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Dec
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20
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pp
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ttp
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Power smo
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trical dis
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y
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a
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imiz
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istri
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rd
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s
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n
d
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u
s
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e
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s.
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e
CM
AES
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a
lg
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rit
h
m
e
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ti
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ti
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ts
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e
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ield
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l
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,
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a
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m
in
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ti
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n
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lt
a
g
e
a
n
d
c
u
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t
p
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tag
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n
d
re
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ize
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lac
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ts.
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m
p
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d
to
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tec
h
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s,
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d
e
m
o
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stra
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fa
ste
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c
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ra
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m
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e
sta
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li
sh
in
g
it
s
e
ffe
c
ti
v
e
n
e
ss
fo
r
s
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c
h
m
u
lt
i
-
o
b
jec
ti
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p
ti
m
iza
ti
o
n
p
ro
b
lem
s.
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e
stu
d
y
'
s
n
o
v
e
lt
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li
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s
in
i
n
teg
ra
t
in
g
C
M
A
-
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h
a
q
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i
la
o
p
ti
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iza
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m
b
in
e
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g
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b
a
l
se
a
rc
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wi
t
h
a
d
a
p
ti
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x
p
lo
ra
ti
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n
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re
su
lt
i
n
g
i
n
ro
b
u
st
a
n
d
e
fficie
n
t
p
o
we
r
sy
ste
m
e
n
h
a
n
c
e
m
e
n
t.
Th
e
p
ro
p
o
se
d
m
e
th
o
d
o
lo
g
y
c
o
n
t
rib
u
t
e
s
to
sm
a
rter,
m
o
re
re
li
a
b
le
d
i
strib
u
ti
o
n
sy
ste
m
s,
su
p
p
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g
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ri
d
re
sili
e
n
c
e
a
n
d
e
n
e
rg
y
e
fficie
n
c
y
.
K
ey
w
o
r
d
s
:
Aq
u
ila
o
p
tim
izatio
n
C
MA
E
S
C
MA
E
SA
O
Dis
tr
ib
u
tio
n
s
y
s
tem
FAC
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S
Po
wer
s
m
o
o
th
in
g
STAT
C
OM
T
h
is i
s
a
n
o
p
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n
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c
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a
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n
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r th
e
CC B
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li
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C
o
r
r
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s
p
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A
uth
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r
:
Sm
r
u
tire
k
h
a
Ma
h
a
n
ta
Sch
o
o
l o
f
E
lectr
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E
n
g
in
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r
in
g
,
KI
I
T
Dee
m
ed
to
b
e
Un
iv
e
r
s
ity
B
h
u
b
an
eswar
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Od
is
h
a,
I
n
d
ia
E
m
ail:
m
.
s
m
r
u
tire
k
h
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8
8
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
R
eb
u
ild
in
g
o
f
elec
tr
ical
n
etw
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r
k
s
was
r
ep
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ted
ly
n
ec
ess
ar
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d
u
e
to
d
ec
r
ea
s
in
g
p
e
r
is
h
ab
le
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s
to
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s
.
R
en
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b
le
en
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y
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o
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r
ce
s
ar
e
n
o
w
i
n
teg
r
ated
in
to
t
h
e
s
y
s
tem
as
a
r
esu
lt
o
f
th
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eb
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g
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Ov
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e
y
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r
s
,
D
-
STAT
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OM
s
h
av
e
b
ec
o
m
e
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n
e
o
f
th
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lead
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s
in
th
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s
o
ciety
'
s
cu
r
r
en
t
p
o
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co
n
s
tr
ain
t.
T
h
e
b
e
n
ef
its
o
f
in
teg
r
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g
D
-
STAT
C
OM
s
in
to
co
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v
en
tio
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g
r
id
s
ar
e
t
h
e
m
o
s
t
cr
u
cial
ar
ea
o
f
s
tu
d
y
f
o
r
D
-
STAT
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OM
lo
ca
tio
n
.
I
t
en
ab
les
o
p
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b
ac
k
o
n
ca
p
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ex
p
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r
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m
a
n
ag
in
g
an
d
im
p
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v
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g
p
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s
y
s
tem
s
.
I
t
co
n
tr
ib
u
tes
to
lo
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in
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x
p
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d
itu
r
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f
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a
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tr
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q
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en
t,
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o
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s
m
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d
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n
etwo
r
k
s
,
an
d
b
o
o
s
tin
g
r
eliab
ilit
y
[
1
]
,
[
2
]
.
Ad
d
itio
n
ally
,
it
h
el
p
s
o
p
er
ato
r
s
in
cr
ea
s
e
ef
f
icien
c
y
an
d
r
e
d
u
ce
p
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wer
tr
an
s
m
is
s
io
n
lo
s
s
.
T
h
e
m
ajo
r
co
n
ce
r
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s
f
o
r
a
d
is
tr
ib
u
tio
n
n
e
two
r
k
ar
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it
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f
er
s
f
r
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m
v
ar
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ch
as
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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E
n
g
I
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N:
2252
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8
7
9
2
P
o
w
er smo
o
th
in
g
in
elec
tr
ica
l d
is
tr
ib
u
tio
n
s
ystem
u
s
in
g
co
va
r
ia
n
ce
ma
tr
ix
…
(
S
mru
tir
ek
h
a
Ma
h
a
n
t
a
)
843
p
o
wer
lo
s
s
es,
p
o
o
r
v
o
ltag
e
lev
els,
an
d
lim
ited
v
o
ltag
e
s
tab
ilit
y
.
T
h
ese
p
r
o
b
lem
s
o
cc
u
r
b
ec
a
u
s
e
th
e
s
y
s
tem
lack
s
s
u
p
p
o
r
t f
o
r
r
ea
ctiv
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p
o
wer
wh
en
th
er
e
is
an
in
cr
ea
s
e
in
d
em
a
n
d
.
T
o
o
v
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o
m
e
th
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ch
allen
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en
g
in
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s
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esear
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h
av
e
p
r
o
p
o
s
ed
d
i
f
f
er
en
t
s
tr
ateg
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[
3
]
,
[
4
]
.
Di
f
f
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t
a
p
p
r
o
ac
h
es
aim
at
im
p
r
o
v
in
g
elec
tr
icity
d
is
tr
ib
u
tio
n
s
y
s
tem
p
er
f
o
r
m
a
n
ce
.
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h
ese
tech
n
iq
u
es
co
n
s
is
t
o
f
estab
lis
h
in
g
v
o
ltag
e
r
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latio
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m
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n
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s
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co
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co
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p
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n
s
atin
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q
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ip
m
en
t
s
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ch
as
s
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t
ca
p
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to
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d
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tr
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e
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ato
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th
e
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etwo
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k
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g
its
co
n
f
ig
u
r
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n
w
h
er
e
n
ec
ess
ar
y
[
5
]
,
[
6
]
.
