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2
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2
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SS
N:
2252
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8792
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DOI
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222
218
J
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CC B
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C
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p
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A
uth
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r
:
Kan
a
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asab
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L
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Dep
ar
t
m
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t o
f
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h
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Kan
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-
520007
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I
n
d
ia.
E
m
ail:
g
k
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i
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@
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m
ai
l.c
o
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
m
ain
o
b
j
ec
tiv
e
o
f
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m
a
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m
ize
t
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l
p
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w
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lo
s
s
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b
u
s
v
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d
ev
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io
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.
T
o
till
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ate
v
ar
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u
s
m
et
h
o
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o
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g
i
es
h
a
s
b
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n
ap
p
lied
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e
th
e
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tim
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p
o
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p
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lem
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e
k
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y
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f
s
o
lv
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g
r
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cti
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o
b
lem
is
to
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ed
u
ce
th
e
r
ea
l
p
o
w
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lo
s
s
.
P
r
ev
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s
l
y
m
a
n
y
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y
p
es
o
f
m
at
h
e
m
atica
l
m
eth
o
d
o
lo
g
ies
[
1
-
6
]
h
av
e
b
ee
n
u
tili
z
ed
to
s
o
lv
e
th
e
r
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tim
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n
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t
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f
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tio
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g
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r
ith
m
s
[
7
-
1
5
]
h
as
b
ee
n
ap
p
lied
to
s
o
lv
e
t
h
e
r
ea
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p
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s
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ith
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to
s
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e
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m
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ain
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m
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izatio
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o
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ith
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n
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all
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r
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to
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ea
ch
th
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s
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tio
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m
ea
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s
ter
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n
g
m
et
h
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d
is
u
t
ili
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d
to
g
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p
th
e
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s
in
to
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c
lu
s
ter
s
i
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t
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e
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u
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n
th
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k
p
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ain
s
to
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m
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itio
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s
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ce
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ter
s
to
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f
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m
as
p
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s
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m
ai
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s
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; h
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w
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m
t
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s
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Fin
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ter
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ter
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ca
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m
ain
tai
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t
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m
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m
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ett
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tr
ap
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to
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s
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n
t
h
e
p
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j
ec
ted
C
PB
alg
o
r
ith
m
c
h
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tic
th
eo
r
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h
as
b
ee
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ap
p
lied
in
th
e
m
o
d
elin
g
o
f
t
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al
g
o
r
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m
.
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n
th
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p
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p
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alg
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m
m
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p
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test
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t
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5
7
b
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test
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d
th
e
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l p
o
w
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lo
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s
ef
f
ec
ti
v
el
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J
A
p
p
l P
o
w
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E
n
g
I
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N:
2252
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8792
P
o
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K
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b
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in
)
219
2.
P
RO
B
L
E
M
F
O
R
M
UL
AT
I
O
N
R
ed
u
ctio
n
r
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p
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f
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a
s
b
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w
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a
s
f
o
llo
w
s
:
F
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P
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=
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k
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Nbr
(
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j
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−
2
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i
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j
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(
1
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Vo
ltag
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a
th
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m
atic
all
y
w
r
itte
n
as,
F
=
P
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+
ω
v
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Vol
ta
ge
De
via
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(
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Vol
ta
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De
via
tion
=
∑
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−
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|
N
p
q
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=
1
(
3
)
C
o
n
s
tr
ain
t (
e
q
u
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t
y
)
;
P
G
=
P
D
+
P
L
(
4
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C
o
n
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tr
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(
i
n
eq
u
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lit
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;
P
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s
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m
i
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≤
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ac
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≤
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5
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Q
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Q
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≤
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i
∈
N
g
(
6)
V
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m
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n
≤
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≤
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i
m
ax
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∈
N
(
7
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(
8
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Q
c
m
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∈
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C
(
9
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3.
