I
nte
rna
t
io
na
l J
o
urna
l o
f
Adv
a
nces in Applie
d Science
s
(
I
J
AAS)
Vo
l.
10
,
No
.
2
,
J
u
n
e
2
0
2
1
,
p
p
.
99
~
1
0
6
I
SS
N:
2252
-
8
8
1
4
,
DOI
: 1
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1
1
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1
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1
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i
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p
p
99
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106
99
J
o
ur
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:
h
ttp
:
//ij
a
a
s
.
ia
esco
r
e.
co
m
Factua
l
po
wer
los
s
reduc
tion
by
dy
na
mic membra
ne
ev
o
lutiona
ry
alg
o
rithm
L
enin
K
a
na
g
a
ba
s
a
i
De
p
a
rtme
n
t
o
f
EE
E
,
P
ra
sa
d
V.
P
o
tl
u
ri
S
id
d
h
a
rt
h
a
In
stit
u
te o
f
Tec
h
n
o
lo
g
y
,
Ka
n
u
r
u
,
Vi
ja
y
a
wa
d
a
,
An
d
h
ra
P
ra
d
e
sh
,
In
d
ia
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Ap
r
4
,
2
0
2
0
R
ev
is
ed
Dec
1
,
2
0
2
0
Acc
ep
ted
Feb
2
2
,
2
0
2
1
Th
is
p
a
p
e
r
p
re
se
n
ts
a
d
y
n
a
m
ic
m
e
m
b
ra
n
e
e
v
o
lu
t
io
n
a
ry
a
l
g
o
rit
h
m
(DME
A)
th
a
t
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
s.
Th
e
p
ro
p
o
se
d
m
e
th
o
d
o
lo
g
y
m
e
rg
e
s
th
e
fu
sio
n
a
n
d
d
iv
isi
o
n
ru
les
o
f
P
sy
ste
m
s
with
a
c
ti
v
e
m
e
m
b
ra
n
e
s
a
n
d
wi
th
a
d
a
p
ti
v
e
d
iffere
n
ti
a
l
e
v
o
l
u
ti
o
n
(AD
E),
p
a
rti
c
le
sw
a
rm
o
p
ti
m
iza
ti
o
n
(P
S
O)
e
x
p
lo
ra
ti
o
n
stra
ta
g
e
m
.
All
e
lem
e
n
tary
m
e
m
b
ra
n
e
s
a
re
a
m
a
lg
a
m
a
ted
in
to
o
n
e
m
e
m
b
ra
n
e
in
t
h
e
c
o
m
p
u
ti
n
g
p
ro
c
e
d
u
re
.
F
u
r
th
e
rm
o
re
,
t
h
e
in
teg
ra
ted
m
e
m
b
ra
n
e
is
a
li
e
n
a
ted
in
t
o
th
e
e
lem
e
n
tary
m
e
m
b
ra
n
e
s
1
,
2
,
_
,
m
.
In
p
a
rti
c
le
sw
a
rm
o
p
ti
m
iza
ti
o
n
(P
S
O)
C
1
a
n
d
C
2
(a
c
c
e
lera
ti
o
n
c
o
n
sta
n
ts
)
a
re
v
it
a
l
p
a
ra
m
e
ters
to
a
u
g
m
e
n
t
th
e
e
x
p
lo
ra
ti
o
n
a
b
il
it
y
o
f
P
S
O
in
th
e
p
e
rio
d
o
f
t
h
e
o
p
ti
m
iza
ti
o
n
p
ro
c
e
d
u
re
.
In
t
h
is
wo
rk
,
G
a
u
ss
ian
p
ro
b
a
b
il
it
y
d
i
strib
u
ti
o
n
is
in
it
iate
d
to
e
n
g
e
n
d
e
r
th
e
a
c
c
e
ler
a
ti
n
g
c
o
e
fficie
n
ts
o
f
P
S
O
.
T
h
e
p
r
o
p
o
se
d
DME
A
h
a
s
b
e
e
n
tes
ted
in
sta
n
d
a
rd
IEE
E
1
4
,
3
0
,
5
7
,
1
1
8
,
a
n
d
3
0
0
b
u
s
tes
t
sy
ste
m
s
a
n
d
sim
u
latio
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re
su
lt
s
sh
o
w
t
h
e
p
ro
jec
ted
a
lg
o
rit
h
m
re
d
u
c
e
d
th
e
re
a
l
p
o
we
r
lo
ss
c
o
m
p
re
h
e
n
siv
e
l
y
.