T
h
e
l
atest
ad
v
an
ce
m
en
ts
s
u
g
g
est
u
tili
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g
f
lex
ib
le
AC
tr
an
s
m
is
s
io
n
s
y
s
tem
(
FAC
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d
ev
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lik
e
d
y
n
a
m
ic
v
o
ltag
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r
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u
lato
r
s
(
DVRs
)
,
D
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STAT
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,
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d
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if
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p
o
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u
ality
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d
itio
n
er
s
(
U
PQC
s
)
.
Am
o
n
g
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ev
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D
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a
s
tr
o
n
g
co
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en
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d
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lo
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r
e
[
7
]
.
B
y
co
n
n
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tin
g
D
-
STAT
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in
p
ar
allel
to
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h
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ilin
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m
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y
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s
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ctu
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o
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ity
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s
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r
s
,
th
er
e
h
a
s
b
ee
n
s
ig
n
if
ican
t
ad
v
an
ce
m
e
n
t
in
estab
lis
h
in
g
a
p
p
r
o
p
r
iate
s
tr
ateg
ies
f
o
r
allo
ca
tin
g
D
-
STAT
C
OM
s
m
o
r
e
ef
f
e
ctiv
ely
.
Sen
s
itiv
ity
-
b
ased
ap
p
r
o
ac
h
es
r
ely
in
g
o
n
d
if
f
er
en
t
i
n
d
icato
r
s
s
u
c
h
as
v
o
lt
ag
e
s
tab
ilit
y
in
d
ex
an
d
o
th
e
r
r
elate
d
v
ar
iab
les
ar
e
co
m
m
o
n
l
y
ap
p
lied
to
d
a
y
[
8
]
,
[
9
]
.
Ho
we
v
er
,
r
elian
ce
o
n
s
u
ch
m
eth
o
d
s
o
f
ten
p
o
s
es
is
s
u
es
with
r
eg
ar
d
t
o
th
ei
r
r
eliab
ilit
y
.
T
o
m
itig
ate
th
is
ch
alle
nge
,
m
an
y
r
esear
ch
e
r
s
h
av
e
b
eg
u
n
ex
p
lo
r
in
g
alter
n
ativ
e
m
eth
o
d
o
lo
g
ies
b
ased
o
n
m
etah
u
r
is
tic
f
r
am
ewo
r
k
f
o
r
attain
in
g
th
e
o
p
tim
al
allo
ca
t
io
n
o
f
D
-
STAT
C
OM
s
.
T
h
is
p
ap
er
in
tr
o
d
u
ce
s
a
n
o
v
el
o
p
tim
izatio
n
tech
n
iq
u
e
(
C
MA
E
SAO)
as
a
m
ea
n
s
to
d
eter
m
in
e
th
e
allo
ca
tio
n
D
-
ST
AT
C
OM
s
.
A
m
o
r
e
r
ec
en
t
m
eta
-
h
eu
r
is
tic
alg
o
r
ith
m
,
ca
lled
C
MA
E
SAO,
i
s
u
s
ed
to
o
p
tim
ize
allo
ca
tio
n
s
o
f
DST
AT
C
OM
s
[
1
0
]
.
T
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
C
MA
E
SAO
tech
n
iq
u
e
d
em
o
n
s
tr
ates
f
aster
co
n
v
er
g
en
ce
,
a
well
-
b
alan
ce
d
ex
p
l
o
r
atio
n
ex
p
lo
itatio
n
p
r
o
ce
s
s
,
an
d
im
p
r
o
v
ed
g
l
ob
al
s
ea
r
ch
ab
ilit
y
[
1
1
]
.
I
ts
ef
f
ec
tiv
e
n
ess
h
as
b
ee
n
v
alid
ated
u
s
in
g
2
3
b
en
ch
m
ar
k
f
u
n
ctio
n
s
,
co
v
er
in
g
v
ar
io
u
s
ty
p
es
s
u
ch
as
co
n
tin
u
o
u
s
,
d
is
co
n
tin
u
o
u
s
,
lin
ea
r
,
n
o
n
lin
ea
r
,
s
ep
ar
a
b
le,
non
-
s
ep
ar
a
b
le,
u
n
im
o
d
al,
an
d
m
u
ltimo
d
al.
T
h
e
r
esu
lts
d
em
o
n
s
tr
ated
th
e
alg
o
r
it
h
m
'
s
ef
f
icien
cy
an
d
r
eliab
ilit
y
in
s
o
lv
in
g
o
p
tim
izatio
n
p
r
o
b
le
m
s
[
1
2
]
.
2.
M
O
DE
L
L
I
NG
O
F
E
L
E
C
T
RICA
L
DIS
T
R
I
B
U
T
I
O
N
S
YST
E
M
Utilizin
g
m
u
ltip
le
co
m
p
o
n
en
ts
s
u
ch
as
a
v
o
ltag
e
s
o
u
r
ce
co
n
v
er
ter
,
DC
ca
p
ac
ito
r
b
u
s
,
co
u
p
lin
g
tr
an
s
f
o
r
m
er
an
d
tu
n
ed
f
ilter
,
t
h
e
D
-
STAT
C
OM
s
h
o
wca
s
es
i
ts
elf
as
a
s
h
u
n
t
d
ev
ice
wo
r
th
t
ak
in
g
n
o
te
o
f
wh
ile
en
er
g
y
s
to
r
a
g
e
d
ev
ices
ar
e
o
p
tio
n
al
in
th
is
d
ev
ice,
it’s
cr
u
cial
to
h
ig
h
lig
h
t
th
at
d
e
p
lo
y
in
g
v
o
ltag
e
s
o
u
r
ce
co
n
v
er
ter
s
e
n
ab
les
its
ab
ilit
y
to
f
u
n
ctio
n
as
a
g
e
n
er
alize
d
im
p
ed
an
ce
co
n
v
er
ter
wh
ich
p
e
r
m
its
im
p
o
r
tin
g
o
r
ex
p
o
r
tin
g
r
ea
ctiv
e
p
o
wer
at
p
o
in
t
o
f
co
m
m
o
n
co
u
p
lin
g
(
PC
C
)
[
1
3
]
.
I
t’
s
n
o
tewo
r
th
y
th
at
wh
en
eq
u
ip
p
e
d
with
an
en
er
g
y
s
to
r
a
g
e
d
ev
ice
to
o
,
a
ctiv
e
p
o
wer
in
jectio
n
is
with
in
r
ea
ch
.
