
e
o
f
o
p
ti
m
izati
o
n
.
E
ac
h
wh
ale
s
elec
ts
th
e
o
p
er
atio
n
t
o
b
e
p
er
f
o
r
m
ed
r
a
n
d
o
m
ly
b
ased
o
n
its
lo
ca
tio
n
with
r
esp
ec
t to
th
e
o
p
tim
al
s
o
lu
tio
n
s
o
f
a
r
.
Fig
u
r
e
1
.
A
b
u
b
b
le
n
et
s
ea
r
ch
s
tr
ateg
y
:
(
a)
h
u
m
p
b
ac
k
wh
ales'
b
u
b
b
le
-
n
et
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tin
g
,
(
b
)
m
ec
h
a
n
is
m
o
f
th
e
s
h
r
in
k
in
g
e
n
cir
clin
g
,
a
n
d
(
c)
u
p
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atin
g
o
f
th
e
s
p
ir
al
lo
ca
ti
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n
3
.
2
.
Sa
lp
s
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rm
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rit
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f
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nced
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o
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it
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T
h
e
SS
A
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ted
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ased
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g
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ee
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io
r
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f
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in
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r
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s
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ated
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Fig
u
r
e
2
.
Ho
we
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er
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s
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ied
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t
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o
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ee
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r
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in
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ed
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th
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e
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etu
s
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s
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ac
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ity
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ce
m
o
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ilit
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th
r
o
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g
h
r
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h
r
o
n
ize
d
m
o
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em
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d
f
o
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ag
in
g
,
as
in
(
13
)
.
T
h
e
s
u
b
s
eq
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en
t
eq
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a
tio
n
is
p
r
o
p
o
s
ed
to
r
ev
is
e
th
e
lead
er
'
s
p
o
s
itio
n
[
2
3
]
.
1
=
{
+
1
(
(
−
)
1
+
)
,
3
≥
0
−
1
(
(
−
)
2
+
)
,
3
<
0
(
1
3
)
T
h
e
p
ar
am
ete
r
s
o
f
(
1
3
)
ar
e
d
e
f
in
ed
as
f
o
ll
o
ws:
1
d
en
o
tes
th
e
in
itial
lo
ca
tio
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o
f
th
e
s
alp
in
th
e
ℎ
d
im
en
s
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,
Fd
r
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r
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e
f
o
o
d
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o
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r
ce
'
s
lo
ca
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,
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e
th
e
u
p
p
er
a
n
d
lo
we
r
b
o
u
n
d
s
o
f
th
e
ℎ
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
16
,
No
.
4
,
Dec
em
b
er
20
25
:
2582
-
2
5
9
1
2586
d
im
en
s
io
n
,
a
n
d
C
2
a
n
d
C
3
[
0
,
1
]
ar
e
t
h
e
r
a
n
d
o
m
co
ef
f
icien
ts
.
Mo
r
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v
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r
,
th
e
co
n
s
tan
t
1
is
wr
itten
u
s
in
g
(
1
4
)
[
2
4
]
-
[
2
6
]
.
1
=
2
×
−
(
)
2
(
1
4
)
W
h
er
e
d
en
o
tes
all
iter
atio
n
s
o
f
th
e
alg
o
r
ith
m
,
an
d
d
en
o
t
es
th
e
cu
r
r
en
t
iter
atio
n
o
f
th
e
alg
o
r
ith
m
.
T
h
e
f
o
llo
wer
'
s
lo
ca
tio
n
ca
n
b
e
g
i
v
e
n
as
(
15
).
=
1
2
(
+
−
1
)
(
1
5
)
W
h
er
e
≥
2
,
is
th
e
lo
ca
tio
n
o
f
ℎ
f
o
l
lo
wer
s
alp
.
Fig
u
r
e
2
.
Stru
ctu
r
e
o
f
th
e
s
alp
s
war
m
o
p
tim
izatio
n
:
(
a)
s
ig
n
al
lead
er
s
alp
an
d
(
b
)
s
lap
f
o
llo
wer
s
3.
3
.