K
ey
w
o
r
d
s
:
Dy
n
am
ic
m
em
b
r
an
e
e
v
o
lu
tio
n
ar
y
a
lg
o
r
ith
m
O
p
tim
al
r
ea
ctiv
e
p
o
wer
Tr
an
s
m
is
s
io
n
lo
s
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
:
L
en
in
Kan
ag
asab
ai
Dep
ar
tm
en
t o
f
E
E
E
Pra
s
ad
V.
Po
tlu
r
i
Sid
d
h
ar
th
a
I
n
s
titu
te
o
f
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
ct
iv
e
p
o
we
r
p
r
o
b
le
m
p
la
y
s
a
k
e
y
r
o
le
i
n
s
ec
u
r
e
a
n
d
ec
o
n
o
m
i
c
o
p
e
r
at
i
o
n
s
o
f
p
o
wer
s
y
s
te
m
.
Op
t
im
al
r
e
ac
t
iv
e
p
o
we
r
p
r
o
b
lem
h
as
b
e
e
n
s
o
l
v
e
d
b
y
v
a
r
i
ety
o
f
t
y
p
es
o
f
m
et
h
o
d
s
[
1
]
-
[
6
]
.
N
ev
e
r
t
h
el
ess
,
n
u
m
er
o
u
s
s
ci
e
n
ti
f
ic
d
if
f
i
cu
lti
es
a
r
e
f
o
u
n
d
w
h
il
e
s
o
l
v
i
n
g
p
r
o
b
l
em
d
u
e
to
a
n
ass
o
r
t
m
e
n
t
o
f
c
o
n
s
tr
ai
n
ts
.
E
v
o
l
u
t
io
n
a
r
y
t
ec
h
n
i
q
u
es
[
7
]
-
[
1
6
]
a
r
e
ap
p
l
ie
d
t
o
s
o
l
v
e
t
h
e
r
ea
c
ti
v
e
p
o
we
r
p
r
o
b
le
m
,
b
u
t
t
h
e
m
a
in
p
r
o
b
le
m
is
m
a
n
y
al
g
o
r
it
h
m
s
g
et
s
tu
c
k
i
n
l
o
c
al
o
p
ti
m
al
s
o
lu
ti
o
n
&
f
ail
ed
to
b
ala
n
ce
th
e
e
x
p
l
o
r
at
io
n
&
ex
p
l
o
it
ati
o
n
d
u
r
in
g
th
e
s
ea
r
c
h
o
f
g
lo
b
al
s
o
l
u
t
i
o
n
.
I
n
th
is
p
ap
er
,
d
y
n
am
ic
m
em
b
r
an
e
e
v
o
lu
tio
n
a
r
y
alg
o
r
ith
m
(
DM
E
A)
h
as
b
ee
n
ap
p
lied
t
o
s
o
lv
e
o
p
tim
al
r
ea
cti
v
e
p
o
wer
p
r
o
b
lem
.
Pr
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
m
er
g
es
th
e
f
u
s
io
n
an
d
d
iv
is
io
n
r
u
les
o
f
P
s
y
s
tem
s
with
ac
tiv
e
m
em
b
r
an
es
an
d
with
ad
ap
tiv
e
d
if
f
er
en
tial
e
v
o
lu
tio
n
(
ADE
)
,
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
ex
p
lo
r
ati
o
n
s
tr
atag
em
.
I
n
t
h
is
wo
r
k
,
c
o
m
p
o
s
itio
n
o
f
th
e
d
y
n
am
ic
m
em
b
r
an
e
alg
o
r
ith
m
alo
n
g
with
th
e
f
u
s
io
n
,
d
iv
is
i
o
n
r
u
les
ar
e
u
tili
ze
d
to
s
o
lv
e
th
e
o
p
tim
al
r
ea
ctiv
e
p
o
we
r
p
r
o
b
lem
.
I
n
s
k
in
m
em
b
r
an
e
0
,
e
lem
en
tar
y
m
e
m
b
r
an
es
1
,
2
,
_
,
m
ar
e
em
b
ed
d
ed
in
th
e
s
tr
u
ct
u
r
e,
an
d
it
co
n
tain
s
s
et
o
f
ev
o
lu
ti
o
n
ar
y
,
c
o
m
m
u
n
icatio
n
r
u
les
,
m
u
lti
-
s
et
o
f
o
b
jects.
All
elem
en
tar
y
m
em
b
r
an
es
a
r
e
am
alg
am
ated
in
to
o
n
e
m
em
b
r
an
e
i
n
th
e
co
m
p
u
ti
n
g
p
r
o
ce
d
u
r
e.
Fu
r
th
er
m
o
r
e
,
in
teg
r
ated
m
e
m
b
r
an
e
is
alien
ated
in
to
th
e
elem
en
tar
y
m
em
b
r
an
es
1
,
2
,
_
,
m
.