Ho
wev
er
,
th
e
p
r
esen
t
r
e
s
ea
r
ch
o
n
ly
f
o
c
u
s
es
o
n
r
ea
ctiv
e
p
o
wer
r
eg
u
latio
n
f
o
r
p
u
r
p
o
s
es
o
f
th
is
s
tu
d
y
.
Fi
g
u
r
e
1
(
a)
illu
s
tr
ates
a
two
-
b
u
s
eq
u
iv
alen
t
o
f
th
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
f
o
r
[
1
4
]
.
+
1
∠
+
1
=
∠
−
(
+
)
∠
(
1
)
T
h
e
co
m
p
en
s
ated
n
etwo
r
k
,
al
o
n
g
with
t
h
e
DSTA
T
C
OM
co
n
f
ig
u
r
atio
n
a
n
d
th
e
p
h
aso
r
r
ep
r
esen
tatio
n
o
f
th
e
co
m
p
en
s
atio
n
n
etwo
r
k
as d
esc
r
ib
ed
in
(
1
)
a
n
d
(
2
)
,
is
illu
s
tr
ated
in
Fig
u
r
e
1
(
b
)
.
+
1
∠
+
1
=
∠
−
(
+
)
(
Ð
+
+
)
(
2
)
Sin
ce
th
e
D
-
STAT
C
OM
f
u
n
ct
io
n
s
as
a
r
ea
ctiv
e
p
o
wer
s
o
u
r
c
e,
its
cu
r
r
en
t
is
p
h
ase
-
s
h
if
ted
b
y
9
0
d
e
g
r
ee
s
with
r
esp
ec
t
to
t
h
e
v
o
ltag
e
at
th
e
c
o
m
p
en
s
ated
n
o
d
e.
C
o
n
s
eq
u
en
tly
,
it
in
jects
o
r
a
b
s
o
r
b
s
r
ea
ct
iv
e
p
o
we
r
with
o
u
t
af
f
ec
tin
g
th
e
ac
tiv
e
p
o
wer
f
lo
w,
th
er
eb
y
im
p
r
o
v
i
n
g
v
o
lta
g
e
s
tab
ilit
y
an
d
p
o
wer
q
u
ali
ty
at
th
e
p
o
in
t
o
f
co
n
n
ec
tio
n
.
=
2
+
+
1
′
(
3
)
T
h
e
p
o
wer
i
n
jecte
d
b
y
th
e
D
-
STAT
C
OM
ca
n
b
e
ex
p
r
ess
ed
as
(
4
)
.
−
=
+
1
′
∠
+
1
′
+
″
∠
(
2
+
+
1
′
)
(
4
)
T
h
is
r
esear
ch
em
p
lo
y
s
th
e
co
v
ar
ian
ce
m
atr
ix
ad
ap
tatio
n
ev
o
lu
tio
n
s
tr
ateg
y
o
f
aq
u
ila
o
p
tim
i
za
tio
n
to
d
ete
r
m
in
e
th
e
o
p
tim
al
lo
ca
tio
n
s
an
d
ca
p
ac
ities
o
f
m
u
ltip
le
D
-
STAT
C
OM
s
,
aim
in
g
to
m
in
im
ize
ac
ti
v
e
(
r
e
al)
p
o
wer
lo
s
s
in
th
e
elec
tr
ical
d
is
tr
ib
u
tio
n
s
y
s
tem
.
T
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
ad
h
er
es
to
o
p
er
atio
n
al
c
o
n
s
tr
ain
ts
o
u
tlin
ed
in
(
3
)
an
d
(
4
)
.
Sin
ce
d
is
tr
ib
u
tio
n
u
til
ities
p
r
io
r
itize
r
ed
u
cin
g
r
ea
l
p
o
wer
lo
s
s
d
u
e
to
its
d
ir
ec
t
ec
o
n
o
m
ic
im
p
ac
t
m
o
r
e
s
o
th
an
v
o
ltag
e
r
eg
u
latio
n
o
r
s
y
s
tem
s
tab
ilit
y
[
1
5
]
.
T
h
e
p
r
im
ar
y
o
b
jectiv
e
o
f
th
is
r
ese
ar
ch
is
th
e
o
p
tim
al
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
14
,
No
.
4
,
Dec
em
b
er
20
25
:
8
4
2
-
858
844
allo
ca
tio
n
o
f
D
-
STAT
C
OM
s
to
ac
h
iev
e
m
in
im
al
r
ea
l
p
o
wer
lo
s
s
.
Ma
th
em
atica
lly
,
th
e
r
ea
l
p
o
wer
lo
s
s
is
ex
p
r
ess
ed
in
(
5
)
.
=
∑
|
|
=
1
2
×
(
5
)
T
h
e
p
r
im
ar
y
o
b
jectiv
e
is
to
m
in
im
ize
r
ea
l
p
o
wer
lo
s
s
in
t
h
e
d
is
tr
ib
u
tio
n
s
y
s
tem
,
wh
ic
h
is
f
o
r
m
u
lated
as
th
e
o
b
jectiv
e
f
u
n
ctio
n
p
r
esen
ted
i
n
(
6
)
.
=
(
)
=
(
∑
|
|
=
1
2
×
)
(
6
)
T
h
e
n
etwo
r
k
'
s
b
u
s
v
o
ltag
e
is
p
er
m
itted
to
v
ar
y
with
in
a
r
an
g
e
o
f
±
5
%
o
f
th
e
n
o
m
in
al
v
o
lta
g
e.
T
h
e
cu
r
r
en
t
f
l
o
w
with
in
th
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
is
ce
r
tain
ly
to
b
e
af
f
ec
te
d
b
y
t
h
e
p
r
esen
ce
o
f
DSTA
T
C
OM
.
T
h
er
ef
o
r
e,
a
co
n
s
tr
ain
t
is
estab
lis
h
ed
to
r
estrict
th
e
b
r
an
ch
f
lo
w
with
in
th
e
p
r
escr
ib
ed
lim
its
wh
en
D
-
STAT
C
OM
s
ar
e
in
tr
o
d
u
ce
d
.
T
h
e
n
o
d
es
u
s
ed
f
o
r
in
s
er
tio
n
o
f
DSTA
T
C
OM
s
ar
e
n
o
t
p
r
o
n
e
t
o
r
ep
etitio
n
.
T
h
e
s
ize
o
f
allo
ca
te
d
DSTA
T
C
OM
s
m
u
s
t
b
e
b
o
u
n
d
b
y
(
7
)
.