Ass
um
ptio
ns
m
a
de
in t
he
s
t
ud
y
Ass
u
m
p
tio
n
s
m
ad
e
in
th
e
s
tu
d
y
:
i)
Stab
le
n
etwo
r
k
c
o
n
d
iti
o
n
s
:
T
h
e
s
tu
d
y
ass
u
m
es
a
s
tead
y
-
s
tate
p
o
wer
s
y
s
tem
with
o
u
t
s
u
d
d
en
ch
an
g
es
in
lo
ad
o
r
g
e
n
er
atio
n
;
ii)
I
d
ea
l
p
ar
am
eter
s
elec
tio
n
:
T
h
e
p
ar
am
eter
s
u
s
ed
f
o
r
SS
A
an
d
W
OA
ar
e
a
s
s
u
m
ed
to
b
e
o
p
tim
al
with
o
u
t
co
n
s
id
er
in
g
r
ea
l
-
tim
e
tu
n
in
g
;
iii)
Acc
u
r
ate
p
o
wer
s
y
s
tem
m
o
d
el
:
T
h
e
s
y
s
tem
m
o
d
el
u
s
ed
in
th
e
s
tu
d
y
is
c
o
n
s
id
er
ed
id
ea
l,
with
o
u
t
ac
co
u
n
tin
g
f
o
r
ex
ter
n
al
d
is
tu
r
b
an
ce
s
o
r
m
ea
s
u
r
em
e
n
t
in
ac
cu
r
ac
i
es
;
an
d
iv
)
Fair
co
m
p
ar
is
o
n
en
v
ir
o
n
m
e
n
t
:
B
o
th
alg
o
r
ith
m
s
ar
e
test
ed
u
n
d
er
i
d
en
tical
co
n
d
itio
n
s
,
ass
u
m
in
g
th
at
e
n
v
ir
o
n
m
en
tal
f
ac
t
o
r
s
d
o
n
o
t f
av
o
r
o
n
e
o
v
er
th
e
o
th
er
.
3.
4
.
J
us
t
if
ica
t
io
n f
o
r
s
elec
t
in
g
WO
A
a
nd
SS
A
T
h
e
s
tu
d
y
em
p
lo
y
s
W
OA
an
d
SS
A
d
u
e
to
th
eir
p
r
o
v
en
s
u
cc
ess
in
co
m
p
lex
p
o
w
er
s
y
s
tem
o
p
tim
izatio
n
.
W
OA
m
im
ics
h
u
m
p
b
ac
k
wh
ales'
b
u
b
b
le
-
n
et
h
u
n
tin
g
,
b
alan
cin
g
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
.
SS
A
r
ep
licates
s
alp
ch
ain
b
eh
av
i
o
r
,
m
ain
tain
in
g
s
tab
le
o
p
tim
izatio
n
th
r
o
u
g
h
d
y
n
am
ic
ex
p
lo
r
atio
n
-
ex
p
lo
itatio
n
b
alan
ce
.
B
o
th
alg
o
r
ith
m
s
o
u
tp
er
f
o
r
m
class
ical
an
d
m
eta
h
eu
r
is
tic
m
eth
o
d
s
in
a
cc
u
r
ac
y
an
d
s
p
ee
d
,
ex
ce
llin
g
in
n
o
n
lin
ea
r
,
m
u
ltim
o
d
al,
an
d
co
n
s
tr
ain
e
d
p
o
wer
d
is
tr
ib
u
tio
n
p
r
o
b
lem
s
.
T
h
eir
f
as
t c
o
n
v
er
g
e
n
ce
,
lo
w
co
m
p
u
tatio
n
al
d
em
an
d
,
an
d
i
n
d
ep
en
d
en
ce
f
r
o
m
in
itial
v
alu
es
o
r
d
er
iv
ativ
es
m
ak
e
th
em
r
o
b
u
s
t
f
o
r
r
ea
l
-
tim
e
an
d
lar
g
e
-
s
ca
le
ap
p
licatio
n
s
,
i
d
ea
l f
o
r
t
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
e
R
DSN,
th
e
lo
ad
f
lo
w
(
L
F)
an
aly
s
is
n
ee
d
s
to
b
e
p
er
f
o
r
m
e
d
in
th
e
o
p
tim
iz
atio
n
p
r
o
ce
s
s
to
o
b
tain
th
e
lo
s
s
es
an
d
v
o
ltag
e
d
ev
iatio
n
o
f
th
e
DS.
