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nte
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t
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na
l J
o
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nfo
rm
a
t
ics a
nd
Co
m
m
un
ica
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n T
ec
hn
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g
y
(
I
J
-
I
CT
)
Vo
l.
9
,
No
.
3
,
Dec
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er
2
0
2
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,
p
p
.
17
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~
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ia
esco
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e.
co
m
Power
lo
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red
uct
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by
g
ry
llida
e
o
ptimiza
tion
a
lg
o
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thm
K
a
na
g
a
s
a
ba
i
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enin
De
p
a
rtme
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t
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EE
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ra
sa
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o
tl
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id
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h
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rt
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stit
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lo
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d
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icle
I
nfo
AB
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A
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ticle
his
to
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y:
R
ec
eiv
ed
No
v
1
2
,
20
19
R
ev
is
ed
J
an
20,
20
20
Acc
ep
ted
Feb
19
,
20
20
Th
is
p
a
p
e
r
p
r
o
jec
ts
G
ry
ll
id
a
e
Op
ti
m
iza
ti
o
n
Alg
o
rit
h
m
(G
OA
)
h
a
s
b
e
e
n
a
p
p
li
e
d
to
so
l
v
e
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
p
ro
b
lem
.
P
r
o
p
o
se
d
G
OA
a
p
p
r
o
a
c
h
is
b
a
se
d
on
th
e
c
h
ir
p
in
g
c
h
a
ra
c
teristics
of
G
ry
ll
i
d
a
e
.
In
c
o
m
m
o
n
,
m
a
le
G
ry
ll
id
a
e
c
h
irp
,
on
th
e
o
th
e
r
h
a
n
d
s
o
m
e
fe
m
a
le
G
ry
ll
id
a
e
a
lso
do
as
we
ll
.
M
a
le
G
ry
ll
id
a
e
d
ra
w
th
e
fe
m
a
les
by
th
is
so
u
n
d
w
h
ich
th
e
y
p
r
o
d
u
c
e
.
M
o
re
o
v
e
r,
t
h
e
y
c
a
u
ti
o
n
th
e
o
t
h
e
r
G
ry
ll
id
a
e
a
g
a
i
n
st
d
a
n
g
e
rs
with
t
h
is
so
u
n
d
.
Th
e
h
e
a
rin
g
o
rg
a
n
s
of
th
e
G
ry
l
li
d
a
e
a
re
h
o
u
se
d
in
an
e
x
p
a
n
sio
n
of
th
e
ir
fo
re
leg
s.
Th
r
o
u
g
h
t
h
is,
th
e
y
b
ias
to
th
e
p
r
o
d
u
c
e
d
flu
tt
e
ri
n
g
so
u
n
d
s
.
P
ro
p
o
se
d
G
ry
ll
id
a
e
Op
ti
m
iza
ti
o
n
Al
g
o
r
it
h
m
(G
OA
)
h
a
s
b
e
e
n
tes
ted
in
sta
n
d
a
rd
IEE
E
14,
30
b
u
s
tes
t
s
y
ste
m
s
a
n
d
si
m
u
latio
n
re
su
lt
s
sh
o
w
t
h
a
t
t
h
e
p
r
o
jec
ted
a
lg
o
rit
h
m
s
re
d
u
c
e
d
t
h
e
re
a
l
p
o
we
r
lo
ss
c
o
n
sid
e
ra
b
l
y
.
K
ey
w
o
r
d
s
:
Gr
y
llid
ae
o
p
tim
izatio
n
alg
o
r
ith
m
,
Op
tim
al
r
ea
ctiv
e
p
o
wer
,
T
r
an
s
m
is
s
io
n
lo
s
s
,
T
h
is
is
an
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r
th
e
CC
BY
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Kan
ag
asab
ai
L
en
in
,
Dep
ar
tm
en
t
of
EEE,
Pra
s
ad
V.
Po
tlu
r
i
Sid
d
h
ar
th
a
I
n
s
titu
te
of
T
ec
h
n
o
lo
g
y
,
Kan
u
r
u
,
Vijay
awa
d
a
,
An
d
h
r
a
Pra
d
esh
,
5
2
0
0
0
7
,
I
n
d
ia.
E
m
ail:
g
k
len
in
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
R
ea
ctiv
e
p
o
wer
p
r
o
b
lem
p
la
y
s
a
k
ey
r
o
le
in
s
ec
u
r
e
an
d
ec
o
n
o
m
ic
o
p
e
r
atio
n
s
of
p
o
w
er
s
y
s
tem
.