I
n
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PSO
)
,
1
,
2
(
ac
ce
ler
atio
n
co
n
s
tan
ts
)
ar
e
v
ital
p
ar
am
eter
s
to
au
g
m
e
n
t
t
h
e
ex
p
lo
r
ati
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
: 2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
10
,
No
.
2
,
J
u
n
e
2
0
2
1
:
99
–
106
100
ab
ilit
y
o
f
PS
O
in
th
e
p
er
i
o
d
o
f
th
e
o
p
tim
izatio
n
p
r
o
ce
d
u
r
e.
C
o
n
v
er
s
ely
,
d
is
s
im
ilar
o
p
ti
m
izatio
n
p
r
o
b
lem
s
h
av
e
alter
ed
v
alu
es f
o
r
t
h
e
ac
c
eler
atio
n
co
n
s
tan
ts
,
it
will
n
o
t
b
e
an
ef
f
o
r
tles
s
ass
ig
n
m
en
t to
ch
o
o
s
e
th
e
o
p
tim
al
v
alu
es.
I
n
th
is
wo
r
k
,
Gau
s
s
ian
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
is
in
it
iate
d
to
e
n
g
en
d
er
th
e
ac
ce
ler
a
tin
g
co
ef
f
icien
ts
o
f
PS
O.
Par
ticle
s
war
m
o
p
tim
iz
atio
n
(
PS
O)
b
ased
o
n
Gau
s
s
ian
d
is
tr
ib
u
tio
n
will
b
e
em
p
l
o
y
ed
c
o
n
cu
r
r
en
tly
in
ar
ea
f
r
o
m
1
to
m
.
T
h
e
p
r
o
p
o
s
e
d
d
y
n
am
ic
m
em
b
r
a
n
e
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
(
DM
E
A)
h
as
b
ee
n
tes
te
d
i
n
s
tan
d
ar
d
I
E
E
E
1
4
,
3
0
,
5
7
,
1
1
8
,
a
n
d
3
0
0
b
u
s
t
est
s
y
s
t
em
a
n
d
s
i
m
u
l
ati
o
n
r
es
u
lts
s
h
o
w
t
h
e
p
r
o
j
ec
te
d
al
g
o
r
i
th
m
r
e
d
u
ce
d
t
h
e
r
ea
l
p
o
we
r
l
o
s
s
ex
t
en
s
i
v
el
y
.
2.
P
RO
B
L
E
M
F
O
R
M
U
L
AT
I
O
N
Ob
j
ec
t
iv
e
o
f
th
e
p
r
o
b
le
m
i
s
t
o
r
e
d
u
ce
t
h
e
t
r
u
e
p
o
w
e
r
l
o
s
s
as
(
1
)
.
F
=
P
L
=
∑
g
k
k
∈
Nbr
(
V
i
2
+
V
j
2
−
2
V
i
V
j
c
os
θ
ij
)
(
1
)
Vo
l
ta
g
e
d
e
v
ia
ti
o
n
g
i
v
e
n
as
(
2
)
.
F
=
P
L
+
ω
v
×
Vol
ta
ge
De
via
tio
(
2
)
Vo
l
ta
g
e
d
e
v
ia
ti
o
n
g
i
v
e
n
b
y
(
3
)
.
Vol
ta
ge
De
via
tion
=
∑
|
V
i
−
1
|
N
p
q
i
=
1
(
3
)
C
o
n
s
tr
ai
n
t
(
E
q
u
ality
)
.
P
G
=
P
D
+
P
L
(
4
)
C
o
n
s
tr
ai
n
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
)
Q
gi
m
i
n
≤
Q
gi
≤
Q
gi
m
ax
,
i
∈
N
g
(
6
)
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.
DYNA
M
I
C
M
E
M
B
RANE
E
VO
L
UT
I
O
NARY
A
L
G
O
RI
T
H
M
I
n
m
em
b
r
an
e
co
m
p
u
tin
g
,
P
s
y
s
tem
s
with
d
y
n
am
ic
m
em
b
r
an
es
ar
e
a
v
er
y
b
lis
ter
in
g
r
es
ea
r
ch
to
p
ic
an
d
th
e
an
alo
g
o
u
s
m
em
b
r
a
n
e
alg
o
r
ith
m
s
h
av
e
b
ee
n
u
s
ed
ex
ten
s
iv
ely
to
s
o
lv
e
v
ar
io
u
s
ty
p
es
o
f
o
p
tim
izatio
n
p
r
o
b
lem
s
[
1
7
]
.
I
n
th
is
wo
r
k
,
c
o
m
p
o
s
itio
n
o
f
t
h
e
d
y
n
a
m
ic
m
e
m
b
r
an
e
alg
o
r
ith
m
alo
n
g
with
th
e
f
u
s
io
n
,
d
iv
is
io
n
r
u
les ar
e
u
tili
ze
d
to
s
o
lv
e
th
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
le
m
.