T
h
e
b
ac
k
war
d
-
f
o
r
war
d
(
B
F)
tech
n
iq
u
e
r
ep
lace
d
th
e
New
to
n
R
ap
h
s
o
n
,
Gau
s
s
Seid
el
o
r
an
y
o
th
er
tr
ad
itio
n
al
L
F
s
ch
em
es
th
at
ev
o
lv
ed
in
th
e
liter
atu
r
e
to
r
ea
lize
th
e
o
b
jectiv
e
f
u
n
ctio
n
p
r
o
p
o
s
ed
as
th
e
B
F
tech
n
iq
u
e
d
o
m
in
ates
p
r
ev
io
u
s
tech
n
i
q
u
es.
T
h
e
co
m
p
a
r
is
o
n
o
f
W
OA
an
d
SS
A
wa
s
p
r
o
g
r
a
m
m
e
d
i
n
MA
T
L
AB
2
0
2
1
a
t
o
c
h
e
ck
t
h
e
e
f
f
ec
ti
v
e
n
ess
o
f
b
o
t
h
th
e
W
O
A
a
n
d
S
SA
alg
o
r
it
h
m
.
T
h
e
p
r
o
p
o
s
e
d
c
o
d
e
im
p
le
m
e
n
t
ati
o
n
s
f
o
r
b
o
t
h
a
lg
o
r
it
h
m
s
a
r
e
e
x
ec
u
t
e
d
o
n
a
p
o
r
ta
b
le
c
o
m
p
u
t
er
u
s
i
n
g
a
C
o
r
e
i
7
-
1
0
7
5
0
H
C
P
U
a
t
2
.
6
0
GHz
wit
h
1
6
GB
o
f
R
A
M.
T
h
e
W
OA
a
n
d
SS
A
o
p
ti
m
i
za
t
io
n
tec
h
n
i
q
u
es
we
r
e
im
p
le
m
e
n
t
ed
o
n
t
h
e
I
E
E
E
3
3
a
n
d
6
9
b
u
s
d
is
tr
ib
u
tio
n
s
y
s
tem
s
to
v
alid
ate
th
e
e
f
f
icac
y
o
f
th
e
s
e
alg
o
r
ith
m
s
.
4
.
1
.
WO
A
a
nd
SS
A
a
lg
o
rit
h
m
a
pp
lica
t
io
n
Fig
u
r
e
3
illu
s
tr
ates
th
e
s
in
g
le
lin
e
d
iag
r
am
o
f
th
e
I
E
E
E
3
3
b
u
s
d
is
tr
ib
u
tio
n
s
y
s
tem
u
tili
z
ed
in
th
is
co
n
tex
t.
T
h
e
d
is
tr
ib
u
tio
n
s
y
s
tem
o
p
er
ates
at
1
2
.
6
6
k
V
an
d
1
0
0
MV
A,
wh
er
ea
s
th
e
lo
ad
v
alu
es
ar
e
3
7
1
5
k
W
an
d
2
3
0
0
k
VAR,
as
d
ep
icted
in
[
2
7
]
.
Fu
r
t
h
er
m
o
r
e,
in
th
e
W
OA
alg
o
r
ith
m
,
th
e
q
u
a
n
tity
o
f
s
ea
r
ch
a
g
en
ts
r
an
g
es
f
r
o
m
1
0
to
2
0
,
with
a
m
ax
im
u
m
o
f
1
0
0
iter
atio
n
s
.
T
h
e
SS
A
alg
o
r
ith
m
em
p
lo
y
s
a
p
o
p
u
latio
n
s
ize
o
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ates
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izatio
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2
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O
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k
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9
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s
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tem
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s
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ated
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u
r
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.
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h
e
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DS
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ar
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eter
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ated
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d
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0
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6
6
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ates
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izatio
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e
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r
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o
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th
e
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u
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ates
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g
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e
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il
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e
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e
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e
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icted
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Fig
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izatio
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o
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ates
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I
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I
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w
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&
Dr
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Vo
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16
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4
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Dec
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20
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p
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(
ONR)
em
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tic
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: sli
m
e
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war
m
o
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tim
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(
SS
A)
an
d
wh
ale
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
OA)
.