Op
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
h
as
b
ee
n
s
o
lv
e
d
by
v
ar
iety
of
ty
p
es
of
m
eth
o
d
s
[1
-
6
]
.
Nev
er
t
h
eless
n
u
m
er
o
u
s
s
cien
tific
d
if
f
icu
lties
ar
e
f
o
u
n
d
wh
ile
s
o
lv
in
g
p
r
o
b
lem
due
to
an
ass
o
r
tm
e
n
t
of
co
n
s
tr
ain
ts
.
E
v
o
lu
tio
n
a
r
y
tech
n
i
q
u
es
[7
-
1
7
]
ar
e
ap
p
lied
to
s
o
lv
e
th
e
r
e
ac
tiv
e
p
o
wer
p
r
o
b
lem
,
but
th
e
m
ain
p
r
o
b
lem
is
m
an
y
al
g
o
r
ith
m
s
g
et
s
tu
ck
in
lo
ca
l
o
p
tim
al
s
o
lu
ti
o
n
&
f
ailed
to
b
alan
ce
t
h
e
E
x
p
lo
r
at
io
n
&
E
x
p
lo
itatio
n
d
u
r
in
g
th
e
s
ea
r
ch
of
g
lo
b
al
s
o
lu
tio
n
.
T
h
is
p
ap
er
p
r
o
jects
Gr
y
llid
ae
Op
tim
izatio
n
Alg
o
r
ith
m
(
GOA)
h
as
b
ee
n
ap
p
lied
to
s
o
l
v
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
.
Pr
o
p
o
s
ed
alg
o
r
ith
m
b
ased
on
th
e
c
h
ir
p
i
n
g
ch
a
r
ac
ter
is
tics
of
Gr
y
llid
ae
an
d
f
o
r
m
u
lated
to
s
o
lv
e
th
e
o
p
tim
al
r
ea
ctiv
e
p
o
w
er
p
r
o
b
lem
.
In
c
o
m
m
o
n
,
m
ale
Gr
y
llid
ae
ch
ir
p
,
on
th
e
o
th
e
r
h
an
d
s
o
m
e
f
em
ale
Gr
y
llid
ae
also
do
as
well.
Ma
le
Gr
y
llid
ae
d
r
aw
th
e
f
em
ales
by
th
is
s
o
u
n
d
w
h
ich
th
ey
p
r
o
d
u
ce
.
Ma
le
Gr
y
llid
ae
cr
ea
te
th
is
s
o
u
n
d
by
ch
ir
p
i
n
g
t
h
e
win
g
s
an
d
k
n
o
wn
as
s
tr
id
u
l
atin
g
.
T
h
ey
attr
ac
t
each
o
th
er
by
th
is
s
o
u
n
d
f
o
r
m
atin
g
an
d
k
ee
p
th
e
o
th
e
r
s
f
r
o
m
th
eir
n
ests
.
Mo
r
eo
v
er
,
th
e
y
ca
u
tio
n
th
e
o
th
er
Gr
y
llid
ae
ag
ain
s
t
d
an
g
er
s
with
th
is
s
o
u
n
d
.
T
h
e
h
ea
r
in
g
o
r
g
a
ns
of
th
e
Gr
y
llid
ae
ar
e
h
o
u
s
ed
in
an
e
x
p
an
s
io
n
of
th
eir
f
o
r
eleg
s
.
T
h
r
o
u
g
h
t
h
is
,
th
ey
b
ias
to
th
e
p
r
o
d
u
ce
d
f
lu
t
ter
in
g
s
o
u
n
d
s
.
Pro
p
o
s
ed
Gr
y
l
lid
ae
Op
tim
izatio
n
Alg
o
r
ith
m
(
GOA)
h
as
b
ee
n
te
s
ted
in
s
tan
d
ar
d
IEEE
14,
30
bus
test
s
y
s
tem
s
an
d
s
im
u
latio
n
r
esu
lts
s
h
o
w
th
at
th
e
p
r
o
jecte
d
al
g
o
r
ith
m
s
r
e
d
u
c
ed
th
e
r
ea
l
p
o
wer
lo
s
s
co
n
s
id
e
r
ab
ly
.
2.