∑
,
=
1
=
∑
,
=
1
(
7
)
T
h
e
allo
ca
tio
n
o
f
DSTA
T
C
OM
s
is
o
p
tim
ized
th
r
o
u
g
h
th
e
u
s
e
o
f
C
MA
E
SAO.
I
t
is
a
co
m
b
i
n
atio
n
o
f
th
e
b
en
ef
its
o
f
two
o
p
tim
izatio
n
tech
n
i
q
u
e
s
ar
e
C
MA
E
S a
n
d
AO
[
1
6
]
.
(
a)
(
b
)
Fig
u
r
e
1
.
DSTA
T
C
OM
co
m
p
e
n
s
atio
n
in
a
d
is
tr
ib
u
tio
n
n
etwo
r
k
:
(
a)
two
-
b
u
s
eq
u
iv
alen
t o
f
t
h
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
an
d
(
b
)
p
h
aso
r
d
iag
r
a
m
o
f
DSTA
T
C
OM
3.
CO
VARIAN
CE
M
AT
RIX A
DAP
T
A
T
I
O
N
E
VO
L
U
T
I
O
N
ST
RA
T
E
G
Y
C
o
v
ar
ian
ce
m
atr
ix
ad
a
p
tatio
n
ev
o
lu
tio
n
s
tr
ateg
y
(
C
MA
-
E
S)
is
a
p
o
wer
f
u
l
ev
o
l
u
tio
n
ar
y
alg
o
r
ith
m
d
esig
n
ed
f
o
r
co
n
tin
u
o
u
s
d
o
m
a
in
o
p
tim
izatio
n
p
r
o
b
lem
s
.
I
t
is
p
ar
ticu
lar
ly
ef
f
ec
tiv
e
f
o
r
n
o
n
-
lin
ea
r
,
n
o
n
-
c
o
n
v
e
x
,
an
d
ill
-
co
n
d
itio
n
ed
o
p
tim
izatio
n
task
s
wh
er
e
g
r
ad
ie
n
t
-
b
ase
d
m
eth
o
d
s
m
ay
s
tr
u
g
g
le
o
r
f
a
il.
T
h
e
co
r
e
id
ea
o
f
C
MA
-
E
S
i
s
to
ev
o
lv
e
a
p
o
p
u
latio
n
o
f
ca
n
d
id
ate
s
o
lu
tio
n
s
b
y
s
am
p
lin
g
f
r
o
m
a
m
u
ltiv
ar
iate
n
o
r
m
al
d
is
tr
ib
u
tio
n
,
th
at
m
ea
n
a
n
d
co
v
ar
ia
n
ce
m
a
tr
ix
ar
e
u
p
d
ate
d
iter
ativ
ely
to
r
ef
lect
th
e
to
p
o
lo
g
y
o
f
th
e
o
b
jectiv
e
f
u
n
ct
io
n
.
At
ea
ch
g
e
n
er
atio
n
,
n
ew
ca
n
d
id
ate
s
o
lu
tio
n
s
(
o
f
f
s
p
r
i
n
g
)
ar
e
s
am
p
led
f
r
o
m
a
Gau
s
s
ian
d
i
s
tr
ib
u
tio
n
ce
n
te
r
ed
ar
o
u
n
d
th
e
cu
r
r
en
t
m
ea
n
.
T
h
e
co
v
ar
ian
ce
m
at
r
ix
p
lay
s
a
cr
iti
ca
l
r
o
le
in
s
h
ap
in
g
th
is
d
is
tr
ib
u
tio
n
,
allo
win
g
t
h
e
alg
o
r
ith
m
t
o
ca
p
tu
r
e
d
ep
e
n
d
e
n
cies
b
etw
ee
n
v
ar
iab
les
an
d
ad
ap
t
th
e
s
ea
r
ch
d
ir
ec
tio
n
.
T
h
e
b
est
-
p
er
f
o
r
m
i
n
g
in
d
iv
id
u
als,
b
ased
o
n
th
e
f
itn
e
s
s
f
u
n
ctio
n
,
ar
e
s
elec
ted
,
an
d
th
e
d
is
tr
ib
u
tio
n
p
ar
a
m
eter
s
(
m
ea
n
,
s
tep
s
ize,
a
n
d
co
v
ar
ian
ce
m
atr
ix
)
ar
e
u
p
d
ate
d
ac
co
r
d
in
g
ly
.
T
h
e
m
o
s
t
d
is
tin
g
u
is
h
in
g
f
ea
t
u
r
e
o
f
C
MA
-
E
S
is
th
e
co
v
ar
ian
ce
m
atr
ix
ad
ap
tatio
n
.
T
h
is
m
ec
h
a
n
is
m
en
ab
les
th
e
alg
o
r
ith
m
to
l
ea
r
n
th
e
s
h
ap
e
o
f
t
h
e
o
b
jectiv
e
f
u
n
ctio
n
la
n
d
s
ca
p
e
d
y
n
am
ically
.
O
v
er
tim
e,
th
e
c
o
v
ar
ian
ce
m
at
r
ix
r
ef
lects
th
e
c
o
r
r
elatio
n
s
tr
u
ctu
r
e
b
etwe
en
v
ar
iab
les,
g
u
id
in
g
th
e
s
am
p
lin
g
p
r
o
ce
s
s
to
war
d
p
r
o
m
is
in
g
r
eg
io
n
s
in
th
e
s
ea
r
ch
s
p
ac
e.
T
h
e
alg
o
r
ith
m
also
i
n
clu
d
e
s
m
ec
h
an
is
m
s
s
u
ch
as
s
tep
-
s
ize
co
n
tr
o
l
an
d
ev
o
lu
tio
n
p
at
h
s
to
im
p
r
o
v
e
co
n
v
er
g
en
ce
an
d
ex
p
lo
r
atio
n
-
ex
p
lo
itatio
n
b
ala
n
ce
.
C
MA
-
E
S
d
o
es
n
o
t
r
eq
u
ir
e
g
r
ad
ien
t
in
f
o
r
m
atio
n
,
m
ak
in
g
it
well
-
s
u
ited
f
o
r
b
lack
-
b
o
x
o
p
ti
m
izatio
n
p
r
o
b
lem
s
.
I
t
h
as
b
ee
n
s
u
cc
ess
f
u
lly
ap
p
lie
d
in
v
ar
io
u
s
d
o
m
ain
s
,
i
n
clu
d
in
g
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el
tu
n
in
g
,
c
o
n
tr
o
l
s
y
s
tem
s
,
r
o
b
o
tics
,
an
d
e
n
g
in
ee
r
in
g
d
e
s
ig
n
.