T
h
e
m
at
h
em
at
ical
f
o
r
m
u
latio
n
s
o
f
th
e
o
b
jectiv
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f
u
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ctio
n
s
f
o
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t
h
e
s
u
g
g
ested
two
alg
o
r
ith
m
s
ar
e
in
itially
p
r
esen
ted
,
f
o
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wed
b
y
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n
a
n
aly
s
is
o
f
t
h
e
p
r
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ce
s
s
es
o
f
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d
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id
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n
tify
th
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o
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ti
m
al
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tio
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th
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g
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f
i
g
u
r
atio
n
.
T
h
e
MA
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AB
en
v
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r
o
n
m
en
t
is
u
tili
ze
d
to
im
p
lem
en
t
th
e
alg
o
r
ith
m
s
f
o
r
th
e
I
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E
3
3
an
d
6
9
b
u
s
n
etwo
r
k
s
.
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h
e
ar
ch
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d
f
in
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in
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em
o
n
s
tr
ated
th
at
th
e
p
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p
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s
ed
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A
alg
o
r
ith
m
is
th
e
m
o
s
t
ef
f
ec
tiv
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ap
p
r
o
ac
h
f
o
r
m
in
im
izin
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s
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im
p
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o
v
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g
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s
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es
in
co
m
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s
.
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d
itio
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ally
,
th
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m
in
im
u
m
b
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o
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p
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o
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to
th
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was
0
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(
b
ase
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,
wh
ich
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9
5
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win
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e
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f
o
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3
3
b
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s
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tili
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g
th
e
SS
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tech
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h
e
m
in
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m
v
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ag
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in
th
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6
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s
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p
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i
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r
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eter
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ase
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
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lec
&
Dr
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s
t
I
SS
N:
2088
-
8
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p
lo
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in
g
th
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SS
A
o
p
tim
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n
tec
h
n
iq
u
e.
Desp
ite
th
eir
s
tr
en
g
th
s
,
W
OA
an
d
SS
A
h
av
e
lim
itatio
n
s
:
i
)
Hig
h
co
m
p
u
tatio
n
al
co
m
p
lex
it
y
in
lar
g
e
-
s
ca
le
s
y
s
tem
s
waste
s
r
eso
u
r
ce
s
;
ii
)
SS
A’
s
s
tr
o
n
g
ex
p
lo
itat
io
n
r
is
k
s
lo
ca
l
o
p
tim
a,
wh
ile
W
OA
s
u
f
f
er
s
f
r
o
m
s
lo
w
co
n
v
er
g
en
ce
;
iii
)
Per
f
o
r
m
an
ce
is
h
ig
h
ly
p
ar
a
m
eter
-
d
e
p
en
d
en
t,
r
ed
u
cin
g
ef
f
ec
tiv
e
n
e
s
s
if
p
o
o
r
ly
tu
n
ed
;
iv
)
Scalab
ilit
y
d
ec
lin
es
with
s
y
s
tem
s
ize,
h
in
d
er
in
g
p
r
ac
ti
ca
l
u
s
e;
v
)
R
ea
l
-
wo
r
ld
co
m
p
lex
ities
(
e.
g
.
,
tim
e
-
v
ar
y
in
g
lo
ad
s
,
u
n
ce
r
tain
ties
)
ar
e
o
v
er
l
o
o
k
e
d
,
lim
itin
g
ap
p
l
icab
ilit
y
.
Ad
d
r
ess
in
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th
ese
is
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co
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en
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n
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th
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z
is
a
lec
tu
re
r
in
th
e
El
e
c
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
De
p
a
rtme
n
t
a
t
Al
-
M
u
th
a
n
n
a
Un
iv
e
rsity
,
Ira
q
.
He
re
c
e
iv
e
d
h
is
B.
S
c
.
i
n
M
e
c
h
a
tr
o
n
ic
En
g
i
n
e
e
rin
g
fro
m
th
e
Un
i
v
e
rsity
o
f
Ba
g
h
d
a
d
i
n
2
0
0
7
a
n
d
h
is
M
.