P
RO
B
L
E
M
F
O
R
M
U
L
AT
I
O
N
Ob
jectiv
e
of
th
e
p
r
o
b
lem
is
to
r
ed
u
ce
th
e
t
r
u
e
p
o
wer
lo
s
s
:
F
=
P
L
=
∑
g
k
k
∈
Nbr
(
V
i
2
+
V
j
2
−
2
V
i
V
j
c
os
θ
ij
)
(
1
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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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.
9
,
No
.
3,
Dec
em
b
e
r
20
20
:
17
9
–
18
4
180
v
o
ltag
e
d
e
v
iatio
n
g
iv
e
n
as
f
o
ll
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ws:
F
=
P
L
+
ω
v
×
Vol
ta
ge
De
via
tion
(
2
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v
o
ltag
e
d
e
v
iatio
n
g
iv
e
n
b
y
:
Vol
ta
ge
De
via
tion
=
∑
|
V
i
−
1
|
N
p
q
i
=
1
(
3
)
co
n
s
tr
ain
t
(
eq
u
ality
)
P
G
=
P
D
+
P
L
(
4
)
co
n
s
tr
ain
ts
(
I
n
eq
u
ality
)
P
g
s
l
ack
m
i
n
≤
P
g
s
l
ac
k
≤
P
g
s
l
ack
m
ax
(
5
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Q
gi
m
i
n
≤
Q
gi
≤
Q
gi
m
ax
,
i
∈
N
g
(
6
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V
i
m
i
n
≤
V
i
≤
V
i
m
ax
,
i
∈
N
(
7
)
T
i
m
i
n
≤
T
i
≤
T
i
m
ax
,
i
∈
N
T
(
8
)
Q
c
m
i
n
≤
Q
c
≤
Q
C
m
ax
,
i
∈
N
C
(
9
)
3.
G
RYLL
I
DA
E
O
P
T
I
M
I
Z
A
T
I
O
N
AL
G
O
RI
T
H
M
Gr
y
llid
ae
o
p
tim
izatio
n
alg
o
r
ith
m
b
ased
on
th
e
ch
ir
p
i
n
g
c
h
a
r
ac
ter
is
tics
of
Gr
y
llid
ae
a
n
d
f
o
r
m
u
lated
to
s
o
lv
e
th
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
.
In
co
m
m
o
n
,
m
ale
Gr
y
llid
ae
ch
ir
p
,
on
th
e
o
th
er
h
an
d
s
o
m
e
f
em
ale
Gr
y
llid
ae
also
do
as
w
ell.
Ma
le
Gr
y
llid
ae
d
r
aw
th
e
f
em
ales
by
th
is
s
o
u
n
d
wh
ich
th
ey
p
r
o
d
u
ce
.
Ma
le
Gr
y
llid
ae
cr
ea
te
th
is
s
o
u
n
d
by
ch
ir
p
in
g
th
e
win
g
s
an
d
k
n
o
w
n
as
s
tr
id
u
latin
g
.
T
h
ey
attr
ac
t
each
o
th
er
by
th
is
s
o
u
n
d
f
o
r
m
atin
g
an
d
k
ee
p
t
h
e
o
th
er
s
f
r
o
m
th
eir
n
ests
.
Mo
r
eo
v
er
,
th
ey
ca
u
tio
n
th
e
o
t
h
er
Gr
y
llid
ae
a
g
ain
s
t
d
an
g
er
s
with
th
is
s
o
u
n
d
.
T
h
e
h
ea
r
in
g
o
r
g
an
s
of
th
e
Gr
y
llid
ae
ar
e
h
o
u
s
ed
in
an
ex
p
a
n
s
io
n
of
th
eir
f
o
r
ele
g
s
.
T
h
r
o
u
g
h
th
is
,
th
e
y
b
ias
to
t
h
e
p
r
o
d
u
ce
d
f
lu
tter
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g
s
o
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n
d
s
.
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em
p
e
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r
e
(T
f
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in
d
eg
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s
Fah
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en
h
eit
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ca
lcu
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f
r
o
m
th
e
ch
ir
p
(N
c
(
p
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m
in
u
te
)
)
b
y
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=
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.
00
+
ℎ
(
)
−
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4
.
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tem
p
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e
in
d
eg
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c
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=
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00
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(
)
−
40
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00
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1
1
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ch
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p
in
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ate
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lcu
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ased
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y
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=
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∗
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00
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2
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=
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00
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3
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v
elo
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e
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d
with
r
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e
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ce
to
th
e
D
o
lb
ea
r
law
=
20
.