CH
AO
T
I
C
P
R
E
DA
T
O
R
-
P
RE
Y
B
RAIN
ST
O
RM
O
P
T
I
M
I
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AT
I
O
N
A
L
G
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R
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T
H
M
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n
s
id
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th
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ea
r
ch
i
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g
s
p
ac
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aset
o
f
“
N
c”
id
ea
s
ar
e
ar
b
itra
r
il
y
e
n
g
e
n
d
er
ed
.
B
r
ain
s
to
r
m
o
p
ti
m
izat
io
n
alg
o
r
ith
m
p
o
p
u
latio
n
(
B
SO
)
p
o
p
u
latio
n
is
d
ef
i
n
ed
as,
=
{
=
[
1
,
.
.
,
]
|
∈
,
1
≤
≤
}
in
t
h
i
s
s
y
m
b
o
lize
th
e
i
t
h
id
ea
o
f
th
e
B
r
ain
s
to
r
m
o
p
ti
m
izatio
n
alg
o
r
ith
m
p
o
p
u
latio
n
,
=
in
d
icate
th
e
id
ea
in
s
o
lu
tio
n
s
p
ac
e,
Nc
-
p
o
p
u
latio
n
s
ize.
P
r
elim
i
n
ar
y
p
o
p
u
latio
n
X
(
0
)
an
d
th
e
n
th
iter
atio
n
p
o
p
u
latio
n
d
en
o
ted
as
X
(
n
)
.
Fit
n
es
s
v
al
u
e
(
)
is
co
m
p
u
ted
f
o
r
ev
al
u
ated
id
ea
.
B
r
a
in
s
to
r
m
o
p
ti
m
izatio
n
al
g
o
r
it
h
m
[
1
6
,
1
7
]
co
m
m
o
n
l
y
u
s
e
s
g
r
o
u
p
i
n
g
,
r
ep
lacin
g
,
cr
ea
ti
n
g
,
cr
o
s
s
i
n
g
,
an
d
s
elec
ti
n
g
o
p
er
ato
r
s
to
g
en
er
ate
n
e
w
-
f
a
n
g
led
id
ea
s
w
h
ic
h
g
r
o
u
n
d
ed
o
n
th
e
p
r
ese
n
t
id
ea
s
,
in
o
r
d
er
to
p
er
k
u
p
th
e
id
ea
s
in
all
g
en
e
r
atio
n
in
o
r
d
er
t
o
r
ea
ch
th
e
o
p
tim
a
l
s
o
lu
tio
n
.
K
-
m
ea
n
cl
u
s
ter
in
g
m
e
th
o
d
is
u
tili
ze
d
to
g
r
o
u
p
th
e
N
id
ea
s
i
n
to
M
clu
s
t
er
s
in
th
e
g
r
o
u
p
i
n
g
o
p
er
ato
r
.
I
n
o
r
d
er
to
en
g
en
d
er
n
e
w
-
f
a
n
g
led
id
ea
=
[
1
,
2
,
.
.
,
]
,
(
1
≤
≤
)
.
B
r
ain
s
to
r
m
o
p
tim
izatio
n
al
g
o
r
ith
m
p
o
p
u
l
atio
n
f
ir
s
t
v
er
i
f
y
w
h
e
th
er
to
g
en
er
ate
th
e
n
e
w
-
f
a
n
g
led
id
ea
b
ased
o
n
o
n
e
o
r
t
w
o
c
h
o
s
en
cl
u
s
ter
s
.
N
ew
-
f
a
n
g
led
id
ea
is
g
e
n
er
ated
b
y
:
,
=
+
×
(
,
)
(
1
0
)
=
{
,
"
1"
1
1
+
2
2
2
(
1
1
)
=
(
0
.
50
×
−
)
×
(
0
.
1
)
(
1
2
)
On
ce
th
e
n
e
w
-
f
a
n
g
led
id
ea
h
as
b
ee
n
f
o
r
m
ed
,
a
cr
o
s
s
o
v
er
b
et
w
ee
n
n
e
w
-
f
an
g
led
o
n
e
an
d
th
e
p
r
ev
io
u
s
o
n
e
is
co
n
d
u
c
ted
[
1
6
,
1
7
]
.