I
n
s
k
in
m
em
b
r
an
e
0
,
elem
e
n
tar
y
m
em
b
r
a
n
es 1
,
2
,
_
,
m
ar
e
em
b
ed
d
e
d
in
th
e
s
tr
u
ctu
r
e,
a
n
d
it
co
n
tain
s
s
et
o
f
ev
o
lu
tio
n
a
r
y
,
c
o
m
m
u
n
icatio
n
r
u
les,
m
u
lti
-
s
et
o
f
o
b
jects
.
All
elem
en
tar
y
m
em
b
r
an
es
ar
e
am
alg
am
ated
in
to
o
n
e
m
em
b
r
a
n
e
in
th
e
co
m
p
u
tin
g
p
r
o
ce
d
u
r
e.
Fu
r
th
er
m
o
r
e
,
in
teg
r
ated
m
e
m
b
r
an
e
is
alien
ated
in
to
th
e
elem
en
tar
y
m
em
b
r
an
es
1
,
2
,
_
,
m
.
Pro
p
o
s
ed
m
eth
o
d
o
l
o
g
y
m
er
g
es
t
h
e
f
u
s
io
n
an
d
d
iv
is
io
n
r
u
les
o
f
P
s
y
s
tem
s
with
ac
tiv
e
m
em
b
r
a
n
es
an
d
with
ad
ap
tiv
e
d
if
f
er
en
tial e
v
o
lu
tio
n
(
ADE
)
,
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
ex
p
lo
r
atio
n
s
tr
atag
em
.
a.
On
e
lev
el
m
em
b
r
a
n
e
s
tr
u
ctu
r
e
h
as b
ee
n
s
p
ec
if
ied
b.
I
n
p
ar
tic
le
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
1
,
2
(
ac
ce
ler
atio
n
co
n
s
tan
ts
)
ar
e
v
ital p
ar
am
ete
r
s
to
a
u
g
m
en
t th
e
ex
p
lo
r
atio
n
ab
ilit
y
o
f
PS
O
in
th
e
p
er
io
d
o
f
th
e
o
p
tim
izatio
n
p
r
o
ce
d
u
r
e.
C
o
n
v
er
s
ely
,
d
is
s
im
ilar
o
p
tim
izatio
n
p
r
o
b
lem
s
h
a
v
e
al
ter
ed
v
alu
es f
o
r
th
e
ac
ce
ler
atio
n
co
n
s
tan
ts
,
it will n
o
t b
e
a
n
ef
f
o
r
tles
s
ass
ig
n
m
en
t to
ch
o
o
s
e
th
e
o
p
ti
m
al
v
alu
es.
I
n
t
h
is
wo
r
k
,
Gau
s
s
ian
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
is
in
itiated
to
en
g
en
d
e
r
th
e
ac
ce
ler
atin
g
co
ef
f
icien
ts
o
f
PS
O.
Par
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
b
ased
o
n
Gau
s
s
ian
d
is
tr
ib
u
tio
n
will b
e
em
p
l
o
y
ed
co
n
cu
r
r
en
tly
in
a
r
ea
f
r
o
m
1
t
o
m
.
−
Star
t
−
Po
s
itio
n
an
d
v
elo
city
a
r
e
in
itia
lized
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2
2
5
2
-
8
8
1
4
F
a
ctu
a
l p
o
w
er lo
s
s
r
ed
u
ctio
n
b
y
d
yn
a
mic
mem
b
r
a
n
e
ev
o
lu
ti
o
n
a
r
y
a
lg
o
r
ith
m
(
Len
in
K
a
n
a
g
a
b
a
s
a
i
)
101
−
C
o
m
p
u
te
th
e
f
itn
ess
v
alu
e
−
Pb
est an
d
Gb
est ar
e
u
p
d
ated
−
I
f
s
to
p
cr
iter
io
n
s
atis
f
ied
,
th
en
en
d
o
r
else u
p
d
ate
Po
s
itio
n
an
d
v
elo
city
g
o
to
s
tep
iii
−
E
n
d
,
+
1
=
|
(
)
|
×
(
,
−
,
)
+
|
(
)
|
×
(
,
−
,
)
(
1
0
)
,
+
1
=
,
+
,
+
1
(
1
1
)
c.
E
x
ec
u
te
th
e
in
te
g
r
atio
n
p
r
o
ce
s
s
,
all
elem
en
tar
y
m
em
b
r
an
es a
r
e
am
alg
am
ated
i
n
to
o
n
e
elem
en
tar
y
m
em
b
r
an
e
o
n
e
an
d
all
elem
e
n
tar
y
m
em
b
r
an
es st
r
in
g
s
ar
e
g
o
n
e
in
to
th
e
m
em
b
r
a
n
e
.
d.
