I
nte
rna
t
io
na
l J
o
urna
l o
f
I
nfo
rm
a
t
ics a
nd
Co
m
m
un
ica
t
io
n T
ec
hn
o
lo
g
y
(
I
J
-
I
CT
)
Vo
l.
9
,
No
.
2
,
Au
g
u
s
t
2020
,
p
p
.
92
~
99
I
SS
N:
2252
-
8
7
7
6
,
DOI
: 1
0
.
1
1
5
9
1
/iji
ct.
v
9
i2
.
p
p
92
-
99
92
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ict.
ia
esco
r
e.
co
m
Rea
l
po
wer los
s d
iminutio
n by p
re
destina
tion o
f
par
ticles
wa
v
ering
sea
rch
a
lg
o
rithm
K
a
na
g
a
s
a
ba
i Leni
n
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
,
In
d
ia
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
No
v
1
5
,
2
0
19
R
ev
is
ed
J
an
17
,
2
0
20
Acc
ep
ted
Feb
11
,
2
0
20
In
th
is wo
r
k
P
re
d
e
sti
n
a
ti
o
n
o
f
P
a
r
ti
c
les
Wav
e
rin
g
S
e
a
rc
h
(P
P
S
)
a
lg
o
rit
h
m
h
a
s
b
e
e
n
a
p
p
li
e
d
t
o
s
o
lv
e
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
p
r
o
b
lem
.
P
P
S
a
l
g
o
rit
h
m
h
a
s
b
e
e
n
m
o
d
e
led
b
a
se
d
o
n
th
e
m
o
ti
o
n
o
f
t
h
e
p
a
rti
c
les
in
th
e
e
x
p
l
o
ra
ti
o
n
sp
a
c
e
.
No
rm
a
ll
y
t
h
e
m
o
v
e
m
e
n
t
o
f
th
e
p
a
rti
c
le
is
b
a
se
d
o
n
g
ra
d
ien
t
a
n
d
sw
a
rm
in
g
m
o
ti
o
n
.
P
a
rti
c
les
a
re
p
e
rm
it
ted
to
p
r
o
g
re
ss
in
ste
a
d
y
v
e
lo
c
it
y
i
n
g
ra
d
ien
t
-
b
a
se
d
p
ro
g
re
ss
,
b
u
t
wh
e
n
th
e
o
u
tco
m
e
is
p
o
o
r
wh
e
n
c
o
m
p
a
re
d
t
o
p
re
v
io
u
s
u
p
sh
o
t,
imm
e
d
iate
ly
p
a
rti
c
le
ra
p
id
it
y
wil
l
b
e
u
p
tu
r
n
e
d
wit
h
se
m
i
o
f
t
h
e
m
a
g
n
it
u
d
e
a
n
d
it
wil
l
h
e
lp
to
re
a
c
h
lo
c
a
l
o
p
ti
m
a
l
so
lu
ti
o
n
a
n
d
it
is
e
x
p
re
ss
e
d
a
s
wa
v
e
rin
g
m
o
v
e
m
e
n
t.
I
n
sta
n
d
a
rd
IEE
E
1
4
,
3
0
,
5
7
,
1
1
8
,
3
0
0
b
u
s
sy
ste
m
s
P
ro
p
o
se
d
P
re
d
e
stin
a
ti
o
n
o
f
P
a
rti
c
les
Wav
e
rin
g
S
e
a
rc
h
(
P
P
S
)
a
l
g
o
rit
h
m
is
e
v
a
lu
a
ted
a
n
d
sim
u
latio
n
re
su
lt
s
sh
o
w
th
e
P
P
S
re
d
u
c
e
d
th
e
p
o
we
r
lo
ss
e
fficie
n
tl
y
.
K
ey
w
o
r
d
s
:
Op
tim
al
r
ea
ctiv
e
p
o
wer
Pre
d
esti
n
atio
n
o
f
p
ar
ticl
es
wav
er
in
g
s
ea
r
ch
al
g
o
r
ith
m
T
r
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
:
Kan
ag
asab
ai
L
en
in
,
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
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
o
f
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
b
y
v
ar
iety
o
f
ty
p
es
o
f
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
d
u
e
to
an
ass
o
r
tm
e
n
t
o
f
co
n
s
tr
ain
ts
.
E
v
o
lu
tio
n
a
r
y
tech
n
i
q
u
es
[
7
-
1
5
]
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
,
b
u
t
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
i
n
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
e
ar
ch
o
f
g
lo
b
al
s
o
lu
tio
n
.
I
n
th
is
wo
r
k
,
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
av
er
in
g
Sear
ch
(
PP
S)
alg
o
r
ith
m
h
as
b
ee
n
ap
p
lied
to
s
o
lv
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
.
PP
S
alg
o
r
ith
m
h
as
b
ee
n
m
o
d
eled
b
ased
o
n
th
e
m
o
tio
n
o
f
t
h
e
p
a
r
ticles
in
th
e
ex
p
lo
r
atio
n
s
p
ac
e.
Par
ti
cles
will
ar
b
itra
r
ily
m
o
v
e
in
th
e
ex
p
lo
r
atio
n
s
p
ac
e
in
m
an
y
alg
o
r
ith
m
s
wh
ic
h
h
a
s
b
ee
n
al
r
ea
d
y
ap
p
lied
to
m
a
n
y
o
p
tim
izatio
n
p
r
o
b
lem
s
.
I
n
th
e
PP
S
alg
o
r
ith
m
p
ar
ticles
ar
e
d
is
tr
ib
u
ted
in
th
e
ex
p
lo
r
atio
n
s
p
ac
e
co
n
s
is
ten
tly
.
I
n
an
ato
m
h
o
w
t
h
e
elec
tr
o
n
s
p
o
s
itio
n
ed
in
th
e
ce
n
tr
e
ac
co
r
d
in
g
ly
p
ar
ticles
ar
e
in
t
h
e
e
x
p
lo
r
atio
n
s
p
ac
e.
No
r
m
ally
th
e
m
o
v
em
en
t
o
f
th
e
p
ar
ticle
is
b
ased
o
n
g
r
ad
ien
t
a
n
d
s
war
m
in
g
m
o
tio
n
[
1
6
,
1
7
]
.
W
h
en
th
e
g
r
a
d
ien
t
m
eth
o
d
f
ailed
th
en
s
war
m
in
g
is
ex
ec
u
te
d
b
y
in
d
u
cin
g
th
e
p
a
r
ticle
s
h
if
t
to
war
d
s
t
h
e
g
lo
b
al
m
o
s
t
ex
ce
llen
t p
o
s
itio
n
b
y
m
o
d
e
r
n
izin
g
th
e
v
elo
city
.
Valid
ity
o
f
th
e
Pro
p
o
s
ed
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
av
er
in
g
Sear
ch
(
PP
S)
alg
o
r
ith
m
h
as
b
ee
n
test
e
d
in
s
tan
d
ar
d
I
E
E
E
1
4
,
3
0
,
5
7
,
1
1
8
,
3
0
0
b
u
s
s
y
s
tem
s
an
d
r
esu
lts
s
h
o
w
th
e
p
r
o
je
cted
PP
S r
ed
u
ce
d
th
e
p
o
wer
lo
s
s
ef
f
ec
tiv
ely
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
R
ea
l p
o
w
er lo
s
s
d
imin
u
tio
n
b
y
p
r
ed
esti
n
a
tio
n
o
f
p
a
r
ticles w
a
ve
r
in
g
… (
K
a
n
a
g
a
s
a
b
a
i Len
in
)
93
2.
P
RO
B
L
E
M
F
O
R
M
U
L
AT
I
O
N
Ob
jectiv
e
o
f
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
:
=
=
∑
∈
(
+
−
)
(
1
)
Vo
ltag
e
d
ev
iatio
n
g
iv
en
as f
o
l
lo
ws:
=
+
×
(
2
)
Vo
ltag
e
d
ev
iatio
n
g
iv
en
b
y
:
=
∑
|
−
|
=
(
3
)
C
o
n
s
tr
a
in
t (
E
q
u
a
lity)
=
+
(
4
)
C
o
n
s
tr
a
in
ts
(
I
n
eq
u
a
lity)
≤
≤
(
5
)
≤
≤
,
∈
(
6
)
≤
≤
,
∈
(
7
)
≤
≤
,
∈
(
8
)
Q
c
m
i
n
≤
Q
c
≤
Q
C
m
ax
,
i
∈
N
C
(
9
)
3.