1
∗
√
273
+
(
1
4
)
f
r
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e
wh
ich
h
as
r
ec
en
tly
p
ar
ticip
ated
to
th
e
m
eta
-
h
eu
r
is
tic
alg
o
r
ith
m
a
p
p
r
o
ac
h
was
co
n
f
ir
m
e
d
.
Pr
o
p
o
s
ed
Gr
y
llid
ae
Op
tim
izatio
n
Alg
o
r
ith
m
(
GO
A)
h
as
b
ee
n
test
ed
in
s
tan
d
ar
d
IEEE
1
4
,
30
bus
test
s
y
s
tem
s
an
d
s
im
u
latio
n
r
esu
lts
s
h
o
w
th
at
th
e
p
r
o
jecte
d
alg
o
r
ith
m
s
r
ed
u
ce
d
th
e
r
ea
l
p
o
wer
lo
s
s
co
n
s
id
er
ab
ly
.
RE
F
E
R
E
NC
E
S
[
1
]
K.
Y.
L
ee
,
“Fu
el
-
co
s
t
m
in
i
m
is
atio
n
f
o
r
b
o
t
h
r
ea
l
an
d
r
ea
ctiv
e
-
p
o
wer
d
is
p
atch
es,”
P
r
o
ce
ed
in
g
s
Gen
era
tio
n
,
Tr
a
n
s
mis
s
io
n
and
Dis
tr
ib
u
tio
n
C
o
n
feren
ce
,
v
o
l.
131
,
no.
3
,
p
p
.
85
-
9
3
,
1
9
8
4
.
[
2
]
N.
I.
Dee
b
,
“An
ef
f
icien
t
tec
h
n
iq
u
e
f
o
r
r
ea
ctiv
e
p
o
wer
d
i
s
p
atch
u
s
in
g
a
r
e
v
is
ed
lin
ea
r
p
r
o
g
r
am
m
i
n
g
ap
p
r
o
ac
h
,
”
E
lectric
P
o
w
er
S
ystem
R
esea
r
ch
,
v
o
l.
1
5
,
n
o
.
2,
p
p
.
1
2
1
–
1
3
4
,
1
9
9
8
.
[
3
]
M.
R.
B
jelo
g
r
li
c,
M.
S.
C
alo
v
ic,
B.
S.
B
ab
ic
,
“Ap
p
licat
io
n
of
New
to
n
’
s
o
p
tim
al
p
o
wer
f
lo
w
in
v
o
ltag
e/r
ea
ctiv
e
p
o
wer
co
n
tr
o
l
,
”
IEEE
T
r
a
n
s
Po
wer
Sy
s
te
m,
v
o
l.
5,
no.
4,
p
p
.
1
4
4
7
-
1
4
5
4
,
1
9
9
0
.
[
4
]
S.
Gr
an
v
ille,
“Op
tim
al
r
ea
ctiv
e
d
is
p
atch
th
r
o
u
g
h
in
ter
io
r
p
o
in
t
m
eth
o
d
s
,
”
I
E
E
E
Tr
a
n
s
a
ctio
n
s
on
P
o
w
er
S
ystem
,
v
o
l.
9
,
no.
1,
p
p
.
1
3
6
–
146
,
1
9
9
4
.
[
5
]
N.
Gr
u
d
in
in
,
“Reactiv
e
p
o
wer
o
p
tim
izatio
n
u
s
in
g
s
u
cc
ess
iv
e
q
u
ad
r
atic
p
r
o
g
r
am
m
in
g
m
eth
o
d
,
”
I
E
E
E
Tr
a
n
s
a
ctio
n
s
on
P
o
w
er
S
ystem,
v
o
l.
13
,
n
o
.
4,
pp.
1
2
1
9
–
1
2
2
5
,
1
9
9
8
.
[
6
]
Ng
Sh
in
Me
i,
R
.
;
Su
laim
an
,
M
.
H.
;
Mu
s
taf
f
a,
Z.;
Dan
iy
al,
H.
“Op
tim
al
r
ea
ctiv
e
p
o
wer
d
i
s
p
atch
s
o
lu
tio
n
by
lo
s
s
m
in
i
m
izatio
n
u
s
in
g
m
o
th
-
f
lam
e
o
p
tim
izatio
n
t
ec
h
n
iq
u
e
,
”
A
p
p
l.