S
c
.
i
n
M
e
c
h
a
tro
n
ic
En
g
i
n
e
e
rin
g
fr
o
m
t
h
e
U
n
iv
e
rsit
y
o
f
Ba
g
h
d
a
d
i
n
2
0
1
5
,
w
h
e
re
h
is
th
e
sis
fo
c
u
se
d
o
n
In
tern
e
t
-
b
a
se
d
c
o
n
tro
l
o
f
a
m
u
lt
i
-
p
u
r
p
o
se
ro
b
o
t.
He
h
a
s
b
e
e
n
with
Al
-
M
u
th
a
n
n
a
Un
iv
e
rsit
y
sin
c
e
2
0
1
5
a
n
d
is
c
u
rre
n
tl
y
p
u
rsu
i
n
g
a
P
h
.
D.
in
C
o
n
tr
o
l
E
n
g
i
n
e
e
rin
g
.
He
h
a
s
tau
g
h
t
c
o
u
rse
s
in
c
lu
d
in
g
El
e
c
tri
c
a
l
Circu
it
s
(th
e
o
ry
a
n
d
lab
),
M
a
th
e
m
a
ti
c
s
(e
lem
e
n
tary
a
n
d
a
d
v
a
n
c
e
d
)
,
a
n
d
P
ro
g
ra
m
m
in
g
.
His
re
se
a
rc
h
i
n
t
e
re
sts
sp
a
n
a
d
a
p
ti
v
e
c
o
n
tro
l
,
s
m
a
rt
e
n
e
rg
y
s
y
ste
m
s,
d
e
e
p
re
in
fo
rc
e
m
e
n
t
lea
rn
in
g
fo
r
c
o
n
tr
o
l,
ro
b
o
t
ics
,
a
n
d
c
lea
n
e
n
e
rg
y
.
An
a
c
ti
v
e
IEE
E
m
e
m
b
e
r,
h
e
ro
u
ti
n
e
ly
e
m
p
l
o
y
s
M
ATLAB
a
n
d
Lab
VIEW
in
tea
c
h
i
n
g
a
n
d
re
se
a
rc
h
.
His
c
u
rre
n
t
wo
r
k
a
ims
to
b
ri
d
g
e
i
n
telli
g
e
n
t
c
o
n
tro
l
wit
h
p
ra
c
ti
c
a
l
m
e
c
h
a
tro
n
ic
a
n
d
re
n
e
wa
b
le
-
e
n
e
rg
y
a
p
p
l
ica
ti
o
n
s,
e
n
h
a
n
c
in
g
r
o
b
u
stn
e
ss
a
n
d
re
a
l
-
wo
rld
p
e
rfo
rm
a
n
c
e
in
in
d
u
strial
e
lec
tro
n
ics
a
n
d
m
icro
g
r
i
d
c
o
n
tex
ts.
He
is
a
lso
in
tere
ste
d
i
n
c
o
ll
a
b
o
ra
ti
o
n
o
n
a
p
p
l
ied
p
r
o
jec
ts
th
a
t
c
o
n
n
e
c
t
a
c
a
d
e
m
ia
with
l
o
c
a
l
in
d
u
str
y
a
n
d
so
c
iet
y
,
wit
h
a
n
e
m
p
h
a
sis
o
n
s
u
sta
in
a
b
il
it
y
a
n
d
tec
h
n
o
l
o
g
y
tran
sfe
r.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
o
h
a
m
m
a
d
.
z
u
h
a
ir@m
u
.
e
d
u
.
iq
.
Abb
a
s
S
w
a
y
e
h
Ati
y
a
h
is
a
l
e
c
tu
re
r
in
t
h
e
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
s
En
g
in
e
e
ri
n
g
a
t
Al
-
M
u
th
a
n
n
a
Un
iv
e
rsity
,
Ira
q
.
H
e
re
c
e
iv
e
d
h
is
B.
S
c
.
d
e
g
re
e
in
El
e
c
tri
c
a
l
E
n
g
in
e
e
rin
g
fr
o
m
t
h
e
Un
iv
e
rsit
y
o
f
Ba
g
h
d
a
d
,
Ira
q
,
i
n
2
0
0
3
a
n
d
h
is
M
.