P
RE
DE
ST
I
NA
T
I
O
N
O
F
P
A
RT
I
C
L
E
S WAV
E
R
I
NG
SE
ARCH
AL
G
O
RI
T
H
M
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
a
v
er
in
g
Sear
ch
(
PP
S)
alg
o
r
ith
m
h
as
b
ee
n
m
o
d
eled
b
ased
o
n
th
e
m
o
tio
n
o
f
th
e
p
ar
ticles
in
th
e
ex
p
lo
r
atio
n
s
p
ac
e.
Par
ticles
will
ar
b
itra
r
ily
m
o
v
e
in
th
e
ex
p
l
o
r
a
tio
n
s
p
ac
e
in
m
a
n
y
alg
o
r
ith
m
s
wh
ich
h
as b
ee
n
alr
ea
d
y
a
p
p
lied
t
o
m
a
n
y
o
p
tim
iz
atio
n
p
r
o
b
lem
s
.
I
n
t
h
e
PP
S
alg
o
r
ith
m
p
ar
ticles
ar
e
d
is
tr
ib
u
ted
in
th
e
ex
p
lo
r
atio
n
s
p
ac
e
co
n
s
is
ten
tly
.
I
n
an
ato
m
h
o
w
th
e
elec
tr
o
n
s
p
o
s
itio
n
ed
in
th
e
ce
n
tr
e
ac
co
r
d
in
g
l
y
p
ar
ticles ar
e
in
th
e
ex
p
lo
r
atio
n
s
p
ac
e.
N
o
r
m
ally
th
e
m
o
v
em
en
t
o
f
th
e
p
a
r
ticle
is
b
ased
o
n
g
r
ad
ien
t
an
d
s
war
m
in
g
m
o
tio
n
.
Par
ticles v
elo
city
h
as b
ee
n
i
n
itiated
as f
o
llo
ws,
0
=
[
−
0
2
]
(
1
0
)
Par
ticles
ar
e
p
er
m
itted
to
p
r
o
g
r
ess
in
s
tead
y
v
elo
city
in
g
r
ad
ien
t
-
b
ased
p
r
o
g
r
ess
,
b
u
t
wh
en
th
e
o
u
tc
o
m
e
is
p
o
o
r
wh
en
co
m
p
ar
ed
to
p
r
ev
io
u
s
u
p
s
h
o
t,
i
m
m
ed
iately
p
a
r
ticle
r
ap
id
it
y
will
b
e
u
p
t
u
r
n
ed
with
s
em
i
o
f
th
e
m
ag
n
itu
d
e
a
n
d
i
t
will
h
elp
to
r
ea
ch
lo
c
al
o
p
tim
al
s
o
lu
tio
n
an
d
it
is
ex
p
r
ess
ed
as
wav
er
in
g
m
o
v
em
en
t.
Par
ticle
m
o
v
es
f
r
o
m
p
o
in
t
o
f
s
lo
p
e
1
to
2
t
h
en
it
en
d
’
s
in
n
eg
ativ
e
f
itn
ess
s
lo
p
e
a
n
d
wh
en
th
e
p
ar
ticle
v
elo
city
is
m
u
ltip
lied
b
y
th
e
v
alu
e
-
0
.
5
0
,
s
u
b
s
eq
u
en
tly
th
e
p
ar
ticle
m
o
v
es
f
r
o
m
2
to
3
th
en
s
eq
u
en
tially
it
en
d
’
s
in
p
o
s
itiv
e
f
itn
ess
s
lo
p
e,
th
r
o
u
g
h
th
is
m
o
tio
n
p
ar
ticle
r
ea
c
h
4
af
ter
war
d
s
a
n
eg
ativ
e
f
itn
ess
s
lo
p
e
attain
ed
ag
ain
b
y
th
e
p
ar
ticle
th
en
o
n
ce
ag
ain
b
y
-
0
.
5
0
th
e
p
ar
ticle
v
elo
city
will
b
e
m
u
ltip
lied
.
Nex
t
at
5
p
ar
ticle
will
attain
,
n
o
w
th
e
p
ar
ticle
f
itn
ess
will
b
e
p
o
s
itiv
e
s
lo
p
e,
th
e
n
in
th
e
s
am
e
way
p
ar
ticl
e
co
n
tin
u
es
its
m
o
tio
n
a
n
d
it
r
e
ac
h
th
e
p
o
in
t
6
.
On
ce
p
ar
ticle
r
ea
ch
es
th
e
lo
ca
l
o
p
tim
al
p
o
i
n
t
th
en
th
e
v
elo
city
will
b
e
r
ev
er
s
ed
ag
ain
.
W
h
en
th
e
g
r
ad
ien
t
m
eth
o
d
f
ailed
th
en
s
war
m
in
g
is
ex
ec
u
ted
b
y
in
d
u
cin
g
th
e
p
ar
ticle
s
h
if
t to
war
d
s
t
h
e
g
lo
b
al
m
o
s
t e
x
ce
llen
t p
o
s
itio
n
b
y
m
o
d
e
r
n
izin
g
t
h
e
v
elo
city
as g
iv
en
b
el
o
w,
+
1
=
+
[
−
2
]
(
1
1
)
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
.
2,
Au
g
u
s
t
20
20
:
92
–
99
94
W
h
en
th
e
p
r
o
g
r
ess
d
ev
elo
p
i
n
to
co
n
s
tr
u
ctiv
e
s
u
b
s
eq
u
en
tly
p
ar
ticle
p
r
o
lo
n
g
to
d
is
co
v
er
an
y
m
o
r
e
lo
ca
l
o
p
tim
al
s
o
lu
tio
n
,
an
d
th
is
p
r
o
ce
d
u
r
e
p
e
r
s
is
t
u
n
til
m
ax
im
u
m
n
u
m
b
er
o
f
ev
alu
atio
n
h
as
b
ee
n
attain
ed
.