Desp
ite
its
co
m
p
u
tatio
n
al
co
s
t
d
u
e
to
co
v
ar
ian
ce
m
atr
ix
u
p
d
ates,
its
r
o
b
u
s
tn
ess
an
d
s
elf
-
ad
a
p
tatio
n
ca
p
a
b
ilit
ie
s
m
ak
e
it
o
n
e
o
f
th
e
m
o
s
t
r
eliab
le
ev
o
l
u
tio
n
ar
y
s
tr
ateg
ies
f
o
r
ch
allen
g
in
g
o
p
tim
izatio
n
p
r
o
b
lem
s
.
T
h
e
(
8
)
r
ep
r
esen
ts
th
e
u
p
d
ated
ev
o
lu
tio
n
.
=
×
(
8
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
P
o
w
er smo
o
th
in
g
in
elec
tr
ica
l d
is
tr
ib
u
tio
n
s
ystem
u
s
in
g
co
va
r
ia
n
ce
ma
tr
ix
…
(
S
mru
tir
ek
h
a
Ma
h
a
n
t
a
)
845
W
h
er
e,
,
,
an
d
d
en
o
tes
n
ew
m
ea
n
,
o
l
d
m
ea
n
,
an
d
th
e
e
v
o
lu
tio
n
p
ath
,
r
esp
ec
tiv
ely
.
T
h
e
ad
ap
tatio
n
f
o
r
σ
is
ex
p
r
ess
ed
b
y
(
9
)
.
=
×
(
(
‖
‖
‖
(
0
,
)
‖
−
1
)
)
(
9
)
W
h
er
e
,
,
,
,
,
,
an
d
(
0
,
)
r
ep
r
esen
ts
th
e
n
ew
s
tep
-
s
ize,
o
l
d
s
tep
s
iz
e,
n
o
r
m
aliza
tio
n
co
n
s
tan
t,
d
am
p
in
g
p
ar
am
eter
,
ex
p
ec
ted
v
alu
e,
a
n
d
e
x
p
ec
ted
len
g
t
h
to
u
p
d
ate
t
h
e
co
n
ju
g
ate
ev
o
l
u
tio
n
p
ath
,
r
esp
ec
tiv
ely
.
T
h
e
ad
ap
tatio
n
f
o
r
C
is
ex
p
r
ess
e
d
b
y
(
1
0
)
.
=
(
1
−
1
−
)
×
+
1
×
(
×
)
+
×
∑
×
(
×
)
=
1
(
1
0
)
W
h
er
e
,
,
1
,
,
,
,
,
,
an
d
r
ep
r
es
en
ts
th
e
n
ew
co
v
a
r
ian
ce
m
atr
ix
,
u
p
d
ate
p
ar
am
eter
-
1
,
u
p
d
ate
p
ar
a
m
eter
-
2
,
o
ld
co
v
ar
ian
ce
m
atr
ix
,
co
n
j
u
g
ate
o
f
th
e
ev
o
lu
tio
n
p
at
h
,
weig
h
t
ass
ig
n
ed
to
ea
ch
s
o
lu
tio
n
ca
n
d
id
ate,
weig
h
te
d
d
if
f
e
r
en
c
e
v
ec
to
r
o
f
s
o
lu
tio
n
,
an
d
its
co
n
ju
g
ate
,
r
esp
ec
tiv
ely
[
1
7
]
.
Fig
u
r
e
2
p
r
esen
ts
th
e
f
lo
wch
a
r
t
f
o
r
th
e
co
v
ar
ia
n
ce
m
atr
ix
a
d
ap
tatio
n
ev
o
lu
ti
o
n
s
tr
ateg
y
(
C
MA
E
S
)
tech
n
iq
u
e,
illu
s
tr
atin
g
its
iter
ativ
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
T
h
e
alg
o
r
ith
m
b
eg
in
s
with
th
e
in
itializatio
n
o
f
k
ey
p
ar
am
eter
s
s
u
ch
as
p
o
p
u
latio
n
s
ize,
m
ea
n
v
ec
to
r
,
an
d
co
v
a
r
ian
ce
m
atr
ix
.
I
t
th
en
en
ter
s
a
lo
o
p
wh
er
e
a
n
ew
p
o
p
u
latio
n
o
f
ca
n
d
id
ate
s
o
lu
ti
o
n
s
is
s
am
p
led
f
r
o
m
a
m
u
ltiv
a
r
iate
n
o
r
m
al
d
is
tr
ib
u
tio
n
[
1
8
]
.
T
h
ese
s
o
lu
tio
n
s
a
r
e
ev
alu
ated
u
s
in
g
a
f
itn
ess
f
u
n
c
tio
n
,
an
d
th
e
b
est
-
p
er
f
o
r
m
in
g
in
d
iv
id
u
als
ar
e
s
elec
ted
to
u
p
d
ate
th
e
m
ea
n
an
d
co
v
ar
ian
ce
m
atr
ix
,
ef
f
ec
tiv
ely
g
u
id
in
g
th
e
s
ea
r
ch
to
war
d
s
p
r
o
m
is
in
g
r
eg
io
n
s
in
th
e
s
o
lu
tio
n
s
p
ac
e.
T
h
e
p
r
o
ce
s
s
co
n
tin
u
es
iter
ativ
ely
,
ad
a
p
tin
g
s
tep
s
izes
an
d
co
v
ar
ian
ce
to
im
p
r
o
v
e
c
o
n
v
e
r
g
en
ce
u
n
til
a
s
to
p
p
in
g
cr
iter
io
n
,
s
u
ch
as a
m
ax
im
u
m
n
u
m
b
er
o
f
g
en
er
atio
n
s
o
r
c
o
n
v
er
g
en
ce
t
h
r
esh
o
ld
,
is
m
et
[
1
9
]
.
Fig
u
r
e
2
.
Flo
wch
ar
t
f
o
r
C
MA
E
S tec
h
n
iq
u
e
4.
AQ
UIL
A
O
P
T
I
M
I
Z
AT
I
O
N
(
AO
)
Aq
u
ila
o
p
tim
izatio
n
(
AO)
is
a
r
ec
en
t
n
atu
r
e
-
in
s
p
ir
e
d
m
etah
eu
r
is
tic
alg
o
r
ith
m
t
h
at
m
im
ics
th
e
in
tellig
en
t
h
u
n
tin
g
s
tr
ateg
ies
o
f
th
e
a
q
u
ila
(
ea
g
le)
.