T
h
r
o
u
g
h
cr
o
s
s
o
v
er
,
′
,
′
ar
e
en
g
en
d
er
ed
to
g
eth
er
b
o
th
th
e
p
r
ev
io
u
s
an
d
n
e
w
l
y
f
o
r
m
ed
o
n
e
ar
e
co
m
p
u
ted
th
e
n
th
e
p
r
ev
io
u
s
o
n
e
is
s
w
ap
b
y
th
e
m
o
s
t
ex
ce
llen
t
o
n
e.
Fo
r
“
N
c
”
ti
m
e
’
s
n
e
w
-
f
a
n
g
led
id
ea
is
cr
ea
ted
cr
ea
tin
g
f
o
r
co
m
p
letio
n
o
f
o
n
e
g
en
er
atio
n
.
O
n
ce
en
d
cr
iter
io
n
s
atis
f
ied
th
en
B
r
ain
s
to
r
m
o
p
ti
m
izatio
n
al
g
o
r
ith
m
p
r
o
ce
d
u
r
e
s
to
p
s
,
o
r
else
it
g
o
to
t
h
e
s
u
b
s
eq
u
en
t
g
e
n
er
atio
n
s
to
r
ep
licate
th
e
g
r
o
u
p
i
n
g
,
r
ep
laci
n
g
,
cr
ea
ti
n
g
,
cr
o
s
s
i
n
g
,
an
d
s
el
ec
t p
r
o
ce
d
u
r
e
[
1
7
]
.
I
n
th
is
w
o
r
k
p
r
ed
a
to
r
-
p
r
ey
b
r
ain
s
to
r
m
o
p
ti
m
izatio
n
p
o
s
itio
n
clu
s
ter
ce
n
ter
s
to
p
er
f
o
r
m
as
p
r
ed
ato
r
s
,
co
n
s
eq
u
e
n
tl
y
it
w
ill
m
o
v
e
to
w
ar
d
s
b
etter
a
n
d
b
etter
p
o
s
iti
o
n
s
,
w
h
ile
t
h
e
r
e
m
ai
n
in
g
id
ea
s
p
e
r
f
o
r
m
a
s
p
r
e
y
s
;
h
en
ce
g
et
a
w
a
y
f
r
o
m
th
e
ir
ad
j
ac
en
t
p
r
ed
at
o
r
s
.
Fin
all
y
c
lu
s
ter
ce
n
ter
s
ca
n
m
ai
n
tai
n
t
h
e
m
o
s
t
ex
ce
lle
n
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8792
I
n
t J
A
p
p
l P
o
w
er
E
n
g
,
Vo
l.
9
,
No
.
3
,
Dec
em
b
er
2
0
2
0
:
218
–
222
220
in
d
iv
id
u
als
o
f
t
h
e
s
w
ar
m
a
n
d
m
o
v
i
n
g
i
n
t
h
e
d
ir
ec
tio
n
o
f
t
h
e
g
lo
b
al
m
o
s
t
e
x
ce
llen
t
p
o
s
iti
o
n
,
b
u
t
at
t
h
e
s
a
m
e
ti
m
e
t
h
e
p
r
ey
o
p
er
atio
n
av
er
t
t
h
e
s
w
ar
m
f
r
o
m
g
e
tti
n
g
tr
ap
p
ed
in
to
lo
ca
l
o
p
tim
u
m
s
o
l
u
tio
n
.
T
h
en
,
th
e
(
1
0
)
ca
n
b
e
r
ep
lace
d
b
y
:
,
=
+
×
(
,
)
+
(
,
−
)
(
1
3
)
,
=
+
×
(
,
)
−
(
,
−
)
−
|
,
−
|
(
1
4
)
“P”
is
a
b
in
ar
y
v
ar
iab
le
w
h
ic
h
d
eter
m
i
n
e
ab
o
u
t
th
e
s
ta
tu
s
o
f
th
e
p
r
e
y
;
f
lee
o
r
n
o
t;
-
w
ei
g
h
t
f
ac
to
r
o
f
th
e
p
r
ed
ato
r
o
p
er
at
o
r
;
a
,
b
-
f
ac
to
r
s
u
s
ed
to
m
ea
s
u
r
e
th
e
co
m
p
le
x
it
y
o
f
f
leein
g
.