Pre
d
esti
n
atio
n
o
f
Par
ticles W
av
er
in
g
Sear
c
h
(
PP
S)
alg
o
r
ith
m
d
ef
in
ed
as f
o
llo
ws,
Step
1
I
n
th
e
ex
p
lo
r
atio
n
s
p
ac
e
I
n
itiate
th
e
p
ar
ticle’
s
p
o
s
itio
n
with
r
ef
er
e
n
ce
to
b
o
u
n
d
ar
y
lim
its
Step
2
: i=
1
; k
=1
Step
3
: I
ter
ativ
e
p
r
o
ce
d
u
r
e:
W
ith
r
esp
ec
t to
u
p
p
er
a
n
d
lo
w
er
b
o
u
n
d
ar
ies p
a
r
ticle
p
o
s
itio
n
s
ar
e
in
itiated
W
h
ile
(
i <
=
s
u
m
o
f
p
a
r
ticles)
Par
ticles p
o
s
s
ib
le
co
m
b
in
atio
n
s
h
as to
b
e
d
is
co
v
er
e
d
Fo
r
c=
1
: su
m
o
f
co
m
b
in
atio
n
s
W
it
h
r
esp
ec
t to
p
o
s
itio
n
s
an
d
co
m
b
in
atio
n
s
alter
th
e
p
o
s
itio
n
s
o
f
th
e
p
a
r
ticle
as e
lev
ated
v
alu
es
i +
+
E
n
d
f
o
r
k
++
if
(
k
>
d
i
m
en
s
io
n
s
)
/ w
h
en
n
o
b
o
u
n
d
ar
y
c
o
m
b
in
atio
n
s
ar
e
f
o
u
n
d
th
e
n
leav
e
th
e
lo
o
p
/
B
r
ea
k
E
n
d
if
E
n
d
wh
ile
Step
4
:
B
etwe
en
two
p
ar
ticles
wh
ich
h
as
b
ee
n
alr
ea
d
y
in
itiated
s
o
m
e
m
o
r
e
p
ar
ticles
ar
e
p
r
esen
t,
th
en
f
ac
to
r
b
ased
p
r
o
ce
d
u
r
e
is
ap
p
lied
to
r
eo
r
g
an
ize
th
e
p
ar
ticle
p
o
s
itio
n
s
Par
ticles n
u
m
b
er
ar
e
f
ac
t
o
r
ize
d
f
=f
ac
to
r
(
n
)
; n
=
s
u
m
o
f
p
ar
tic
les ; f
is
an
ar
r
ay
to
s
to
r
e
th
e
f
ac
to
r
v
alu
es
I
ter
ativ
e
p
r
o
ce
d
u
r
e:
W
h
ile
(
i <
=
n
)
Fo
r
c=
1
: su
m
o
f
f
ac
to
r
s
(
with
r
ef
er
en
ce
to
len
g
th
o
f
“f
”
)
Fo
r
j=1
: d
im
en
s
io
n
s
(
p
)
Fo
r
i =
1
:f
(
c)
(
)
=
(
)
+
∗
(
(
)
−
(
)
)
/
(
(
)
+
1
)
i++
E
n
d
E
n
d
if
i >
n
th
en
w
h
en
n
o
b
o
u
n
d
ar
y
co
m
b
in
atio
n
s
ar
e
f
o
u
n
d
t
h
en
l
ea
v
e
th
e
lo
o
p
R
ep
ea
t step
4
with
Min
im
u
m
an
d
Ma
x
im
u
m
ar
e
e
x
ch
an
g
ed
B
r
ea
k
E
n
d
if
E
n
d
f
o
r
E
n
d
wh
ile
T
h
en
with
s
u
itab
le
p
a
r
am
et
er
s
p
r
o
jecte
d
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
av
er
in
g
Sea
r
ch
(
PP
S)
alg
o
r
ith
m
is
ap
p
lied
to
s
o
lv
e
t
h
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
as sh
o
wn
b
elo
w,
Step
1
: I
n
itializatio
n
o
f
p
ar
am
eter
s
Step
2
: I
n
th
e
e
x
p
lo
r
atio
n
s
p
ac
e
I
n
itiate
th
e
p
ar
ticle’
s
p
o
s
itio
n
with
r
ef
er
e
n
ce
to
b
o
u
n
d
ar
y
lim
its
Step
3
: Par
ticles f
itn
ess
v
alu
es
ar
e
co
m
p
u
ted
an
d
m
o
s
t e
x
ce
ll
en
t p
ar
ti
cle
will b
e
id
en
tifie
d
Step
4
: V
elo
city
o
f
th
e
p
ar
ticl
es a
r
e
in
itialized
th
r
o
u
g
h
0
=
[
−
0
2
]
Step
5
: I
ter
ativ
e
p
r
o
ce
d
u
r
e
W
h
ile
(
co
m
p
u
tatio
n
n
u
m
b
er
<
m
ax
im
u
m
n
u
m
b
er
o
f
co
m
p
u
t
atio
n
)
Fo
r
i
=
1
; su
m
o
f
p
ar
ticles
B
y
au
g
m
en
tin
g
th
e
v
elo
cit
y
to
th
e
p
r
esen
t p
o
s
itio
n
d
eter
m
in
e
n
ew
-
f
an
g
le
d
p
o
s
itio
n
W
ith
r
ef
er
en
ce
to
n
ew
-
f
an
g
le
d
p
o
s
itio
n
p
ar
ticle
f
itn
ess
s
h
o
u
l
d
b
e
ca
lcu
lated
Au
g
m
en
tatio
n
s
o
f
co
m
p
u
tatio
n
co
u
n
te
r
,
an
d
th
en
m
o
d
e
r
n
ize
g
lo
b
al
m
o
s
t e
x
ce
llen
t so
lu
tio
n
W
h
en
(
s
lo
p
e
=
=
u
n
k
n
o
wn
)
t
h
en
m
o
d
er
n
ize
s
lo
p
e
o
f
th
e
p
a
r
ticle
with
r
ef
er
en
ce
to
n
ew
f
i
tn
ess
to
b
e
p
o
s
itiv
e
o
r
n
eg
ativ
e;
Oth
e
r
wis
e
wh
en
(
s
lo
p
e
=
=
p
o
s
itiv
e)
W
h
en
(
n
ew
-
f
an
g
led
f
itn
ess
in
f
er
io
r
th
an
p
r
ev
io
u
s
f
itn
ess
)
;
T
h
en
m
o
d
er
n
ize
v
el
o
city
b
y
"
−
2
"
;
m
o
d
er
n
ize
t
h
e
s
lo
p
e
with
r
e
f
er
en
ce
to
n
ew
-
f
an
g
led
f
itn
ess
to
b
e
n
eg
ativ
e;
o
th
er
wis
e
(
s
lo
p
e
=
=
n
eg
ativ
e)
W
h
en
(
n
ew
-
f
an
g
led
f
itn
ess
in
f
er
io
r
th
an
th
e
p
r
e
v
io
u
s
f
itn
ess
)
T
h
en
m
o
d
er
n
ize
v
elo
city
b
y
+
(
−
2
⁄
)
Up
d
ate
s
lo
p
e
to
b
e
u
n
k
n
o
wn
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
R
ea
l p
o
w
er lo
s
s
d
imin
u
tio
n
b
y
p
r
ed
esti
n
a
tio
n
o
f
p
a
r
ticles w
a
ve
r
in
g
… (
K
a
n
a
g
a
s
a
b
a
i Len
in
)
95
E
n
d
if
E
n
d
f
o
r
E
n
d
wh
ile
Step
6
: G
lo
b
al
m
o
s
t e
x
ce
llen
t
p
ar
ticle
p
o
s
itio
n
f
o
u
n
d
with
f
it
n
ess
v
alu
e
Step
7
; O
u
tp
u
t th
e
r
esu
lt
4.
SI
M
UL
A
T
I
O
N
R
E
S
UL
T
S
I
n
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
jecte
d
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
av
er
in
g
Sear
ch
(
PP
S)
alg
o
r
ith
m
h
as
b
ee
n
test
ed
,
T
a
b
le
1
s
h
o
ws
th
e
co
n
s
tr
ain
ts
o
f
co
n
tr
o
l
v
a
r
iab
le
s
T
ab
le
2
s
h
o
ws
th
e
lim
its
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
in
T
ab
le
3
.
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
0
.
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
l
e
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
]
PPS
−1
1
.
0
6
0
1
.
1
0
0
1
.
1
0
0
N
R
*
N
R
*
1
.
0
1
2
−2
1
.
0
4
5
1
.
0
8
5
1
.
0
8
6
1
.
0
2
9
1
.
0
6
0
1
.
0
1
3
−3
1
.
0
1
0
1
.
0
5
5
1
.
0
5
6
1
.
0
1
6
1
.
0
3
6
1
.
0
1
9
−6
1
.
0
7
0
1
.
0
6
9
1
.
0
6
7
1
.
0
9
7
1
.
0
9
9
1
.
0
2
4
−8
1
.
0
9
0
1
.
0
7
4
1
.
0
6
0
1
.
0
5
3
1
.
0
7
8
1
.
0
0
3
8
0
.
9
7
8
1
.
0
1
8
1
.
0
1
9
1
.
0
4
0
.
9
5
0
.
9
0
4
9
0
.
9
6
9
0
.
9
7
5
0
.
9
8
8
0
.
9
4
0
.
9
5
0
.
9
0
3
10
0
.
9
3
2
1
.
0
2
4
1
.
0
0
8
1
.
0
3
0
.
9
6
0
.
9
2
0
−9
0
.
1
9
1
4
.
6
4
0
.
1
8
5
0
.
1
8
0
.
0
6
0
.
1
4
5
2
7
2
.
3
9
2
7
1
.
3
2
2
7
1
.
3
2
N
R
*
N
R
*
2
7
1
.
6
0
(
M
v
a
r
)
8
2
.
4
4
7
5
.
7
9
7
6
.
7
9
N
R
*
N
R
*
7
4
.
7
5
R
e
d
u
c
t
i
o
n
i
n
P
L
o
ss (%)
0
9
.
2
9
.
1
1
.
5
2
.
5
2
4
.
6
7
To
t
a
l
P
Lo
ss (M
w
)
1
3
.