T
h
is
b
ir
d
o
f
p
r
ey
u
s
es
f
o
u
r
d
is
tin
ct
m
eth
o
d
s
—
h
ig
h
s
o
ar
with
v
er
tical
s
to
o
p
,
co
n
to
u
r
f
lig
h
t
w
ith
g
lid
e
attac
k
,
l
o
w
f
lig
h
t
with
s
lo
w
d
escen
t,
an
d
walk
-
a
n
d
-
g
r
ab
—
to
ad
a
p
tiv
ely
ca
tch
its
p
r
ey
,
d
ep
e
n
d
in
g
o
n
t
h
e
tar
g
et'
s
lo
ca
tio
n
an
d
m
o
v
e
m
en
t.
Simi
lar
ly
,
AO
m
o
d
els
t
h
e
ex
p
l
o
r
atio
n
an
d
ex
p
lo
itatio
n
p
h
ases
o
f
o
p
tim
i
za
tio
n
b
ased
o
n
t
h
ese
h
u
n
tin
g
s
tr
ateg
ies,
en
ab
lin
g
it
to
s
witch
d
y
n
am
ically
b
etwe
en
g
lo
b
al
an
d
lo
ca
l
s
ea
r
ch
es
d
u
r
in
g
th
e
p
r
o
b
lem
-
s
o
lv
i
n
g
p
r
o
ce
s
s
.
I
n
th
e
AO
alg
o
r
ith
m
,
th
e
o
p
tim
izatio
n
b
eg
in
s
b
y
in
itializin
g
a
p
o
p
u
la
tio
n
o
f
ca
n
d
id
ate
s
o
lu
tio
n
s
.
T
h
e
to
tal
n
u
m
b
er
o
f
ca
n
d
id
ates
is
d
en
o
ted
b
y
N,
a
n
d
ea
ch
ca
n
d
i
d
ate
o
p
e
r
ates
with
in
a
s
o
lu
tio
n
s
p
ac
e
d
e
f
in
ed
b
y
th
e
d
im
e
n
s
io
n
D.
T
h
ese
ca
n
d
id
ates
f
o
r
m
a
two
-
d
im
en
s
io
n
al
ar
r
a
y
X
o
f
s
ize
N×
D
tim
es
N×
D,
wh
er
e
ea
ch
r
o
w
r
e
p
r
esen
ts
a
p
o
ten
tial
s
o
lu
tio
n
.
T
h
e
in
itial
p
o
s
itio
n
s
ar
e
r
a
n
d
o
m
ly
g
en
er
ated
with
in
th
e
d
ef
i
n
ed
b
o
u
n
d
ar
ies
o
f
th
e
p
r
o
b
lem
.
AO
th
e
n
ap
p
lies
o
n
e
o
f
t
h
e
f
o
u
r
m
o
d
eled
h
u
n
tin
g
b
eh
av
i
o
r
s
b
ased
o
n
a
p
r
o
b
ab
ilis
tic
s
elec
tio
n
m
ec
h
an
is
m
,
wh
ich
is
m
ath
em
atica
lly
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
14
,
No
.
4
,
Dec
em
b
er
20
25
:
8
4
2
-
858
846
g
o
v
er
n
ed
b
y
(
1
3
)
,
allo
win
g
th
e
alg
o
r
ith
m
to
b
alan
ce
ex
p
lo
r
a
tio
n
an
d
e
x
p
lo
itatio
n
e
f
f
ec
tiv
e
ly
.
As th
e
iter
atio
n
s
p
r
o
g
r
ess
,
ea
ch
ca
n
d
id
ate'
s
p
o
s
itio
n
is
u
p
d
ated
ac
co
r
d
in
g
to
th
e
s
tr
ateg
y
s
elec
ted
in
th
at
iter
atio
n
.
T
h
e
p
er
f
o
r
m
an
ce
o
r
f
itn
ess
o
f
ea
ch
ca
n
d
id
at
e
is
ev
alu
ated
u
s
in
g
a
p
r
o
b
lem
-
s
p
ec
if
ic
f
itn
ess
f
u
n
ctio
n
.
T
h
e
b
est
ca
n
d
id
ate
is
p
r
eser
v
ed
ac
r
o
s
s
g
en
er
atio
n
s
,
an
d
th
e
p
o
p
u
la
tio
n
ad
ap
ts
ac
c
o
r
d
in
g
ly
.
AO's
ad
ap
tab
ilit
y
an
d
s
tr
ateg
ic
u
p
d
ate
m
ec
h
an
is
m
en
ab
le
it
to
co
n
v
er
g
e
q
u
ick
l
y
to
th
e
g
lo
b
al
o
p
tim
u
m
wh
ile
av
o
id
in
g
lo
ca
l
m
in
im
a,
m
ak
in
g
it
s
u
itab
le
f
o
r
s
o
lv
in
g
co
m
p
lex
o
p
tim
izatio
n
p
r
o
b
lem
s
in
v
ar
io
u
s
en
g
in
ee
r
i
n
g
an
d
co
m
p
u
tatio
n
al
d
o
m
ain
s
[
2
0
]
.
Fig
u
r
e
3
illu
s
tr
ates
th
e
f
lo
wch
ar
t
f
o
r
th
e
a
q
u
ila
o
p
tim
izatio
n
(
AO)
al
g
o
r
ith
m
,
d
e
p
ictin
g
its
s
tr
u
ctu
r
ed
ap
p
r
o
ac
h
to
s
o
lv
in
g
co
m
p
le
x
o
p
tim
izatio
n
p
r
o
b
lem
s
.
T
h
e
p
r
o
ce
s
s
b
eg
in
s
with
th
e
i
n
itializatio
n
o
f
k
ey
p
ar
am
eter
s
,
in
clu
d
in
g
t
h
e
n
u
m
b
er
o
f
ca
n
d
i
d
ate
s
o
lu
tio
n
s
,
d
i
m
en
s
io
n
ality
o
f
th
e
p
r
o
b
lem
s
p
ac
e,
an
d
b
o
u
n
d
a
r
y
co
n
s
tr
ain
ts
.
An
in
itial
p
o
p
u
lat
io
n
o
f
p
o
te
n
tial
s
o
lu
tio
n
s
is
r
a
n
d
o
m
ly
g
en
e
r
ated
an
d
ev
al
u
at
ed
u
s
in
g
a
d
ef
i
n
ed
f
itn
ess
f
u
n
ctio
n
.
T
h
e
b
est
s
o
lu
tio
n
am
o
n
g
t
h
em
is
id
en
tifie
d
an
d
s
to
r
ed
f
o
r
r
ef
er
e
n
ce
[
2
1
]
.