=
(
1
5
)
=
100
(
16)
I
n
th
e
p
r
o
j
ec
ted
C
P
B
alg
o
r
ith
m
ch
ao
tic
th
eo
r
y
h
as
b
ee
n
ap
p
lied
in
th
e
m
o
d
elin
g
o
f
t
h
e
alg
o
r
ith
m
.
I
n
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
m
ai
n
p
r
o
p
er
ties
o
f
ch
ao
tic
s
u
ch
a
s
er
g
o
d
icit
y
a
n
d
ir
r
eg
u
lar
it
y
u
s
ed
to
m
a
k
e
th
e
alg
o
r
it
h
m
to
j
u
m
p
o
u
t o
f
t
h
e
lo
ca
l o
p
ti
m
u
m
a
s
w
ell
as to
d
eter
m
i
n
e
o
p
ti
m
al
p
ar
a
m
eter
s
.
ℎ
+
1
=
4
ℎ
(
1
−
ℎ
)
(
1
7
)
A
t
ea
ch
g
e
n
er
atio
n
en
d
,
ch
ao
tic
s
ea
r
ch
w
il
l
b
e
in
tr
o
d
u
ce
d
to
th
e
ex
p
lo
r
atio
n
in
th
e
n
eig
h
b
o
r
h
o
o
d
o
f
th
e
p
r
ese
n
t
b
es
t
s
o
l
u
tio
n
t
o
p
r
ef
er
s
u
p
er
io
r
s
o
lu
tio
n
f
o
r
s
u
b
s
eq
u
en
t
g
e
n
er
atio
n
.
T
h
r
o
u
g
h
t
h
i
s
w
h
en
lo
ca
l
b
est is
r
ea
ch
ed
th
e
n
s
to
p
p
in
g
w
il
l b
e
av
o
id
ed
an
d
also
,
r
ea
c
h
in
g
t
h
e
o
p
ti
m
al
s
o
lu
tio
n
ti
m
e
w
ill b
e
r
ed
u
ce
d
.
Ste
p
a
: P
ar
am
eter
s
ar
e
in
itialized
.
Step
b
:
Ass
e
s
s
m
e
n
t
o
f
all
id
ea
s
,
t
h
e
n
r
ec
o
r
d
th
e
m
o
s
t
e
x
ce
lle
n
t
o
n
e
as
t
h
e
g
lo
b
al
m
o
s
t
e
x
ce
lle
n
t
id
ea
.
I
n
th
e
in
ter
i
m
,
b
y
k
-
m
ea
n
s
clu
s
t
er
in
g
al
g
o
r
ith
m
,
clu
s
ter
th
e
N
c
id
ea
s
in
to
M
clu
s
ter
s
;
s
u
b
s
eq
u
en
tl
y
g
r
ad
e
th
e
id
ea
s
in
ea
c
h
cl
u
s
ter
an
d
r
ec
o
r
d
th
e
m
o
s
t e
x
ce
lle
n
t
id
ea
as c
lu
s
ter
ce
n
ter
in
e
v
er
y
clu
s
ter
.
Step
c
:
C
o
m
p
ar
is
o
n
w
ill
b
e
d
o
n
e
w
it
h
P
r
o
b
ab
ilit
y
to
r
ep
lace
th
e
clu
s
t
er
ce
n
ter
,
w
h
e
n
ar
b
itra
r
y
v
al
u
e
b
et
w
ee
n
0
an
d
1
is
s
m
aller
,
an
d
th
en
ar
b
itra
r
ily
ch
o
o
s
e
a
clu
s
ter
ce
n
ter
to
b
e
s
w
ap
b
y
an
ar
b
itra
r
ily
en
g
e
n
d
e
r
ed
id
ea
; o
r
else,
n
o
t a
n
y
t
h
i
n
g
.