5
5
0
1
2
.
2
9
3
1
2
.
3
1
5
1
3
.
3
4
6
1
3
.
2
1
6
1
0
.
2
0
6
N
R
*
-
N
o
t
r
e
p
o
r
t
e
d
T
h
en
th
e
p
r
o
jecte
d
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
av
er
in
g
Se
ar
ch
(
PP
S)
alg
o
r
ith
m
h
as
b
ee
n
test
ed
,
in
I
E
E
E
3
0
B
u
s
s
y
s
tem
.
T
ab
le
4
s
h
o
ws
th
e
co
n
s
tr
ain
ts
o
f
co
n
tr
o
l
v
ar
ia
b
les,
T
ab
le
5
s
h
o
ws
th
e
lim
its
o
f
r
ea
ctiv
e
p
o
wer
g
e
n
er
ato
r
s
an
d
co
m
p
ar
i
s
o
n
r
esu
lts
ar
e
p
r
esen
ted
in
T
a
b
le
6
.
T
ab
le
4
. 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
3
0
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
5
.
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
3
0
B
u
s
1
0
10
2
-
40
50
5
-
40
40
8
-
10
40
11
-
6
24
13
-
6
24
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
.
2,
Au
g
u
s
t
20
20
:
92
–
99
96
T
ab
le
6
.
Simu
latio
n
r
esu
lts
o
f
I
E
E
E
−3
0
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
]
PPS
−1
1
.
0
6
0
1
.
1
0
1
1
.
1
0
0
N
R
*
N
R
*
1
.
0
1
3
−2
1
.
0
4
5
1
.
0
8
6
1
.
0
7
2
1
.
0
9
7
1
.
0
9
4
1
.
0
1
4
−5
1
.
0
1
0
1
.
0
4
7
1
.
0
3
8
1
.
0
4
9
1
.
0
5
3
1
.
0
6
1
−8
1
.
0
1
0
1
.
0
5
7
1
.
0
4
8
1
.
0
3
3
1
.
0
5
9
1
.
0
0
5
−
1
2
1
.
0
8
2
1
.
0
4
8
1
.
0
5
8
1
.
0
9
2
1
.
0
9
9
1
.
0
2
4
VG
-
13
1
.
0
7
1
1
.
0
6
8
1
.
0
8
0
1
.
0
9
1
1
.
0
9
9
1
.
0
4
3
Ta
p
1
1
0
.
9
7
8
0
.
9
8
3
0
.
9
8
7
1
.
0
1
0
.
9
9
0
.
9
0
4
Ta
p
1
2
0
.
9
6
9
1
.
0
2
3
1
.
0
1
5
1
.
0
3
1
.
0
3
0
.
9
1
2
Ta
p
1
5
0
.
9
3
2
1
.
0
2
0
1
.
0
2
0
1
.
0
7
0
.
9
8
0
.
9
0
6
Ta
p
3
6
0
.
9
6
8
0
.
9
8
8
1
.
0
1
2
0
.
9
9
0
.
9
6
0
.
9
0
5
Q
C
1
0
0
.
1
9
0
.
0
7
7
0
.
0
7
7
0
.
1
9
0
.
1
9
0
.
0
6
4
Q
C
2
4
0
.
0
4
3
0
.
1
1
9
0
.
1
2
8
0
.
0
4
0
.
0
4
0
.
1
0
3
(
M
W
)
3
0
0
.
9
2
9
9
.
5
4
2
9
9
.
5
4
N
R
*
N
R
*
2
9
8
.
6
2
(
M
v
a
r
)
1
3
3
.
9
1
3
0
.
8
3
1
3
0
.
9
4
N
R
*
N
R
*
1
3
0
.
7
4
R
e
d
u
c
t
i
o
n
i
n
P
L
o
ss (%)
0
8
.
4
7
.
4
6
.
6
8
.
3
1
8
.
4
1
To
t
a
l
P
Lo
ss (M
w
)
1
7
.
5
5
1
6
.
0
7
1
6
.
2
5
1
6
.
3
8
1
6
.
0
9
1
4
.
3
1
9
N
R
*
-
N
o
t
r
e
p
o
r
t
e
d
.
T
h
en
th
e
p
r
o
p
o
s
ed
Pre
d
esti
n
a
tio
n
o
f
Par
ticles
W
av
er
in
g
Se
ar
ch
(
PP
S)
alg
o
r
ith
m
h
as
b
ee
n
test
ed
,
in
I
E
E
E
5
7
B
u
s
s
y
s
tem
.
T
ab
le
7
s
h
o
ws
th
e
co
n
s
tr
ain
ts
o
f
co
n
tr
o
l
v
ar
ia
b
les,
T
ab
le
8
s
h
o
ws
th
e
lim
its
o
f
r
ea
ctiv
e
p
o
wer
g
e
n
er
ato
r
s
an
d
co
m
p
ar
i
s
o
n
r
esu
lts
ar
e
p
r
esen
ted
in
T
a
b
le
9
.
T
ab
le
7
.
co
n
s
tr
ain
ts
o
f
co
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
5
7
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
8
.
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
5
7
B
u
s
1
-
1
4
0
2
0
0
2
-
17
50
3
-
10
60
6
-
8
25
8
-
1
4
0
2
0
0
9
-
3
9
12
-
1
5
0
1
5
5
T
ab
le
9
.
Simu
latio
n
r
esu
lts
o
f
I
E
E
E
−5
7
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
]
C
G
A
[
1
8
]
AGA
[
1
8
]
PPS
1
1
.
0
4
0
1
.
0
9
3
1
.
0
8
3
0
.
9
6
8
1
.
0
2
7
1
.
0
2
4
2
1
.
0
1
0
1
.
0
8
6
1
.
0
7
1
1
.
0
4
9
1
.
0
1
1
1
.
0
1
3
3
0
.
9
8
5
1
.
0
5
6
1
.
0
5
5
1
.
0
5
6
1
.
0
3
3
1
.
0
3
3
6
0
.
9
8
0
1
.
0
3
8
1
.
0
3
6
0
.
9
8
7
1
.
0
0
1
1
.
0
1
2
8
1
.
0
0
5
1
.
0
6
6
1
.
0
5
9
1
.
0
2
2
1
.
0
5
1
1
.
0
3
0
9
0
.
9
8
0
1
.
0
5
4
1
.
0
4
8
0
.
9
9
1
1
.
0
5
1
1
.
0
1
4
12
1
.
0
1
5
1
.
0
5
4
1
.
0
4
6
1
.
0
0
4
1
.
0
5
7
1
.
0
4
2
19
0
.
9
7
0
0
.
9
7
5
0
.
9
8
7
0
.
9
2
0
1
.
0
3
0
0
.
9
5
3
20
0
.
9
7
8
0
.
9
8
2
0
.
9
8
3
0
.
9
2
0
1
.
0
2
0
0
.
9
3
4
31
1
.
0
4
3
0
.
9
7
5
0
.
9
8
1
0
.
9
7
0
1
.
0
6
0
0
.
9
2
0
35
1
.
0
0
0
1
.
0
2
5
1
.
0
0
3
N
R
*
N
R
*
1
.
0
1
2
36
1
.
0
0
0
1
.
0
0
2
0
.
9
8
5
N
R
*
N
R
*
1
.
0
0
4
37
1
.
0
4
3
1
.
0
0
7
1
.
0
0
9
0
.
9
0
0
0
.
9
9
0
1
.
0
0
5
41
0
.
9
6
7
0
.
9
9
4
1
.
0
0
7
0
.
9
1
0
1
.
1
0
0
0
.
9
9
0
46
0
.
9
7
5
1
.
0
1
3
1
.
0
1
8
1
.
1
0
0
0
.
9
8
0
1
.
0
1
0
54
0
.
9
5
5
0
.
9
8
8
0
.
9
8
6
0
.
9
4
0
1
.
0
1
0
0
.
9
7
3
58
0
.
9
5
5
0
.
9
7
9
0
.
9
9
2
0
.
9
5
0
1
.
0
8
0
0
.
9
6
2
59
0
.
9
0
0
0
.
9
8
3
0
.
9
9
0
1
.
0
3
0
0
.
9
4
0
0
.