S
o
ft
C
o
m
p
u
t
.
,
v
o
l.
59,
pp.
2
1
0
–
222,
2
0
1
7
.
[
7
]
C
h
en
,
G.
,
L
iu
,
L.
,
Z
h
an
g
,
Z.
,
Hu
an
g
,
S.
“Op
tim
al
r
ea
ctiv
e
p
o
wer
d
is
p
atch
by
im
p
r
o
v
e
d
GSA
-
b
ased
alg
o
r
ith
m
with
th
e
n
o
v
el
s
tr
ate
g
ies
to
h
an
d
le
c
o
n
s
tr
ain
ts
,
”
A
p
p
l.
S
o
ft
C
o
mp
u
t
.,
v
o
l.
50,
pp.
58
–
70
,
2
0
1
7
.
[
8
]
Nad
er
i,
E.
,
Nar
im
an
i,
H.
,
Fath
i,
M.
,
Nar
im
a
n
i,
M.
R
,
“A
n
o
v
el
f
u
zz
y
ad
ap
tiv
e
co
n
f
ig
u
r
ati
o
n
of
p
ar
ticle
s
war
m
o
p
tim
izatio
n
to
s
o
lv
e
lar
g
e
-
s
ca
le
o
p
tim
al
r
ea
ctiv
e
p
o
wer
d
is
p
atch
,
”
A
p
p
l.
S
o
ft
C
o
mp
u
t
.,
v
o
l.
5
3
,
pp.
4
4
1
–
456
,
2
0
1
7
.
[
9
]
Heid
ar
i,
A.
A
,
Ali
Ab
b
asp
o
u
r
,
R
.
R
ez
ae
e
J
o
r
d
eh
i,
A.
“Ga
u
s
s
ian
b
ar
e
-
b
o
n
es
wate
r
cy
cle
alg
o
r
ith
m
f
o
r
o
p
tim
al
r
ea
ctiv
e
p
o
wer
d
is
p
atch
in
elec
tr
ical
po
wer
s
y
s
tem
s
,
”
A
p
p
l.
S
o
ft
C
o
mp
u
t
,
v
o
l.
57,
pp.
6
5
7
–
671
,
2
0
1
7
.
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.
9
,
No
.
3,
Dec
em
b
e
r
20
20
:
17
9
–
18
4
184
[
1
0
]
Ma
h
aletc
h
u
m
i
Mo
r
g
an
,
No
r
R
u
l
Hasm
a
Ab
d
u
llah
,
Mo
h
d
Her
wan
Su
laim
an
,
Mah
f
u
za
h
Mu
s
taf
a
,
R
o
s
d
iy
an
a
Sam
ad
,
“Ben
ch
m
a
r
k
Stu
d
ies
on
Op
tim
al
R
ea
ctiv
e
Po
wer
Dis
p
atch
(
OR
PD)
B
ased
Mu
lti
-
o
b
jectiv
e
E
v
o
lu
tio
n
ar
y
Pro
g
r
am
m
in
g
(
MO
E
P)
Usi
n
g
Mu
t
atio
n
B
ased
on
Ad
a
p
tiv
e
Mu
tatio
n
Ad
ap
ter
(
AM
O)
an
d
Po
ly
n
o
m
ial
M
u
tatio
n
Op
er
at
o
r
(
PMO)
,
”
J
o
u
r
n
a
l
of
E
lectrica
l
S
ystems
,
v
o
l.
1
2
,
no.
1,
pp.
1
2
1
-
132
,
2
0
1
6
[
1
1
]
R
eb
ec
ca
Ng
Sh
in
Me
i,
Mo
h
d
Her
wan
Su
laim
an
,
Z
u
r
ian
i
Mu
s
taf
f
a,
“An
t
L
io
n
Op
tim
iz
er
f
o
r
Op
tim
al
R
ea
c
tiv
e
Po
wer
Dis
p
atch
S
o
lu
tio
n
,
”
Jo
u
r
n
a
l
of
E
lectrica
l
S
ystems
,
Sp
e
cial
I
s
s
u
e
AM
PE2
0
1
5
,
pp.
68
-
7
4
,
2
0
1
6
.
[
1
2
]
P.
An
b
ar
asan
,
T.