I
n
m
o
n
e
m
em
b
r
an
e,
a
d
ap
tiv
e
d
if
f
er
e
n
tial e
v
o
lu
tio
n
is
u
tili
ze
d
to
m
o
d
er
n
ize
th
e
s
tr
in
g
s
o
b
ject.
I
n
th
is
wo
r
k
s
elf
-
ad
ap
tiv
e
m
eth
o
d
is
u
s
ed
t
o
co
n
tr
o
l th
e
p
a
r
am
eter
s
C
R
an
d
F.
(
)
=
1
(
)
+
(
0
,
1
)
×
(
2
(
)
−
3
(
)
)
(
1
2
)
(
)
=
4
(
)
+
(
0
,
0
.
5
)
×
(
5
(
)
−
6
(
)
)
(
1
3
)
−
E
n
g
en
d
e
r
th
e
p
r
elim
in
ar
y
p
o
p
u
latio
n
−
Fo
r
ea
ch
in
d
i
v
id
u
a
l in
t
h
e
p
o
p
u
latio
n
,
en
g
en
d
er
th
r
ee
ar
b
itra
r
y
d
if
f
e
r
en
t in
teg
e
r
s
r
1
,
r
2
a
n
d
r
3
∈
{
1
,
2
,
.
.
,
N
}
an
d
en
g
en
d
e
r
an
a
r
b
itra
r
y
in
te
g
er
J
r
an
d
o
m
∈
{
1
,
2
,
.
,
n
}
−
If
r
a
n
dom
J
(
0
,
1
)
<
CR
th
en
x
i
,
j
′
=
x
i
,
r
3
+
F
∗
(
x
i
,
r
1
−
x
i
,
r
2
)
−
E
ls
e
x
i
,
j
′
=
x
i
,
j
−
E
n
d
if
−
E
n
d
f
o
r
−
If
F
ite
n
s
s
(
x
i
′
)
≤
F
itn
e
s
s
(
x
i
)
; th
en
x
i
=
x
i
′
−
E
n
d
if
−
E
n
d
f
o
r
−
E
n
d
co
n
d
itio
n
W
h
en
,
′
in
f
r
in
g
e
th
e
b
o
u
n
d
ar
y
c
o
n
s
tr
ain
t,
an
d
th
en
th
e
v
io
late
d
v
ar
iab
le
v
alu
e
is
b
r
o
u
g
h
t
b
a
ck
b
y
,
,
′
=
{
,
(
(
)
≤
0
.
5
)
˅
(
,
′
<
,
)
,
(
(
)
≤
0
.
5
)
˅
(
,
′
<
,
)
2
×
,
−
,
′
(
(
)
>
0
.
5
)
˅
(
,
′
<
,
)
2
×
,
−
,
′
(
(
)
>
0
.
5
)
˅
(
,
′
<
,
)
(
1
4
)
e.
B
y
u
s
in
g
f
itn
ess
f
u
n
ctio
n
c
o
m
p
u
te
th
e
f
itn
ess
o
f
ea
c
h
s
tr
in
g
f.
E
m
p
lo
y
th
e
co
n
tact
r
u
les,
a
r
e
p
lica
o
f
th
e
m
o
s
t e
x
ce
llen
t strin
g
s
is
ch
o
s
en
in
th
e
m
em
b
r
a
n
e
m
o
n
e
wh
ich
will b
e
s
en
t to
th
e
s
k
in
m
em
b
r
an
e,
an
d
th
e
p
r
esen
t
m
o
s
t e
x
ce
llen
t strin
g
s
ar
e
ac
cu
m
u
lated
i
n
th
e
s
k
in
m
em
b
r
an
e
.
g.
On
ce
th
e
en
d
co
n
d
itio
n
is
m
et,
s
u
b
s
eq
u
en
tly
o
u
tp
u
t t
h
e
r
esu
lt
s
; o
th
er
wis
e
g
o
to
Step
h
.
h.
W
ith
th
e
m
elem
en
tar
y
m
e
m
b
r
an
es
m
o
n
e
Me
m
b
r
an
e
is
alien
ated
in
t
o
th
e
id
en
tical
s
tr
u
ctu
r
e.
At
p
r
esen
t
m
o
s
t e
x
ce
llen
t strin
g
s
an
d
−
1
s
tr
in
g
s
with
th
e
p
o
o
r
f
itn
ess
will b
e
s
en
d
to
e
v
er
y
elem
e
n
tar
y
m
em
b
r
an
e
in
r
o
ll b
y
th
e
s
en
d
-
i
n
co
n
tact
r
u
les,
an
d
th
en
g
o
b
ac
k
to
Step
b
.
i.