Step
d
:
C
o
m
p
ar
is
o
n
w
il
l
b
e
d
o
n
e
w
i
t
h
p
r
o
b
ab
ilit
y
to
s
elec
t
o
n
e
clu
s
ter
,
w
h
en
ar
b
i
tr
ar
y
v
al
u
e
b
etw
ee
n
0
an
d
1
is
s
m
aller
,
s
u
b
s
eq
u
e
n
tl
y
c
h
o
o
s
e
o
n
e
clu
s
ter
; o
r
else,
p
ick
t
w
o
cl
u
s
ter
s
.
Step
e
:
C
o
m
p
ar
is
o
n
w
ill
b
e
d
o
n
e
with
p
r
o
b
ab
ilit
y
to
s
elec
t
th
e
c
en
ter
o
f
t
h
e
o
n
e
s
elec
ted
w
h
e
n
ar
b
itra
r
y
v
alu
e
b
et
w
ee
n
0
an
d
1
is
s
m
aller
,
s
u
b
s
eq
u
e
n
tl
y
c
h
o
o
s
e
cl
u
s
ter
ce
n
ter
an
d
g
o
to
s
tep
f
;
o
r
else,
ch
o
o
s
e
f
u
r
th
er
id
ea
s
a
n
d
m
o
v
e
to
s
tep
g
.
Step
f
:
W
ith
r
ef
er
en
ce
to
,
=
+
×
(
,
)
+
(
,
−
)
an
d
th
e
m
o
s
t
ex
ce
lle
n
t id
ea
,
m
o
d
er
n
ize
th
e
clu
s
ter
ce
n
ter
(
s
)
,
an
d
s
u
b
s
eq
u
en
tl
y
m
o
v
e
to
s
tep
h
.
Step
g
:
W
ith
r
ef
er
en
ce
to
,
=
+
×
(
,
)
−
(
,
−
)
−
|
,
−
|
m
o
d
er
n
i
ze
th
e
id
ea
s
w
it
h
p
r
o
p
en
s
it
y
o
f
s
t
ir
r
in
g
a
w
a
y
f
r
o
m
th
e
ad
j
o
in
in
g
clu
s
ter
ce
n
ter
s
.
Step
h
:
R
ec
en
tl
y
e
n
g
en
d
er
ed
id
ea
cr
o
s
s
o
v
er
s
w
it
h
t
h
e
cu
r
r
e
n
t
id
ea
to
en
g
en
d
er
t
w
o
m
o
r
e
id
ea
s
.
T
h
en
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f
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n
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al.
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i
:
I
n
t
h
e
r
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o
f
t
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m
o
s
t
ex
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u
b
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ar
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1
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ize
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ce
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ter
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l
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W
h
en
p
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t
n
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m
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iter
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is
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th
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m
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m
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m
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m
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er
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s
,
th
en
m
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v
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r
else
th
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o
r
it
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m
is
s
to
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th
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eter
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m
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t
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t
s
o
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tio
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.
4.
SI
M
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AT
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N
ST
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P
r
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5
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T
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N
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Fig
u
r
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C
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u
r
e
2
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s
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ed
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ctio
n
in
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ce
n
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g
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8792
I
n
t J
A
p
p
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w
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9
,
No
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3
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Dec
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2
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:
218
–
222
222
5.
CO
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SI
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C
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n
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ith
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ith
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h
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s
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n
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ta
n
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RE
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e
,
Y.
M
.
P
a
rk
,
a
n
d
J.
L
.
Ortiz,
"
F
u
e
l
-
c
o
st m
in
im
is
a
ti
o
n
f
o
r
b
o
t
h
re
a
l
-
a
n
d
re
a
c
ti
v
e
-
p
o
w
e
r
d
isp
a
tch
e
s
,
"
in
IE
E
Pro
c
e
e
d
in
g
s C
-
Ge
n
e
ra
ti
o
n
,
T
r
a
n
s
miss
io
n
a
n
d
Distrib
u
ti
o
n
,
v
o
l.