J
ay
ab
ar
ath
i
,
“Op
tim
al
r
ea
ctiv
e
p
o
wer
d
is
p
atch
p
r
o
b
lem
s
o
lv
ed
by
s
y
m
b
i
o
tic
o
r
g
an
is
m
s
ea
r
ch
alg
o
r
ith
m
,
”
I
n
n
o
va
tio
n
s
in
P
o
w
er
and
A
d
v
a
n
ce
d
C
o
mp
u
tin
g
Tech
n
o
lo
g
ies
,
2
0
1
7
.
[
1
3
]
Gag
lian
o
A.
,
No
ce
r
a
F,
“
An
aly
s
is
of
th
e
p
er
f
o
r
m
a
n
ce
s
of
elec
tr
ic
en
er
g
y
s
to
r
a
g
e
in
r
esid
en
tial
ap
p
licatio
n
s
,
”
I
n
tern
a
tio
n
a
l
Jo
u
r
n
a
l
of
Hea
t
a
n
d
Tech
n
o
lo
g
y
,
v
o
l.
3
5,
n
o
.
1,
p
p
.
S4
1
-
S4
8
,
2
0
1
7
.
[
1
4
]
C
ald
er
a
M.,
Un
g
ar
o
P.,
C
a
m
m
ar
ata
G.
,
Pu
g
lis
i
G,
“
Su
r
v
ey
-
b
ased
an
aly
s
is
of
th
e
elec
tr
ical
en
er
g
y
d
em
an
d
in
I
talian
h
o
u
s
eh
o
ld
s
,
”
Ma
th
ema
tica
l
Mo
d
ellin
g
of
E
n
g
in
ee
r
in
g
P
r
o
b
lems
,
Vo
l.
5,
No
.
3,
p
p
.
217
-
224
,
2
0
1
8
.
[
1
5
]
M.
B
asu
,
“Qu
asi
-
o
p
p
o
s
itio
n
a
l
d
if
f
er
en
tial
ev
o
lu
tio
n
f
o
r
o
p
tim
al
r
ea
ctiv
e
p
o
we
r
d
is
p
at
ch
,
”
E
lectrica
l
P
o
w
er
and
E
n
erg
y
S
ystems
,
v
o
l.
78,
pp.
29
-
40,
2
0
1
6
.
[
1
6
]
J
o
n
ti
Deu
r
i
,
S.
Siv
a
Sath
y
a,
“A
No
v
el
C
r
ick
et
C
h
ir
p
in
g
Alg
o
r
ith
m
f
o
r
E
n
g
in
ee
r
in
g
Op
tim
izatio
n
Pro
b
lem
,
”
A
d
v
a
n
ce
s
in
N
a
tu
r
a
l
and
A
p
p
lied
S
cie
n
ce
s
,
v
o
l.
9,
no.
6,
p
p
,
3
9
7
-
4
0
2
,
Sp
ec
ial
2
0
1
5
.
[
1
7
]
A.
Kav
eh
,
N.
Far
h
o
u
d
i,
“
A
n
e
w
o
p
tim
izatio
n
m
eth
o
d
:
Do
lp
h
in
ec
h
o
lo
ca
tio
n
,
”
A
d
va
n
ces
in
E
n
g
in
ee
r
in
g
S
o
ftw
a
r
e
,
v
o
l.
5
9
,
p
p
.
53
–
7
0
,
2013
.
[
1
8
]
IEEE,
“T
h
e
IEEE
-
test
s
y
s
tem
s
”
,
h
ttp
://www.
ee
.
wash
in
g
to
n
.
e
d
u
/trsear
ch
/p
s
tca/
,
1
9
9
3
.
[
1
9
]
Ali
Nas
s
er
Hu
s
s
ain
,
Ali
Ab
d
u
lab
b
as
Ab
d
u
llah
an
d
Om
ar
M
u
h
am
m
ed
Ne
d
a,
“M
o
d
if
ie
d
Par
ticle
Swar
m
Op
tim
izatio
n
f
o
r
So
lu
tio
n
of
R
ea
ctiv
e
Po
wer
Dis
p
atch
,
”
R
esea
r
ch
Jo
u
r
n
a
l
of
A
p
p
l
ied
S
cien
ce
s
,
E
n
g
in
ee
r
in
g
and
Tech
n
o
lo
g
y
,
v
o
l.
1
5
,
n
o
.
8,
pp.
316
-
3
2
7
,
2
0
1
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.