S
c
.
d
e
g
re
e
in
El
e
c
tri
c
a
l
P
o
we
r
E
n
g
i
n
e
e
ri
n
g
fro
m
S
o
u
t
h
R
u
ss
ia
S
tate
Tec
h
n
ica
l
Un
i
v
e
rsity
,
Ru
ss
ia,
i
n
2
0
1
4
.
He
h
a
s
b
e
e
n
a
fa
c
u
lt
y
m
e
m
b
e
r
a
t
Al
-
M
u
t
h
a
n
n
a
Un
i
v
e
rsity
sin
c
e
2
0
1
4
a
n
d
is
c
u
rre
n
tl
y
p
u
rsu
i
n
g
h
is
P
h
.
D.
in
e
lec
tri
c
a
l
p
o
we
r
e
n
g
in
e
e
rin
g
.
His
re
se
a
rc
h
in
tere
sts
e
n
c
o
m
p
a
ss
a
b
ro
a
d
sp
e
c
tru
m
o
f
p
o
we
r
sy
ste
m
s,
in
c
lu
d
i
n
g
p
o
we
r
e
lec
tro
n
ics
,
re
n
e
wa
b
le
e
n
e
rg
y
in
teg
ra
ti
o
n
,
a
rti
ficia
l
in
telli
g
e
n
c
e
a
p
p
li
c
a
ti
o
n
s
in
p
o
we
r
e
n
g
i
n
e
e
rin
g
,
in
tell
i
g
e
n
t
c
o
n
tro
l
sy
ste
m
s,
a
n
d
in
d
u
strial
e
lec
tro
n
ics
.
His
w
o
rk
fo
c
u
se
s
o
n
d
e
v
e
lo
p
i
n
g
i
n
n
o
v
a
ti
v
e
so
lu
ti
o
n
s
t
o
e
n
h
a
n
c
e
th
e
e
fficie
n
c
y
,
sta
b
il
it
y
,
a
n
d
i
n
telli
g
e
n
c
e
o
f
m
o
d
e
rn
e
lec
tri
c
a
l
p
o
we
r
s
y
ste
m
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
b
b
a
ss
wa
y
e
h
2
2
@m
u
.
e
d
u
.
iq
.
Y
a
q
d
h
a
n
M
a
h
m
o
o
d
H
u
s
s
e
i
n
w
a
s
b
o
r
n
i
n
S
a
m
a
wa
h
,
I
r
a
q
,
i
n
1
9
9
1
.
H
e
r
e
c
e
i
v
e
d
t
h
e
B
.
S
.
i
n
c
o
m
p
u
t
e
r
t
e
c
h
n
i
q
u
e
s
e
n
g
i
n
e
e
r
i
n
g
i
n
2
0
1
4
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2
0
1
5
f
r
o
m
I
s
l
a
m
i
c
U
n
i
v
e
r
s
i
t
y
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o
l
l
e
g
e
i
n
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a
j
a
f
C
i
t
y
,
a
n
d
M
.
S
.
d
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g
r
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e
s
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t
r
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c
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n
g
i
n
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r
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n
g
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t
e
l
e
c
o
m
m
u
n
i
c
a
t
io
n
s
y
s
t
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)
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r
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m
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n
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n
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k
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l
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a
l
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y
s
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M
e
la
k
a
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T
e
M
)
,
M
a
l
a
y
s
ia
,
i
n
2
0
1
8
.
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e
c
u
r
re
n
t
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t
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h
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l
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a
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n
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o
h
o
r
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a
h
r
u
c
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t
y
.
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m
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n
e
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k
s
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e
c
a
n
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t
a
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e
d
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m
a
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l
:
y
a
q
th
a
n
m
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9
@
g
m
a
i
l
.
c
o
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.
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a
te
m
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d
a
y
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a
n
o
o
sh
wa
s
b
o
r
n
in
S
a
m
a
wa
h
,
Ira
q
,
i
n
1
9
9
1
.
He
re
c
e
iv
e
d
th
e
B.
S
.
d
e
g
re
e
in
Co
m
p
u
ter
Tec
h
n
iq
u
e
s
En
g
in
e
e
rin
g
fro
m
t
h
e
Isla
m
ic
Un
iv
e
rsity
Co
ll
e
g
e
,
Na
jaf,
in
2
0
1
4
;
t
h
e
M
.