1
3
1
,
n
o
.
3
,
p
p
.
8
5
-
9
3
,
1
9
8
4
.
[2
]
N
.
I.
De
e
b
a
n
d
S.
M.
S
h
a
h
id
e
h
p
o
u
r
,
"
A
n
e
ff
icie
n
t
tec
h
n
iq
u
e
f
o
r
re
a
c
ti
v
e
p
o
w
e
r
d
isp
a
tch
u
sin
g
a
re
v
is
e
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 P
o
we
r S
y
ste
m R
e
se
a
rc
h
,
v
o
l
.
15
,
n
o
.
2
,
p
p
.
1
2
1
-
1
3
4
,
1
9
9
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,
"
A
p
p
li
c
a
ti
o
n
o
f
Ne
w
to
n
'
s
o
p
ti
m
a
l
p
o
w
e
r
f
lo
w
in
v
o
lt
a
g
e
/rea
c
ti
v
e
p
o
w
e
r
c
o
n
tro
l,
"
i
n
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
P
o
we
r S
y
ste
ms
,
v
o
l
.
5
,
n
o
.
4
,
p
p
.
1
4
4
7
-
1
4
5
4
,
1
9
9
0
.
[4
]
S
.
G
ra
n
v
il
le,
"
Op
ti
m
a
l
re
a
c
ti
v
e
d
isp
a
tch
th
r
o
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
,
1
9
9
4
.
[5
]
N.
G
ru
d
in
i
n
,
"
Re
a
c
ti
v
e
p
o
w
e
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
,
"
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.
1
3
,
n
o
.
4
,
p
p
.
1
2
1
9
-
1
2
2
5
,
1
9
9
8
.
[6
]
R.
Ng
S
h
in
M
e
i,
M
.
H.
S
u
laim
a
n
,
Z.
M
u
st
a
f
fa
,
a
n
d
H.
Da
n
iy
a
l
,
"
Op
ti
m
a
l
re
a
c
ti
v
e
p
o
w
e
r
d
isp
a
tch
so
lu
ti
o
n
b
y
lo
ss
m
in
i
m
iza
ti
o
n
u
sin
g
m
o
t
h
-
f
la
m
e
o
p
ti
m
iza
ti
o
n
tec
h
n
i
q
u
e
,
"
A
p
p
l
.
S
o
ft
Co
mp
u
t
,
v
o
l
.
5
9
,
p
p
.
2
1
0
-
2
2
2
,
2
0
1
7
.
[7
]
G
o
n
g
g
u
i
Ch
e
n
,
L
il
a
n
L
iu
,
Zh
izh
o
n
g
Zh
a
n
g
,
a
n
d
S
h
a
n
w
a
i
Hu
a
n
g
,
"
Op
ti
m
a
l
re
a
c
ti
v
e
p
o
w
e
r
d
isp
a
tch
b
y
i
m
p
ro
v
e
d
G
S
A
-
b
a
se
d
a
lg
o
rit
h
m
w
it
h
th
e
n
o
v
e
l
st
ra
teg
ie
s to
h
a
n
d
le co
n
stra
in
t
s
,
"
Ap
p
l.
S
o
ft
C
o
mp
u
t
,
v
o
l
.
5
0
,
p
p
.
58
-
7
0
,
2
0
1
7
.
[8
]
E.
Na
d
e
ri,
H.
Na
rim
a
n
i,
M
.
F
a
t
h
i
,
a
n
d
M
.
R.
Na
rim
a
n
i,
"
A
n
o
v
e
l
f
u
z
z
y
a
d
a
p
ti
v
e
c
o
n
f
ig
u
ra
ti
o
n
o
f
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
t
o
so
lv
e
larg
e
-
sc
a
le
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
w
e
r
d
isp
a
tch
,
"
Ap
p
l.
S
o
ft
Co
mp
u
t
,
v
o
l.