9
6
1
65
0
.
9
3
0
1
.
0
1
5
0
.
9
9
7
1
.
0
9
0
0
.
9
5
0
1
.
0
0
3
66
0
.
8
9
5
0
.
9
7
5
0
.
9
8
4
0
.
9
0
0
1
.
0
5
0
0
.
9
5
2
71
0
.
9
5
8
1
.
0
2
0
0
.
9
9
0
0
.
9
0
0
0
.
9
5
0
1
.
0
0
3
73
0
.
9
5
8
1
.
0
0
1
0
.
9
8
8
1
.
0
0
0
1
.
0
1
0
1
.
0
0
4
76
0
.
9
8
0
0
.
9
7
9
0
.
9
8
0
0
.
9
6
0
0
.
9
4
0
0
.
9
6
1
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
R
ea
l p
o
w
er lo
s
s
d
imin
u
tio
n
b
y
p
r
ed
esti
n
a
tio
n
o
f
p
a
r
ticles w
a
ve
r
in
g
… (
K
a
n
a
g
a
s
a
b
a
i Len
in
)
97
T
ab
le
9
.
Simu
latio
n
r
esu
lts
o
f
I
E
E
E
−5
7
s
y
s
tem
(
C
o
n
tin
u
e
d
)
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
]
C
G
A
[
1
8
]
AGA
[
1
8
]
PPS
80
0
.
9
4
0
1
.
0
0
2
1
.
0
1
7
1
.
0
0
0
1
.
0
0
0
1
.
0
0
3
18
0
.
1
0
.
1
7
9
0
.
1
3
1
0
.
0
8
4
0
.
0
1
6
0
.
1
7
2
25
0
.
0
5
9
0
.
1
7
6
0
.
1
4
4
0
.
0
0
8
0
.
0
1
5
0
.
1
6
0
53
0
.
0
6
3
0
.
1
4
1
0
.
1
6
2
0
.
0
5
3
0
.
0
3
8
0
.
1
4
2
(
M
W
)
1
2
7
8
.
6
1
2
7
4
.
4
1
2
7
4
.
8
1
2
7
6
1
2
7
5
1
2
7
0
.
1
2
(
M
v
a
r
)
3
2
1
.
0
8
2
7
2
.
2
7
2
7
6
.
5
8
3
0
9
.
1
3
0
4
.
4
2
7
2
.
3
3
R
e
d
u
c
t
i
o
n
i
n
P
L
o
ss (%)
0
1
5
.
4
1
4
.
1
9
.
2
1
1
.
6
2
3
.
3
6
To
t
a
l
P
Lo
ss (M
w
)
2
7
.
8
2
3
.
5
1
2
3
.
8
6
2
5
.
2
4
2
4
.
5
6
2
1
.
3
0
5
N
R
*
-
N
o
t
r
e
p
o
r
t
e
d
.
T
h
en
th
e
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
av
er
in
g
Sear
ch
(
PP
S)
alg
o
r
ith
m
h
as
b
ee
n
test
ed
,
in
I
E
E
E
1
1
8
B
u
s
s
y
s
tem
.
T
ab
le
1
0
s
h
o
ws
th
e
co
n
s
tr
ain
ts
o
f
co
n
tr
o
l
v
ar
iab
les
an
d
co
m
p
a
r
is
o
n
r
esu
lts
ar
e
p
r
esen
ted
in
T
a
b
le
1
1
.
T
ab
le
1
0
.
C
o
n
s
tr
ain
ts
o
f
co
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
1
8
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
1
1
.
Simu
latio
n
r
esu
lts
o
f
I
E
E
E
−1
1
8
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
]
P
S
O
[
1
8
]
C
LPS
O
[
1
8
]
PPS
1
0
.
9
5
5
1
.
0
2
1
1
.
0
1
9
1
.
0
8
5
1
.
0
3
3
1
.
0
1
3
4
0
.
9
9
8
1
.
0
4
4
1
.
0
3
8
1
.
0
4
2
1
.
0
5
5
1
.
0
4
2
6
0
.
9
9
0
1
.
0
4
4
1
.
0
4
4
1
.
0
8
0
0
.
9
7
5
1
.
0
2
4
8
1
.
0
1
5
1
.
0
6
3
1
.
0
3
9
0
.
9
6
8
0
.
9
6
6
1
.
0
0
3
10
1
.
0
5
0
1
.
0
8
4
1
.
0
4
0
1
.
0
7
5
0
.
9
8
1
1
.
0
1
2
12
0
.
9
9
0
1
.
0
3
2
1
.
0
2
9
1
.
0
2
2
1
.
0
0
9
1
.
0
2
1
15
0
.
9
7
0
1
.
0
2
4
1
.
0
2
0
1
.
0
7
8
0
.
9
7
8
1
.
0
3
4
18
0
.
9
7
3
1
.
0
4
2
1
.
0
1
6
1
.
0
4
9
1
.
0
7
9
1
.
0
4
2
19
0
.
9
6
2
1
.
0
3
1
1
.
0
1
5
1
.
0
7
7
1
.
0
8
0
1
.
0
3
4
24
0
.
9
9
2
1
.
0
5
8
1
.
0
3
3
1
.
0
8
2
1
.
0
2
8
1
.
0
1
0
25
1
.
0
5
0
1
.
0
6
4
1
.
0
5
9
0
.
9
5
6
1
.
0
3
0
1
.
0
3
1
26
1
.
0
1
5
1
.
0
3
3
1
.
0
4
9
1
.
0
8
0
0
.
9
8
7
1
.
0
5
0
27
0
.
9
6
8
1
.
0
2
0
1
.
0
2
1
1
.
0
8
7
1
.
0
1
5
0
.
9
0
2
31
0
.
9
6
7
1
.
0
2
3
1
.
0
1
2
0
.
9
6
0
0
.
9
6
1
0
.
9
0
1
32
0
.
9
6
3
1
.
0
2
3
1
.
0
1
8
1
.
1
0
0
0
.
9
8
5
0
.
9
1
3
34
0
.
9
8
4
1
.
0
3
4
1
.
0
2
3
0
.
9
6
1
1
.
0
1
5
1
.
0
0
2
36
0
.
9
8
0
1
.
0
3
5
1
.
0
1
4
1
.
0
3
6
1
.
0
8
4
1
.
0
0
1
40
0
.
9
7
0
1
.
0
1
6
1
.
0
1
5
1
.
0
9
1
0
.
9
8
3
0
.
9
6
0
42
0
.
9
8
5
1
.
0
1
9
1
.
0
1
5
0
.
9
7
0
1
.
0
5
1
1
.
0
0
1
46
1
.
0
0
5
1
.
0
1
0
1
.
0
1
7
1
.
0
3
9
0
.
9
7
5
1
.
0
0
2
49
1
.
0
2
5
1
.
0
4
5
1
.
0
3
0
1
.
0
8
3
0
.
9
8
3
1
.
0
0
3
54
0
.
9
5
5
1
.
0
2
9
1
.
0
2
0
0
.
9
7
6
0
.
9
6
3
0
.
9
2
0
55
0
.
9
5
2
1
.
0
3
1
1
.
0
1
7
1
.
0
1
0
0
.
9
7
1
0
.
9
6
1
56
0
.
9
5
4
1
.
0
2
9
1
.
0
1
8
0
.
9
5
3
1
.
0
2
5
0
.
9
5
4
59
0
.
9
8
5
1
.
0
5
2
1
.
0
4
2
0
.
9
6
7
1
.
0
0
0
0
.
9
6
3
61
0
.
9
9
5
1
.
0
4
2
1
.
0
2
9
1
.
0
9
3
1
.
0
7
7
0
.
9
7
0
62
0
.
9
9
8
1
.
0
2
9
1
.
0
2
9
1
.
0
9
7
1
.
0
4
8
0
.
9
8
2
65
1
.
0
0
5
1
.
0
5
4
1
.
0
4
2
1
.
0
8
9
0
.
9
6
8
1
.
0
0
1
66
1
.
0
5
0
1
.
0
5
6
1
.
0
5
4
1
.
0
8
6
0
.
9
6
4
1
.
0
0
2
69
1
.
0
3
5
1
.
0
7
2
1
.