E
n
d
co
n
d
itio
n
is
th
e
u
tm
o
s
t n
u
m
b
er
o
f
iter
atio
n
s
.
Alg
o
r
ith
m
will e
n
d
if
th
e
u
tm
o
s
t n
u
m
b
e
r
o
f
iter
atio
n
s
is
r
ea
ch
ed
an
d
o
u
tp
u
t th
e
r
esu
lts
.
4.
SI
M
UL
A
T
I
O
N
R
E
S
UL
T
S
At
f
ir
s
t
in
s
tan
d
ar
d
I
E
E
E
1
4
b
u
s
s
y
s
tem
th
e
v
alid
ity
o
f
th
e
p
r
o
p
o
s
ed
d
y
n
am
ic
m
em
b
r
an
e
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
(
DM
E
A)
h
as
b
ee
n
test
ed
.
T
ab
le
1
s
h
o
ws
th
e
co
n
s
tr
ain
ts
o
f
co
n
t
r
o
l
v
ar
iab
les
.
T
ab
le
2
s
h
o
ws th
e
lim
i
ts
o
f
r
ea
ctiv
e
p
o
wer
g
en
er
ato
r
s
an
d
c
o
m
p
ar
is
o
n
r
esu
lts
ar
e
p
r
esen
ted
i
n
T
ab
l
e
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
: 2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
10
,
No
.
2
,
J
u
n
e
2
0
2
1
:
99
–
106
102
T
ab
le
1
.
C
o
n
s
tr
ain
ts
o
f
c
o
n
tr
o
l
v
ar
iab
les
S
y
st
e
m
V
a
r
i
a
b
l
e
s
M
i
n
i
m
u
m
(
P
U
)
M
a
x
i
m
u
m
(
P
U
)
I
EEE
1
4
B
u
s
G
e
n
e
r
a
t
o
r
V
o
l
t
a
g
e
0
.
9
5
1
.
1
Tr
a
n
sf
o
r
mer T
a
p
o
.
9
1
.
1
V
A
R
S
o
u
r
c
e
0
0
.
2
0
T
ab
le
2
.
C
o
n
s
tr
ain
s
o
f
r
ea
ctiv
e
p
o
wer
g
e
n
er
ato
r
s
S
y
st
e
m
V
a
r
i
a
b
l
e
s
Q
M
i
n
i
mu
m
(
P
U
)
Q
M
a
x
i
mu
m
(
P
U
)
I
EEE
1
4
B
u
s
1
0
10
2
-
40
50
3
0
40
6
-
6
24
8
-
6
24
T
ab
le
3
.
Simu
latio
n
r
esu
lts
o
f
I
E
E
E
−1
4
s
y
s
tem
C
o
n
t
r
o
l
v
a
r
i
a
b
l
e
s
B
a
se
c
a
se
M
P
S
O
[
1
8
]
P
S
O
[
1
8
]
EP [
1
8
]
S
A
R
G
A
[
1
8
]
D
M
EA
−1
1
.
0
6
0
1
.
1
0
0
1
.
1
0
0
N
R
*
N
R
*
1
.
0
2
0
−2
1
.
0
4
5
1
.
0
8
5
1
.
0
8
6
1
.
0
2
9
1
.
0
6
0
1
.
0
4
1
−
3
1
.
0
1
0
1
.
0
5
5
1
.
0
5
6
1
.
0
1
6
1
.
0
3
6
1
.
0
5
2
−
6
1
.
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I
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2
2
5
2
-
8
8
1
4
F
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l p
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105
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1
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.
Simu
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ab
le
1
2
.
C
o
m
p
ar
is
o
n
o
f
r
ea
l
p
o
wer
lo
s
s
P
a
r
a
me
t
e
r
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e
t
h
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d
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A
[
2
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]
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e
t
h
o
d
EEA
[
2
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]
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e
t
h
o
d
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[
2
1
]
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M
EA
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LO
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(
M
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)
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9
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5
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6
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2
7
6
3
5
.
8
9
4
2
6
1
0
.
1
2
4
9
5.
CO
NCLU
SI
O
N
In
th
is
wo
r
k
d
y
n
am
ic
m
e
m
b
r
an
e
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
(
DM
E
A)
s
u
cc
ess
f
u
lly
s
o
lv
ed
t
h
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
.