5
3
,
p
p
.
4
4
1
-
4
5
6
,
2
0
1
7
.
[9
]
A
.
A
.
He
id
a
ri,
R.
A
.
A
b
b
a
sp
o
u
r
,
a
n
d
A
.
R.
J
o
rd
e
h
i
,
"
G
a
u
ss
ian
b
a
re
-
b
o
n
e
s
w
a
ter
c
y
c
l
e
a
lg
o
rit
h
m
f
o
r
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
t
c
h
i
n
e
lec
tri
c
a
l
p
o
w
e
r
s
y
ste
m
s
,
"
Ap
p
l.
S
o
ft
C
o
mp
u
t
,
vol
.
5
7
,
p
p
.
6
5
7
-
6
7
1
,
2
0
1
7
.
[1
0
]
M
.
M
o
rg
a
n
,
N
.
R.
H.
A
b
d
u
ll
a
,
M
.
H.
S
u
laim
a
n
,
M
.
M
u
sta
f
a
,
a
n
d
R.
S
a
m
a
d
,
"
Be
n
c
h
m
a
rk
stu
d
ies
o
n
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
(OR
P
D)
b
a
se
d
m
u
lt
i
-
o
b
jec
ti
v
e
e
v
o
lu
ti
o
n
a
ry
p
ro
g
ra
m
m
in
g
(M
OEP
)
u
sin
g
m
u
tati
o
n
b
a
se
d
o
n
a
d
a
p
ti
v
e
m
u
tatio
n
a
d
a
p
ter
(A
M
O)
a
n
d
p
o
ly
n
o
m
ial
m
u
tatio
n
o
p
e
ra
to
r
(
P
M
O)
,
"
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
S
y
ste
ms
,
v
o
l.
12
,
n
o
.
1
,
p
p
1
2
1
-
1
3
2
,
2
0
1
6
.
[1
1
]
Re
b
e
c
c
a
N
g
S
h
in
M
e
i,
M
o
h
d
H
e
rw
a
n
S
u
laim
a
n
,
a
n
d
Zu
rian
i
M
u
sta
ff
a
,
"
A
n
t
li
o
n
o
p
ti
m
ize
r
f
o
r
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
w
e
r
d
isp
a
tch
so
lu
ti
o
n
,
"
J
o
u
rn
a
l
o
f
El
e
c
trica
l
S
y
ste
ms
,
S
p
e
c
ia
l
I
ss
u
e
AM
PE
2
0
1
5
,
p
p
.
6
8
-
7
4
,
2
0
1
5
.
[1
2
]
P
.
A
n
b
a
ra
sa
n
a
n
d
T
.
Ja
y
a
b
a
ra
th
i,
"
Op
ti
m
a
l
re
a
c
ti
v
e
p
o
w
e
r
d
isp
a
tch
p
r
o
b
lem
so
lv
e
d
b
y
s
y
m
b
io
ti
c
o
rg
a
n
is
m
se
a
rc
h
a
lg
o
rit
h
m
,
"
2
0
1
7
In
n
o
v
a
t
io
n
s in
Po
we
r a
n
d
A
d
v
a
n
c
e
d
Co
m
p
u
ti
n
g
T
e
c
h
n
o
l
o
g
ies
(
i
-
PA
CT
)
,
V
e
ll
o
re
,
2
0
1
7
,
p
p
.
1
-
8.
[1
3
]
A
.
G
a
g
li
a
n
o
a
n
d
F
.
No
c
e
ra
,
"
A
n
a
l
y
sis
o
f
th
e
p
e
r
f
o
r
m
a
n
c
e
s
o
f
e
le
c
tri
c
e
n
e
rg
y
sto
ra
g
e
in
re
sid
e
n
ti
a
l
a
p
p
li
c
a
ti
o
n
s
,
"
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
He
a
t
a
n
d
T
e
c
h
n
o
lo
g
y
,
v
o
l.
3
5
,
n
o
.
1
S
I
,
p
p
.