0
5
8
0
.
9
6
6
0
.
9
5
7
1
.
0
5
0
70
0
.
9
8
4
1
.
0
4
0
1
.
0
3
1
1
.
0
7
8
0
.
9
7
6
1
.
0
3
4
72
0
.
9
8
0
1
.
0
3
9
1
.
0
3
9
0
.
9
5
0
1
.
0
2
4
1
.
0
2
0
73
0
.
9
9
1
1
.
0
2
8
1
.
0
1
5
0
.
9
7
2
0
.
9
6
5
1
.
0
1
3
74
0
.
9
5
8
1
.
0
3
2
1
.
0
2
9
0
.
9
7
1
1
.
0
7
3
1
.
0
1
4
76
0
.
9
4
3
1
.
0
0
5
1
.
0
2
1
0
.
9
6
0
1
.
0
3
0
1
.
0
0
5
77
1
.
0
0
6
1
.
0
3
8
1
.
0
2
6
1
.
0
7
8
1
.
0
2
7
1
.
0
0
6
80
1
.
0
4
0
1
.
0
4
9
1
.
0
3
8
1
.
0
7
8
0
.
9
8
5
1
.
0
0
3
85
0
.
9
8
5
1
.
0
2
4
1
.
0
2
4
0
.
9
5
6
0
.
9
8
3
1
.
0
1
4
87
1
.
0
1
5
1
.
0
1
9
1
.
0
2
2
0
.
9
6
4
1
.
0
8
8
1
.
0
1
3
89
1
.
0
0
0
1
.
0
7
4
1
.
0
6
1
0
.
9
7
4
0
.
9
8
9
1
.
0
4
2
90
1
.
0
0
5
1
.
0
4
5
1
.
0
3
2
1
.
0
2
4
0
.
9
9
0
1
.
0
3
1
91
0
.
9
8
0
1
.
0
5
2
1
.
0
3
3
0
.
9
6
1
1
.
0
2
8
1
.
0
0
0
92
0
.
9
9
0
1
.
0
5
8
1
.
0
3
8
0
.
9
5
6
0
.
9
7
6
1
.
0
3
1
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
.
2,
Au
g
u
s
t
20
20
:
92
–
99
98
T
ab
le
1
1
.
Simu
latio
n
r
esu
lts
o
f
I
E
E
E
−1
1
8
s
y
s
tem
(
C
o
n
tin
u
e
d
)
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
]
P
S
O
[
1
8
]
C
LPS
O
[
1
8
]
PPS
99
1
.
0
1
0
1
.
0
2
3
1
.
0
3
7
0
.
9
5
4
1
.
0
8
8
1
.
0
0
3
100
1
.
0
1
7
1
.
0
4
9
1
.
0
3
7
0
.
9
5
8
0
.
9
6
1
1
.
0
0
1
103
1
.
0
1
0
1
.
0
4
5
1
.
0
3
1
1
.
0
1
6
0
.
9
6
1
1
.
0
1
0
104
0
.
9
7
1
1
.
0
3
5
1
.
0
3
1
1
.
0
9
9
1
.
0
1
2
1
.
0
0
1
105
0
.
9
6
5
1
.
0
4
3
1
.
0
2
9
0
.
9
6
9
1
.
0
6
8
1
.
0
5
0
107
0
.
9
5
2
1
.
0
2
3
1
.
0
0
8
0
.
9
6
5
0
.
9
7
6
1
.
0
1
2
110
0
.
9
7
3
1
.
0
3
2
1
.
0
2
8
1
.
0
8
7
1
.
0
4
1
1
.
0
1
4
111
0
.
9
8
0
1
.
0
3
5
1
.
0
3
9
1
.
0
3
7
0
.
9
7
9
1
.
0
0
0
112
0
.
9
7
5
1
.
0
1
8
1
.
0
1
9
1
.
0
9
2
0
.
9
7
6
1
.
0
9
1
113
0
.
9
9
3
1
.
0
4
3
1
.
0
2
7
1
.
0
7
5
0
.
9
7
2
1
.
0
0
0
116
1
.
0
0
5
1
.
0
1
1
1
.
0
3
1
0
.
9
5
9
1
.
0
3
3
1
.
0
0
1
8
0
.
9
8
5
0
.
9
9
9
0
.
9
9
4
1
.
0
1
1
1
.
0
0
4
0
.
9
4
3
32
0
.
9
6
0
1
.
0
1
7
1
.
0
1
3
1
.
0
9
0
1
.
0
6
0
1
.
0
0
0
36
0
.
9
6
0
0
.
9
9
4
0
.
9
9
7
1
.
0
0
3
1
.
0
0
0
0
.
9
5
1
51
0
.
9
3
5
0
.
9
9
8
1
.
0
0
0
1
.
0
0
0
1
.
0
0
0
0
.
9
3
3
93
0
.
9
6
0
1
.
0
0
0
0
.
9
9
7
1
.
0
0
8
0
.
9
9
2
1
.
0
0
2
95
0
.
9
8
5
0
.
9
9
5
1
.
0
2
0
1
.
0
3
2
1
.
0
0
7
0
.
9
7
0
1
0
2
0
.
9
3
5
1
.
0
2
4
1
.
0
0
4
0
.
9
4
4
1
.
0
6
1
1
.
0
0
1
1
0
7
0
.
9
3
5
0
.
9
8
9
1
.
0
0
8
0
.
9
0
6
0
.
9
3
0
0
.
9
4
2
1
2
7
0
.
9
3
5
1
.
0
1
0
1
.
0
0
9
0
.
9
6
7
0
.
9
5
7
1
.
0
0
0
34
0
.
1
4
0
0
.
0
4
9
0
.
0
4
8
0
.
0
9
3
0
.
1
1
7
0
.
0
0
2
44
0
.
1
0
0
0
.
0
2
6
0
.
0
2
6
0
.
0
9
3
0
.
0
9
8
0
.
0
2
1
45
0
.
1
0
0
0
.
1
9
6
0
.
1
9
7
0
.
0
8
6
0
.
0
9
4
0
.
1
6
3
46
0
.
1
0
0
0
.
1
1
7
0
.
1
1
8
0
.
0
8
9
0
.
0
2
6
0
.
1
2
0
48
0
.
1
5
0
0
.
0
5
6
0
.
0
5
6
0
.
1
1
8
0
.
0
2
8
0
.
0
4
2
74
0
.
1
2
0
0
.
1
2
0
0
.
1
2
0
0
.
0
4
6
0
.
0
0
5
0
.
1
1
0
79
0
.
2
0
0
0
.
1
3
9
0
.
1
4
0
0
.
1
0
5
0
.
1
4
8
0
.
1
0
2
82
0
.
2
0
0
0
.
1
8
0
0
.
1
8
0
0
.
1
6
4
0
.
1
9
4
0
.
1
5
0
83
0
.
1
0
0
0
.
1
6
6
0
.
1
6
6
0
.
0
9
6
0
.
0
6
9
0
.
1
2
3
1
0
5
0
.
2
0
0
0
.
1
8
9
0
.
1
9
0
0
.
0
8
9
0
.
0
9
0
0
.
1
5
1
1
0
7
0
.
0
6
0
0
.
1
2
8
0
.
1
2
9
0
.
0
5
0
0
.
0
4
9
0
.
1
3
3
1
1
0
0
.
0
6
0
0
.
0
1
4
0
.
0
1
4
0
.
0
5
5
0
.
0
2
2
0
.
0
0
1
P
G
(
M
W
)
4
3
7
4
.
8
4
3
5
9
.
3
4
3
6
1
.
4
N
R
*
N
R
*
4
3
6
2
.
1
0
Q
G
(
M
V
A
R
)
7
9
5
.
6
6
0
4
.
3
6
5
3
.
5
N
R
*
N
R
*
6
1
0
.
1
1
R
e
d
u
c
t
i
o
n
i
n
P
LO
S
S
(
%)
0
1
1
.
7
1
0
.
1
0
.
6
1
.
3
1
3
.
8
4
To
t
a
l
P
LO
S
S
(
M
w
)
1
3
2
.
8
1
1
7
.
1
9
1
1
9
.
3
4
1
3
1
.
9
9
1
3
0
.