Pro
p
o
s
ed
m
eth
o
d
o
lo
g
y
m
er
g
es th
e
f
u
s
io
n
an
d
d
iv
is
io
n
r
u
les o
f
P
s
y
s
tem
s
with
ac
tiv
e
m
em
b
r
an
es
a
n
d
with
ad
a
p
tiv
e
d
if
f
er
e
n
tial
ev
o
lu
tio
n
(
ADE
)
,
p
ar
ticle
s
wa
r
m
o
p
tim
izatio
n
(
PS
O)
ex
p
lo
r
atio
n
s
tr
atag
em
.
I
n
th
is
p
ap
er
,
co
m
p
o
s
itio
n
o
f
th
e
d
y
n
a
m
ic
m
em
b
r
an
e
al
g
o
r
ith
m
alo
n
g
with
t
h
e
f
u
s
io
n
,
d
iv
is
io
n
r
u
les
ar
e
u
tili
ze
d
to
s
o
lv
e
th
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
le
m
.
I
n
th
is
wo
r
k
,
Gau
s
s
ian
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
is
in
i
tiated
to
e
n
g
en
d
er
th
e
a
cc
eler
atin
g
co
e
f
f
icien
ts
o
f
P
SO
.
Pr
o
p
o
s
ed
d
y
n
a
m
ic
m
em
b
r
an
e
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
(
DM
E
A)
h
as
b
ee
n
t
este
d
i
n
s
ta
n
d
a
r
d
I
E
E
E
1
4
,
3
0
,
5
7
,
1
1
8
,
an
d
3
0
0
b
u
s
test
s
y
s
t
em
a
n
d
s
im
u
l
ati
o
n
r
es
u
lts
s
h
o
w
t
h
e
p
r
o
je
cte
d
al
g
o
r
it
h
m
r
e
d
u
c
ed
t
h
e
r
ea
l
p
o
we
r
l
o
s
s
ex
te
n
s
i
v
e
ly
.
RE
F
E
R
E
NC
E
S
[1
]
K.
Y.
Lee
,
Y.
M
.
P
a
rk
,
J.
L.
Ortiz,
“
F
u
e
l
-
c
o
st
m
in
imis
a
ti
o
n
fo
r
b
o
th
re
a
l
a
n
d
re
a
c
ti
v
e
-
p
o
we
r
d
i
sp
a
tch
e
s,”
IET
Dig
it
a
l
L
i
b
ra
ry
,
v
o
l
.
1
3
1
,
n
o
.
3
,
p
p
.
8
5
-
93
,
1
9
8
4
.
[On
li
n
e
].
Av
a
il
a
b
le:
h
tt
p
s://
d
ig
it
a
l
-
li
b
ra
ry
.
t
h
e
iet.
o
r
g
/co
n
ten
t
/j
o
u
rn
a
ls
/1
0
.
1
0
4
9
/i
p
-
c
.
1
9
8
4
.
0
0
1
2
.
[2
]
N.
I.
De
e
b
,
S
.
M
.
S
h
a
h
id
e
h
p
o
u
r,
“
An
e
fficie
n
t
tec
h
n
i
q
u
e
f
o
r
r
e
a
c
ti
v
e
p
o
we
r
d
isp
a
tc
h
u
sin
g
a
re
v
ise
d
li
n
e
a
r
p
ro
g
ra
m
m
in
g
a
p
p
ro
a
c
h
,
”
El
e
c
tric
Po
we
r
S
y
ste
m
Res
e
a
rc
h
,
v
o
l
.
1
5
,
n
o
.
2
,
p
p
.
1
2
1
-
1
3
4
,
1
9
8
8
.
[O
n
li
n
e
].
Av
a
il
a
b
le:
h
tt
p
s:/
/www
.
sc
ien
c
e
d
irec
t.
c
o
m
/sc
ien
c
e
/article
/ab
s/p
ii
/0
3
7
8
7
7
9
6
8
8
9
0
0
1
6
8
.
[3
]
M
.
Bjelo
g
rli
c
,
M
.
S
.
Ca
lo
v
ic,
P
.
Ristan
o
v
ic
a
n
d
B.
S
.
Ba
b
ic,
“
Ap
p
li
c
a
ti
o
n
o
f
Ne
wto
n
'
s
o
p
ti
m
a
l
p
o
we
r
flo
w
i
n
v
o
lt
a
g
e
/rea
c
ti
v
e
p
o
we
r
c
o
n
tr
o
l,
”
i
n
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r
S
y
ste
ms
,
v
o
l.
5
,
n
o
.
4
,
p
p
.
1
4
4
7
-
1
4
5
4
,
No
v
.
1
9
9
0
.
[On
li
n
e
].
A
v
a
il
a
b
le:
h
tt
p
s://
iee
e
x
p
lo
re
.
iee
e
.
o
r
g
/ab
stra
c
t/
d
o
c
u
m
e
n
t/
9
9
3
9
9
.
[4
]
S
.