S
4
1
-
S
4
8
,
S
e
p
.
2
0
1
7
.
[1
4
]
M
.
Ca
ld
e
ra
,
P
.
Un
g
a
ro
,
G
.
Ca
m
m
a
ra
ta
,
a
n
d
G
.
P
u
g
li
si
,
"
S
u
rv
e
y
-
b
a
se
d
a
n
a
ly
sis
o
f
th
e
e
lec
tri
c
a
l
e
n
e
rg
y
d
e
m
a
n
d
in
Italian
h
o
u
se
h
o
ld
s
,
"
M
a
t
h
e
ma
ti
c
a
l
M
o
d
e
ll
i
n
g
o
f
E
n
g
in
e
e
rin
g
Pro
b
l
e
ms
,
v
o
l.
5
,
n
o
.
3
,
p
p
.
2
1
7
-
2
2
4
.
2
0
1
8
.
[1
5
]
M
.
Ba
su
,
"
Qu
a
si
-
o
p
p
o
siti
o
n
a
l
d
i
ff
e
r
e
n
ti
a
l
e
v
o
lu
ti
o
n
f
o
r
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
w
e
r
d
isp
a
tch
,
"
El
e
c
trica
l
Po
we
r
a
n
d
En
e
rg
y
S
y
ste
ms
,
v
o
l.
7
8
,
p
p
.
2
9
-
4
0
,
Ju
n
e
2
0
1
6
.
[1
6
]
T
.
Ya
m
a
d
a
,
T
.
Ok
u
d
a
,
M
.
A
b
d
u
ll
a
h
,
M
.
A
w
a
n
g
,
a
n
d
A
.
F
u
ru
k
a
w
a
,
"
T
h
e
lea
f
d
e
v
e
lo
p
m
e
n
t
p
ro
c
e
ss
a
n
d
it
s
s
i
g
n
if
i
c
a
n
c
e
f
o
r
re
d
u
c
i
n
g
se
lf
-
s
h
a
d
i
n
g
o
f
a
tr
o
p
i
c
a
l
p
i
o
n
e
e
r
t
r
e
e
s
p
e
c
ie
s
,
"
O
e
c
o
l
o
g
i
a
,
v
o
l
.
1
2
5
,
n
o
.
4
,
p
p
.
4
7
6
-
4
8
2
,
2
0
0
0
.
[1
7
]
M
.
El
-
A
b
d
,
"
Co
o
p
e
ra
ti
v
e
c
o
e
v
o
lu
ti
o
n
u
sin
g
t
h
e
b
ra
in
sto
rm
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
,
"
2
0
1
6
IEE
E
S
y
mp
o
siu
m
S
e
rie
s
o
n
C
o
mp
u
ta
t
io
n
a
l
I
n
telli
g
e
n
c
e
(
S
S
CI)
,
A
th
e
n
s,
2
0
1
6
,
p
p
.
1
-
7
,
2
0
1
6
.
[1
8
]
IEE
E,
"
T
h
e
IEE
E
-
tes
t
sy
ste
m
s,"
1
9
9
3
.
[
On
li
n
e
]
.
A
v
a
il
a
b
le at:
h
tt
p
s:
//
lab
s.e
c
e
.
u
w
.
e
d
u
/p
stc
a
/p
f
5
7
/
p
g
_
t
c
a
5
7
b
u
s.
h
tm
.
[1
9
]
A.
N
.
Hu
ss
a
in
,
A
.
A
.
A
b
d
u
ll
a
h
,
a
n
d
O
.
M
.
Ne
d
a
,
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M
o
d
if
ied
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iz
a
ti
o
n
f
o
r
so
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t
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re
a
c
ti
v
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p
o
w
e
r
d
i
sp
a
tch
,
"
Res
e
a
r
c
h
J
.
o
f
A
p
p
li
e
d
S
c
ien
c
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s,
E
n
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rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
15
,
n
o
.
8
,
p
p
.
316
-
3
2
7
,
2
0
1
8
.
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