9
6
1
1
4
.
4
1
8
N
R
*
-
N
o
t
r
e
p
o
r
t
e
d
.
T
h
en
I
E
E
E
3
0
0
b
u
s
s
y
s
tem
[
1
8
]
is
u
s
ed
as
tes
t
s
y
s
tem
to
au
th
en
ticate
th
e
g
o
o
d
p
e
r
f
o
r
m
an
ce
o
f
th
e
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
av
er
in
g
Sear
ch
(
PP
S)
a
lg
o
r
ith
m
.
T
ab
le
1
2
s
h
o
ws
th
e
co
m
p
ar
is
o
n
o
f
r
ea
l
p
o
wer
lo
s
s
o
b
tain
ed
af
ter
o
p
tim
izatio
n
.
T
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
M
e
t
h
o
d
EG
A
[
2
0
]
M
e
t
h
o
d
EEA
[
2
0
]
M
e
t
h
o
d
C
S
A
[
2
1
]
PPS
P
LO
S
S
(
M
W
)
6
4
6
.
2
9
9
8
6
5
0
.
6
0
2
7
6
3
5
.
8
9
4
2
6
1
0
.
3
3
7
1
5.
CO
NCLU
SI
O
N
I
n
th
is
wo
r
k
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
av
er
in
g
Sear
c
h
(
PP
S)
alg
o
r
ith
m
s
u
cc
ess
f
u
lly
s
o
lv
ed
th
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
.
I
n
th
e
PP
S
alg
o
r
ith
m
p
ar
ticles
ar
e
d
is
tr
ib
u
ted
in
th
e
ex
p
lo
r
atio
n
s
p
ac
e
co
n
s
is
ten
tly
.
I
n
an
ato
m
h
o
w
th
e
elec
tr
o
n
s
p
o
s
itio
n
ed
in
th
e
ce
n
tr
e
ac
co
r
d
in
g
ly
p
ar
ticles ar
e
in
th
e
ex
p
lo
r
atio
n
s
p
ac
e.
No
r
m
ally
th
e
m
o
v
em
en
t
o
f
t
h
e
p
ar
ticle
is
b
ased
o
n
g
r
ad
ien
t
a
n
d
s
war
m
in
g
m
o
tio
n
.
Par
ticles
ar
e
p
er
m
itted
to
p
r
o
g
r
ess
in
s
tead
y
v
el
o
city
in
g
r
ad
ien
t
-
b
ased
p
r
o
g
r
ess
,
b
u
t
wh
e
n
th
e
o
u
tc
o
m
e
is
p
o
o
r
wh
en
co
m
p
ar
ed
t
o
p
r
ev
i
o
u
s
u
p
s
h
o
t
,
im
m
ed
iately
p
ar
ticle
r
ap
id
it
y
will
b
e
u
p
tu
r
n
e
d
.
I
n
s
tan
d
ar
d
I
E
E
E
1
4
,
3
0
,
5
7
,
1
1
8
,
3
0
0
b
u
s
s
y
s
tem
s
Pre
d
esti
n
atio
n
o
f
Par
ticles
W
av
er
in
g
Sear
ch
(
PP
S)
alg
o
r
ith
m
h
a
v
e
b
ee
n
test
ed
a
n
d
p
o
wer
lo
s
s
h
as b
ee
n
r
ed
u
ce
d
e
f
f
icien
tly
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
R
ea
l p
o
w
er lo
s
s
d
imin
u
tio
n
b
y
p
r
ed
esti
n
a
tio
n
o
f
p
a
r
ticles w
a
ve
r
in
g
… (
K
a
n
a
g
a
s
a
b
a
i Len
in
)
99
RE
F
E
R
E
NC
E
S
[1
]
K.
Y.
Lee
,
“
F
u
e
l
-
c
o
st
m
i
n
imis
a
ti
o
n
fo
r
b
o
t
h
re
a
l
a
n
d
re
a
c
ti
v
e
-
p
o
we
r
d
isp
a
tch
e
s,”
Pr
o
c
e
e
d
in
g
s
Ge
n
e
ra
ti
o
n
,
T
ra
n
sm
issio
n
a
n
d
Distrib
u
ti
o
n
C
o
n
fer
e
n
c
e
,
v
o
l.
1
3
1
,
n
o
.
3
,
p
p
.
85
-
9
3
,
1
9
8
4
.
[2
]
Ao
k
i,
K.,
A.
Nish
ik
o
ri
a
n
d
R.
T.
Yo
k
o
y
a
m
a
,
“
Co
n
stra
in
e
d
l
o
a
d
flo
w u
sin
g
re
c
u
rsiv
e
q
u
a
d
ra
ti
c
p
r
o
g
r
a
m
m
in
g
,
”
IEE
E
T
.
Po
we
r
S
y
st.
,
v
o
l
.
2
,
n
o
.
1
,
p
p
.
8
-
1
6
.
1
9
8
7
.
[3
]
Kirsc
h
e
n
,
D.S
.
a
n
d
H.P
.
Va
n
M
e
e
tere
n
,
"
M
W/
v
o
lt
a
g
e
c
o
n
tro
l
in
a
li
n
e
a
r
p
r
o
g
ra
m
m
in
g
b
a
se
d
o
p
t
ima
l
p
o
we
r
fl
o
w,
"
IEE
E
T
.
Po
we
r
S
y
st
.
,
v
o
l.
3
,
n
o
.
2
,
p
p
.
4
8
1
-
4
8
9
.
1
9
8
8
.
[4
]
Li
u
,
W.
H.E
.
,
A.D.
P
a
p
a
lex
o
p
o
u
l
o
s
a
n
d
W.
F
.
Ti
n
n
e
y
,
“
Disc
re
te
sh
u
n
t
c
o
n
tr
o
ls
i
n
a
Ne
wto
n
o
p
t
ima
l
p
o
we
r
f
lo
w,”
IEE
E
T
.
Po
we
r
S
y
st
.
,
v
o
l.
7
,
n
o
.
4
,
p
p
.
1
5
0
9
-
1
5
1
8
,
1
9
9
2
.
[5
]
V.
H.
Qu
in
tan
a
a
n
d
M
.
S
a
n
t
o
s
-
Nie
to
,
“
Re
a
c
ti
v
e
-
p
o
we
r
d
isp
a
tc
h
b
y
su
c
c
e
ss
iv
e
q
u
a
d
ra
ti
c
p
ro
g
ra
m
m
in
g
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
En
e
rg
y
C
o
n
v
e
rs
i
o
n
,
v
o
l.
4
,
n
o
.
3
,
p
p
.
4
2
5
–
4
3
5
,
1
9
8
9
.
[6
]
V.
d
e
S
o
u
sa
,
E.
Ba
p
ti
sta
,
a
n
d
G
.
d
a
C
o
sta
,
“
Op
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
flo
w
v
ia
th
e
m
o
d
ifi
e
d
b
a
rrier
L
a
g
ra
n
g
ia
n
fu
n
c
ti
o
n
a
p
p
r
o
a
c
h
,
”
El
e
c
tric P
o
w
e
r S
y
ste
ms
Res
e
a
rc
h
,
v
o
l.
8
4
,
n
o
.
1
,
p
p
.
1
5
9
–
1
6
4
,
2
0
1
2
.
[7
]
Y.
Li
,
X.
L
i,
a
n
d
Z.
Li
,
“
Re
a
c
ti
v
e
p
o
we
r
o
p
ti
m
iza
ti
o
n
u
sin
g
h
y
b
rid
CABC
-
DE
a
l
g
o
ri
th
m
,
”
El
e
c
tric
Po
we
r
Co
mp
o
n
e
n
ts
a
n
d
S
y
ste
ms
,
v
o
l.
4
5
,
n
o
.
9
,
p
p
.
9
8
0
–
9
8
9
,
2
0
1
7.
[8
]
Ro
y
,
P
ro
v
a
s
Ku
m
a
r
a
n
d
S
u
sa
n
ta
Du
tt
a
,
“
Ec
o
n
o
m
ic
Lo
a
d
Disp
a
t
c
h
:
Op
ti
m
a
l
P
o
we
r
F
lo
w
a
n
d
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
C
o
n
c
e
p
t
,
”
IGI Gl
o
b
a
l
,
p
p
.