G
ra
n
v
il
le,
“
O
p
ti
m
a
l
re
a
c
ti
v
e
d
i
sp
a
tch
t
h
ro
u
g
h
i
n
terio
r
p
o
in
t
m
e
th
o
d
s
,
”
in
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r
S
y
ste
ms
,
v
o
l.
9
,
n
o
.
1
,
p
p
.
1
3
6
-
1
4
6
,
F
e
b
.
1
9
9
4
.
[On
li
n
e
].
Av
a
il
a
b
le:
h
t
tp
s://
iee
e
x
p
lo
re
.
iee
e
.
o
r
g
/ab
stra
c
t/
d
o
c
u
m
e
n
t/
3
1
7
5
4
8
.
[5
]
N.
G
ru
d
in
in
,
“
Re
a
c
ti
v
e
p
o
we
r
o
p
ti
m
iza
ti
o
n
u
si
n
g
su
c
c
e
ss
iv
e
q
u
a
d
ra
ti
c
p
r
o
g
ra
m
m
in
g
m
e
th
o
d
,
”
in
I
EE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r
S
y
ste
ms
,
v
o
l
.
1
3
,
n
o
.
4
,
p
p
.
1
2
1
9
-
1
2
2
5
,
No
v
.
1
9
9
8
.
[On
li
n
e
].
Av
a
il
a
b
le:
h
tt
p
:
//
d
x
.
d
o
i.
o
rg
/1
0
.
1
1
0
9
/
5
9
.
7
3
6
2
3
2
.
[6
]
Ng
S
h
in
M
e
i.
R,
S
u
la
ima
n
M.
H
,
M
u
sta
ffa
Z,
Da
n
iy
a
l
H,
“
Op
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
s
o
lu
ti
o
n
b
y
l
o
ss
m
in
imiz
a
ti
o
n
u
sin
g
m
o
t
h
-
f
lam
e
o
p
ti
m
iza
ti
o
n
tec
h
n
iq
u
e
,
”
Ap
p
li
e
d
S
o
ft
Co
m
p
u
t
in
g
,
v
o
l.
5
9
,
p
p
.
2
1
0
-
2
2
2
,
2
0
1
7
.
[On
li
n
e
].
A
v
a
il
a
b
le:
h
tt
p
s://
ww
w.
sc
ien
c
e
d
irec
t.
c
o
m
/sc
ien
c
e
/article
/
a
b
s/p
ii
/S
1
5
6
8
4
9
4
6
1
7
3
0
3
3
5
6
.
[7
]
Ch
e
n
G
,
Li
u
L,
Zh
a
n
g
Z,
Hu
a
n
g
S
,
“
Op
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
b
y
imp
ro
v
e
d
G
S
A
-
b
a
se
d
a
lg
o
rit
h
m
with
th
e
n
o
v
e
l
stra
teg
ies
to
h
a
n
d
le
c
o
n
st
ra
in
ts”
Ap
p
li
e
d
S
o
f
t
C
o
mp
u
ti
n
g
,
v
o
l.
5
0
,
p
p
.
5
8
-
7
0
,
2
0
1
7
.
[On
li
n
e
].
A
v
a
il
a
b
le
:
h
tt
p
s:/
/www
.
sc
ien
c
e
d
irec
t.
c
o
m
/sc
ien
c
e
/article
/ab
s/p
ii
/S
1
5
6
8
4
9
4
6
1
6
3
0
5
7
7
4
.
[8
]
Na
d
e
ri
E,
Na
rima
n
i
H,
F
a
t
h
i
M
,
Na
rima
n
i
M
.
R
,
“
A
n
o
v
e
l
fu
z
z
y
a
d
a
p
t
iv
e
c
o
n
fi
g
u
ra
ti
o
n
o
f
p
a
rti
c
le
sw
a
rm
o
p
ti
m
iza
ti
o
n
t
o
so
l
v
e
larg
e
-
sc
a
le
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
,
”
Ap
p
li
e
d
S
o
ft
Co
m
p
u
t
in
g
,
v
o
l.
5
3
,
p
p
.
4
4
1
-
4
5
6
,
2
0
1
7
.
[On
li
n
e
].
Av
a
il
a
b
le:
h
t
tp
s://
ww
w.sc
ien
c
e
d
irec
t.
c
o
m
/sc
ien
c
e
/a
rti
c
le/a
b
s/p
ii
/
S
1
5
6
8
4
9
4
6
1
7
3
0
0
1
6
9
.
[9
]
He
id
a
ri
A.
A,
Ali
Ab
b
a
sp
o
u
r
R,
Re
z
a
e
e
Jo
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8
1
8
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.
Evaluation Warning : The document was created with Spire.PDF for Python.