B
ased
o
n
a
r
an
d
o
m
p
r
o
b
a
b
ilit
y
an
d
t
h
e
cu
r
r
en
t
iter
atio
n
,
th
e
alg
o
r
ith
m
s
elec
ts
o
n
e
o
f
th
e
f
o
u
r
a
q
u
ila
-
in
s
p
ir
e
d
h
u
n
tin
g
s
tr
ateg
ies
to
g
u
id
e
th
e
m
o
v
em
en
t
o
f
ca
n
d
i
d
ates
r
an
g
in
g
f
r
o
m
g
lo
b
al
s
o
ar
in
g
a
n
d
g
lid
in
g
to
p
r
ec
is
e
lo
ca
l
attac
k
s
.
I
n
th
e
iter
ativ
e
lo
o
p
,
ea
ch
ca
n
d
id
ate
s
o
lu
tio
n
is
u
p
d
ated
b
ased
o
n
t
h
e
s
elec
ted
s
tr
ateg
y
,
r
ef
lectin
g
th
e
d
y
n
am
ic
n
at
u
r
e
o
f
a
q
u
ila’
s
h
u
n
tin
g
b
eh
av
i
o
r
[
2
2
]
.
T
h
ese
s
tr
ateg
ies b
alan
ce
ex
p
lo
r
atio
n
(
s
ea
r
ch
in
g
n
ew
ar
e
as)
an
d
ex
p
l
o
itatio
n
(
r
ef
in
in
g
k
n
o
w
n
g
o
o
d
s
o
lu
tio
n
s
)
.
Af
ter
ea
ch
u
p
d
ate,
th
e
f
itn
e
s
s
o
f
th
e
n
ew
ca
n
d
id
ates
is
ev
alu
ated
,
an
d
th
e
b
est
s
o
lu
tio
n
is
u
p
d
ated
if
a
s
u
p
e
r
i
o
r
o
n
e
is
f
o
u
n
d
[
2
3
]
.
T
h
is
p
r
o
ce
s
s
r
ep
ea
ts
u
n
til
a
ter
m
in
atio
n
cr
iter
io
n
,
s
u
ch
as
r
ea
ch
in
g
th
e
m
a
x
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
o
r
ac
h
ie
v
in
g
a
s
atis
f
ac
to
r
y
f
itn
ess
lev
el,
is
m
et.
T
h
e
f
lo
wch
ar
t
ef
f
ec
tiv
ely
ca
p
t
u
r
es
th
e
a
d
ap
ti
v
e
an
d
in
tellig
en
t
n
atu
r
e
o
f
A
O,
s
h
o
wca
s
in
g
h
o
w
it
em
u
lat
es
a
q
u
ila'
s
f
lex
ib
le
h
u
n
tin
g
tactics to
n
av
ig
ate
th
e
s
ea
r
ch
s
p
ac
e
an
d
f
in
d
o
p
tim
al
s
o
lu
tio
n
s
[
2
4
]
.
Fig
u
r
e
3
.
Flo
wch
ar
t
f
o
r
AO
5.
CO
VARIAN
CE
M
AT
R
I
X
ADAP
T
AT
I
O
N
E
VO
L
U
T
I
O
N
S
T
RA
T
E
G
Y
B
A
S
E
D
AQ
UI
L
A
O
P
T
I
M
I
Z
AT
I
O
N
T
h
e
co
v
ar
ia
n
ce
m
atr
ix
ad
a
p
tatio
n
ev
o
lu
tio
n
s
tr
ateg
y
-
b
ase
d
aq
u
ila
o
p
tim
izatio
n
(
AOCMAE
S)
is
a
n
o
v
el
h
y
b
r
id
al
g
o
r
ith
m
t
h
at
co
m
b
in
es
th
e
s
tr
en
g
th
s
o
f
two
p
o
wer
f
u
l
o
p
tim
izatio
n
tech
n
iq
u
es
aq
u
ila
o
p
tim
izatio
n
(
AO)
a
n
d
c
o
v
ar
ia
n
ce
m
atr
ix
a
d
ap
tatio
n
e
v
o
lu
tio
n
s
tr
ateg
y
(
C
MA
E
S).
AO
is
k
n
o
wn
f
o
r
its
ef
f
icien
t
g
lo
b
al
s
ea
r
ch
ca
p
ab
ilit
ies
in
s
p
ir
ed
b
y
th
e
h
u
n
tin
g
s
tr
ateg
ies
o
f
a
q
u
ila
ea
g
les.
Ho
wev
er
,
d
e
s
p
ite
its
ex
p
lo
r
ato
r
y
s
tr
en
g
th
,
AO
o
f
ten
s
u
f
f
er
s
f
r
o
m
s
lo
wer
co
n
v
er
g
e
n
ce
,
lo
n
g
er
r
u
n
tim
es,
an
d
a
ten
d
e
n
cy
to
g
et
tr
ap
p
ed
in
lo
ca
l
o
p
tim
a
wh
e
n
d
ea
lin
g
with
c
o
m
p
lex
p
r
o
b
lem
la
n
d
s
ca
p
es.
T
o
o
v
e
r
co
m
e
th
ese
lim
itatio
n
s
,
th
e
p
r
o
p
o
s
ed
AOCMAE
S
in
teg
r
ates
th
e
lo
ca
l
s
ea
r
ch
ef
f
icien
cy
o
f
C
MA
E
S
in
to
th
e
AO
f
r
am
ewo
r
k
.
C
MA
E
S,
as
a
lo
ca
l
o
p
tim
izer
,
ad
a
p
ts
th
e
co
v
a
r
ian
ce
m
atr
ix
o
f
a
m
u
ltiv
ar
iate
n
o
r
m
al
d
is
tr
ib
u
tio
n
to
g
u
i
d
e
th
e
g
en
er
atio
n
o
f
n
ew
ca
n
d
id
ate
s
o
lu
tio
n
s
[
2
5
]
.
I
t
ex
ce
ls
in
f
in
e
-
tu
n
in
g
s
o
lu
tio
n
s
b
y
ex
p
lo
itin
g
th
e
s
ea
r
ch
s
p
ac
e
ar
o
u
n
d
p
r
o
m
is
in
g
r
eg
io
n
s
,
lead
i
n
g
t
o
f
aster
co
n
v
er
g
en
ce
an
d
im
p
r
o
v
ed
p
r
ec
is
io
n
.
B
y
em
b
e
d
d
in
g
C
MA
E
S
in
to
AO’
s
iter
atio
n
cy
cle,
th
e
h
y
b
r
id
alg
o
r
ith
m
b
e
n
ef
its
f
r
o
m
b
o
th
th
e
ex
p
lo
r
atio
n
ab
ilit
y
o
f
AO
an
d
th
e
ex
p
lo
itatio
n
ef
f
icien
cy
o
f
C
MA
E
S.