4
6
-
6
4
,
2
0
1
9
.
[9
]
Ch
risti
a
n
Bi
n
g
a
n
e
,
M
i
g
u
e
l
F
.
,
An
jo
s,
S
é
b
a
stien
Le
Di
g
a
b
e
l,
“
Ti
g
h
t
-
a
n
d
-
c
h
e
a
p
c
o
n
ic
re
lax
a
ti
o
n
fo
r
t
h
e
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
is
p
a
tch
p
ro
b
lem
”
,
IEE
E
T
ra
n
s
a
c
ti
o
n
s o
n
P
o
we
r S
y
ste
ms
,
2
0
1
9
,
[1
0
]
Dh
a
rm
b
ir
P
ra
sa
d
&
Vi
v
e
k
a
n
a
n
d
a
M
u
k
h
e
rjee
,
“
S
o
lu
ti
o
n
o
f
O
p
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
b
y
S
y
m
b
io
ti
c
Org
a
n
ism
S
e
a
rc
h
Al
g
o
rit
h
m
I
n
c
o
rp
o
ra
ti
n
g
F
ACTS
De
v
ice
s”
,
IET
E
J
o
u
rn
a
l
o
f
Res
e
a
rc
h
,
v
o
l.
6
4
,
n
o
.
1
,
e
q
u
a
t
p
p
.
1
4
9
-
1
6
0
,
2
0
1
8
.
[1
1
]
TM
Aljo
h
a
n
i,
AF
E
b
ra
h
im,
O
M
o
h
a
m
m
e
d
S
in
g
le,
“
M
u
lt
i
o
b
jec
t
iv
e
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
Ba
se
d
o
n
Hy
b
ri
d
Artifi
c
ial
P
h
y
sic
s
–
P
a
rti
c
le S
wa
rm
Op
ti
m
iza
ti
o
n
,
”
En
e
rg
ies
,
v
o
l.
1
2
,
n
o
.
1
2
,
p
p
.
2
3
3
3
,
2
0
1
9
.
[1
2
]
Ra
m
Kish
a
n
M
a
h
a
te,
&
Him
m
a
t
S
i
n
g
h
,
“
M
u
lt
i
-
Ob
jec
ti
v
e
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
Us
in
g
Diffe
re
n
ti
a
l
Ev
o
l
u
ti
o
n
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
En
g
i
n
e
e
rin
g
T
e
c
h
n
o
l
o
g
ie
s
a
n
d
M
a
n
a
g
e
me
n
t
Res
e
a
rc
h
,
v
o
l.
6
,
n
o
.
2
,
p
p
.
2
7
–
3
8
,
2
0
1
9
.
[1
3
]
Ya
lçın
,
E,
Tap
lam
a
c
ıo
ğ
lu
,
M
,
Ça
m
,
E,
“
Th
e
Ad
a
p
ti
v
e
C
h
a
o
ti
c
S
y
m
b
io
ti
c
Org
a
n
ism
s S
e
a
rc
h
Alg
o
ri
th
m
P
ro
p
o
sa
l
f
o
r
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
P
ro
b
lem
in
P
o
we
r
S
y
ste
m
s,”
.
El
e
c
trica
,
v
o
l.
1
9
,
p
p
.
3
7
-
4
7
,
2
0
1
9
.
[1
4
]
M
o
u
a
ss
a
,
S
.
a
n
d
Bo
u
k
ti
r
,
T,
“
M
u
lt
i
-
o
b
jec
ti
v
e
a
n
t
l
io
n
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
to
s
o
l
v
e
larg
e
-
sc
a
le
m
u
lt
i
-
o
b
jec
ti
v
e
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
is
p
a
tch
p
ro
b
lem
,
”
COMP
EL
-
T
h
e
i
n
ter
n
a
ti
o
n
a
l
jo
u
rn
a
l
f
o
r
c
o
mp
u
ta
ti
o
n
a
n
d
m
a
th
e
ma
t
ics
in
e
lec
trica
l
a
n
d
e
lec
tro
n
ic en
g
in
e
e
rin
g
,
v
o
l.
3
8
,
n
o
.
1
,
p
p
.
3
0
4
-
3
2
4
,
2
0
1
9
.
[1
5
]
Taw
fiq
M
.
Aljo
h
a
n
i,
A
h
m
e
d
F
.
Eb
ra
h
im
&
Os
a
m
a
M
o
h
a
m
m
e
d
,
“
S
in
g
le
a
n
d
M
u
lt
io
b
jec
ti
v
e
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
Ba
se
d
o
n
Hy
b
r
id
Artifi
c
ial
P
h
y
sic
s
–
P
a
rti
c
le
S
wa
r
m
Op
ti
m
iza
ti
o
n
,
"
E
n
e
rg
ies
,
M
DP
I
,
Op
e
n
Ac
c
e
ss
Jo
u
rn
a
l,
v
o
l.
1
2
,
n
o
.
1
2
,
p
p
.
1
-
2
4
,
2
0
1
9
.
[1
6
]
Ba
rto
c
c
in
i,
U.,
Ca
rp
i,
A.
P
o
g
g
io
n
i,
V.,
S
a
n
tu
c
c
i,
V.
,
“
M
e
m
e
s
Ev
o
lu
ti
o
n
in
a
M
e
m
e
ti
c
Va
rian
t
o
f
P
a
rti
c
le
S
wa
rm
Op
ti
m
iza
ti
o
n
,
”
M
a
th
e
ma
ti
c
s
,
v
o
l.
7
,
p
p
.
4
2
3
,
2
0
1
9
.
[1
7
]
F
a
n
,
S
.
K.
S
.
Je
n
,
C
.
H.,
“
An
En
h
a
n
c
e
d
P
a
rti
a
l
S
e
a
rc
h
to
P
a
rti
c
le
S
wa
rm
Op
ti
m
iza
ti
o
n
fo
r
Un
c
o
n
stra
in
e
d
Op
ti
m
iza
ti
o
n
,
”
M
a
th
e
ma
ti
c
s
,
v
o
l.
7
,
p
p
.
3
5
7
,
2
0
1
9
.
[1
8
]
Ali
Na
ss
e
r
Hu
ss
a
in
,
Ali
Ab
d
u
lab
b
a
s
A
b
d
u
ll
a
h
a
n
d
Om
a
r
M
u
h
a
m
m
e
d
Ne
d
a
,
“
M
o
d
ifi
e
d
P
a
rti
c
le
S
wa
rm
Op
ti
m
iza
ti
o
n
f
o
r
S
o
l
u
ti
o
n
o
f
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
,
”
Res
e
a
rc
h
J
o
u
r
n
a
l
o
f
Ap
p
li
e
d
S
c
ien
c
e
s,
E
n
g
i
n
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
1
5
,
n
o
.
8
,
p
p
.
3
1
6
-
3
2
7
,
2
0
1
8
.
[1
9
]
IEE
E,
“
Th
e
I
EE
E
-
tes
t
sy
ste
m
s”
,
ww
w.ee
.
wa
s
h
in
g
to
n
.
e
d
u
/t
rse
a
rc
h
/p
stc
a
/.
1
9
9
3
.
[2
0
]
S
.
S
.
Re
d
d
y
,
e
t
a
l.
,
“
F
a
ste
r
e
v
o
lu
ti
o
n
a
ry
a
lg
o
rit
h
m
b
a
se
d
o
p
ti
m
a
l
p
o
we
r
fl
o
w
u
sin
g
i
n
c
re
m
e
n
tal
v
a
riab
les
,
”
El
e
c
trica
l
Po
we
r a
n
d
En
e
rg
y
S
y
st
e
ms
,
v
o
l.
5
4
,
p
p
.
1
9
8
-
2
1
0
,
2
0
1
4
.
[2
1
]
S
.
S
u
re
n
d
e
r
Re
d
d
y
,
“
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
S
c
h
e
d
u
l
in
g
Us
i
n
g
C
u
c
k
o
o
S
e
a
rc
h
Alg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(IJ
ECE
)
,
v
o
l
.
7
,
n
o
.
5
,
p
p
.
2
3
4
9
-
2
3
5
6
.
2
0
1
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.