En
g
.
d
e
g
re
e
in
El
e
c
tro
n
ic
E
n
g
i
n
e
e
rin
g
(Tele
c
o
m
m
u
n
ica
ti
o
n
S
y
ste
m
s)
fro
m
Un
iv
e
rsiti
Tek
n
ik
a
l
M
a
lay
si
a
M
e
lak
a
(UTe
M
),
M
a
lay
sia
,
i
n
2
0
1
8
;
a
n
d
th
e
P
h
.
D
.
d
e
g
re
e
in
Co
m
m
u
n
ica
ti
o
n
s
with
a
fo
c
u
s
o
n
m
il
li
m
e
ter
-
wa
v
e
No
len
-
m
a
tri
x
wa
v
e
g
u
id
e
s
fro
m
Un
i
v
e
rsiti
Tek
n
o
lo
g
i
M
a
la
y
sia
(UTM
),
Jo
h
o
r
Ba
h
ru
,
i
n
2
0
2
3
.
He
is
c
u
rr
e
n
tl
y
a
P
ro
fe
ss
o
r
with
t
h
e
De
p
a
rtme
n
t
o
f
E
lec
tro
n
ic
s
a
n
d
C
o
m
m
u
n
ica
ti
o
n
s
E
n
g
i
n
e
e
rin
g
,
Al
-
M
u
th
a
n
n
a
U
n
iv
e
rsit
y
,
Ira
q
.
His
re
se
a
rc
h
in
tere
sts
s
p
a
n
a
p
p
li
e
d
e
lec
tro
m
a
g
n
e
ti
c
s
a
n
d
a
n
te
n
n
a
e
n
g
in
e
e
rin
g
,
in
c
l
u
d
i
n
g
t
h
e
a
n
a
ly
sis,
d
e
sig
n
,
a
n
d
o
p
ti
m
iza
ti
o
n
o
f
d
iele
c
tri
c
re
so
n
a
to
r
a
n
ten
n
a
s (
DRA
s),
wa
v
e
g
u
id
e
a
n
d
slo
t
a
n
ten
n
a
s,
re
flec
t
a
rra
y
a
n
ten
n
a
s,
a
n
d
No
len
-
m
a
tri
x
-
b
a
se
d
stru
c
tu
re
s
fo
r
m
il
li
m
e
ter
-
wa
v
e
a
p
p
li
c
a
ti
o
n
s.
His
wo
rk
targ
e
ts
h
ig
h
-
e
fficie
n
c
y
,
wi
d
e
-
b
a
n
d
,
a
n
d
b
e
a
m
-
c
o
n
tro
ll
a
b
le
ra
d
iat
o
rs
su
it
a
b
le
f
o
r
n
e
x
t
-
g
e
n
e
ra
ti
o
n
wir
e
les
s
sy
ste
m
s.
In
a
d
d
it
io
n
t
o
h
i
s
re
se
a
rc
h
,
h
e
tea
c
h
e
s
a
n
d
su
p
e
rv
ise
s
p
ro
jec
ts
in
RF
/mic
ro
wa
v
e
c
ircu
it
s
a
n
d
a
n
ten
n
a
d
e
sig
n
,
wit
h
a
n
e
m
p
h
a
sis
o
n
p
ra
c
ti
c
a
l
p
ro
to
ty
p
in
g
a
n
d
m
e
a
su
re
m
e
n
t.
He
is
a
c
ti
v
e
ly
e
n
g
a
g
e
d
in
a
c
a
d
e
m
ic
se
rv
ice
a
n
d
c
o
ll
a
b
o
ra
ti
o
n
,
a
imin
g
t
o
tran
sla
t
e
rig
o
r
o
u
s
e
lec
tro
m
a
g
n
e
ti
c
d
e
si
g
n
i
n
t
o
ro
b
u
st
,
re
a
l
-
wo
rl
d
wire
les
s so
lu
ti
o
n
s.
He
c
a
n
b
e
c
o
n
t
a
c
ted
a
t
e
m
a
il
:
h
a
tem
.
a
lt
a
e
e
1
9
9
0
@g
m
a
il
.
c
o
m
.
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