T
h
is
in
teg
r
atio
n
h
el
p
s
in
escap
in
g
lo
ca
l
o
p
tim
a
a
n
d
ac
h
ie
v
in
g
g
lo
b
al
o
p
tim
a
m
o
r
e
co
n
s
is
ten
tly
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
P
o
w
er smo
o
th
in
g
in
elec
tr
ica
l d
is
tr
ib
u
tio
n
s
ystem
u
s
in
g
co
va
r
ia
n
ce
ma
tr
ix
…
(
S
mru
tir
ek
h
a
Ma
h
a
n
t
a
)
847
q
u
ick
ly
[
2
6
]
.
I
n
AOCMAE
S,
th
e
o
p
tim
izatio
n
b
eg
i
n
s
with
AO’
s
g
lo
b
al
s
ea
r
ch
p
h
ase,
wh
er
e
d
iv
er
s
e
ca
n
d
id
ate
s
o
lu
tio
n
s
ex
p
lo
r
e
th
e
wid
er
s
ea
r
ch
s
p
ac
e.
W
h
en
th
e
alg
o
r
i
th
m
id
en
tifie
s
a
p
r
o
m
is
in
g
r
e
g
io
n
o
r
b
eg
in
s
to
s
tag
n
ate,
th
e
C
MA
E
S
m
o
d
u
le
is
tr
ig
g
er
ed
t
o
p
er
f
o
r
m
an
in
t
en
s
iv
e
lo
ca
l
s
ea
r
ch
ar
o
u
n
d
th
e
b
est
s
o
lu
tio
n
.
T
h
is
d
y
n
am
ic
s
witch
in
g
e
n
s
u
r
es
th
a
t
th
e
ex
p
lo
r
atio
n
is
n
o
t
p
r
em
at
u
r
ely
s
to
p
p
e
d
an
d
th
at
th
e
alg
o
r
ith
m
ca
n
f
in
e
-
tu
n
e
o
p
tim
al
s
o
lu
tio
n
s
ef
f
ec
tiv
ely
.
T
h
e
b
ala
n
ce
b
etwe
en
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
is
t
h
er
eb
y
s
ig
n
if
ica
n
tly
en
h
an
ce
d
[
2
7
]
.
Ov
e
r
all,
th
e
AOCMAE
S
h
y
b
r
id
alg
o
r
ith
m
d
eliv
er
s
s
u
p
er
i
o
r
o
p
tim
izatio
n
p
e
r
f
o
r
m
an
ce
b
y
co
m
b
in
in
g
th
e
g
lo
b
al
r
ea
ch
o
f
AO
an
d
th
e
lo
ca
l
p
r
ec
is
io
n
o
f
C
MA
E
S.
T
h
e
r
esu
lt
is
an
im
p
r
o
v
ed
co
n
v
er
g
en
c
e
r
ate,
r
ed
u
ce
d
c
o
m
p
u
tatio
n
ti
m
e,
an
d
en
h
a
n
ce
d
r
o
b
u
s
tn
ess
in
n
av
ig
atin
g
co
m
p
le
x
s
ea
r
ch
s
p
ac
es.
T
h
e
o
p
tim
izatio
n
p
ar
a
m
eter
s
ar
e
a
d
ap
tiv
ely
tu
n
ed
d
u
r
in
g
th
e
r
u
n
to
en
s
u
r
e
t
h
at
th
e
alg
o
r
ith
m
ap
p
r
o
ac
h
es
o
p
tim
al
s
o
lu
tio
n
s
with
in
a
s
h
o
r
ter
in
ter
v
al,
m
ak
in
g
it
s
u
itab
le
f
o
r
r
ea
l
-
wo
r
ld
en
g
in
ee
r
i
n
g
an
d
co
m
p
u
t
atio
n
al
ap
p
licatio
n
s
r
eq
u
ir
in
g
h
ig
h
ef
f
icien
c
y
an
d
ac
cu
r
ac
y
[
2
8
]
.
Fig
u
r
e
4
illu
s
tr
ates
th
e
n
u
m
er
ical
r
ep
r
esen
tatio
n
o
f
k
e
y
o
p
tim
izatio
n
p
a
r
am
eter
s
u
s
ed
in
th
e
C
MA
E
SA
O
tech
n
iq
u
e
th
r
o
u
g
h
a
b
ar
g
r
ap
h
.
T
h
e
g
r
a
p
h
s
h
o
ws
th
at
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
is
s
et
to
1
0
0
0
,
h
ig
h
lig
h
tin
g
th
e
alg
o
r
it
h
m
'
s
ca
p
ac
ity
f
o
r
e
x
ten
s
iv
e
s
e
ar
ch
in
g
[
2
9
]
.
B
o
th
t
h
e
a
lp
h
a
(
α
)
an
d
d
elta
(
δ)
p
ar
am
eter
s
,
wh
ich
co
n
tr
o
l
th
e
ex
p
lo
r
atio
n
a
n
d
ex
p
l
o
itatio
n
b
alan
ce
,
ar
e
s
et
to
th
eir
av
er
ag
e
v
alu
es
o
f
0
.
5
with
in
th
e
r
an
g
e
[
0
.
1
,
0
.
9
]
.
T
h
e
p
r
o
b
l
em
d
im
en
s
io
n
ality
is
r
elativ
ely
lo
w
at
4
,
in
d
icatin
g
a
m
o
d
e
r
a
te
co
m
p
lex
ity
o
f
th
e
s
ea
r
ch
s
p
ac
e.
L
astl
y
,
th
e
n
u
m
b
er
o
f
a
q
u
ilas
(
ca
n
d
id
ate
s
o
lu
t
io
n
s
)
is
1
0
0
,
r
ef
lectin
g
a
r
o
b
u
s
t
p
o
p
u
latio
n
s
ize
t
o
s
u
p
p
o
r
t d
iv
er
s
e
g
lo
b
al
s
ea
r
ch
ca
p
ab
ilit
ies.
T
h
is
v
is
u
aliza
tio
n
h
elp
s
in
u
n
d
er
s
tan
d
in
g
th
e
s
ca
le
an
d
r
o
le
o
f
ea
ch
p
ar
am
eter
in
t
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
[
3
0
]
.
Fig
u
r
e
4
.
Par
am
eter
s
f
o
r
C
MA
E
SAO
Fig
u
r
e
5
p
r
esen
ts
th
e
f
l
o
wch
a
r
t
f
o
r
th
e
co
v
ar
ian
ce
m
atr
ix
a
d
ap
tatio
n
e
v
o
lu
tio
n
s
tr
ateg
y
-
b
ased
aq
u
ila
o
p
tim
izatio
n
(
C
MA
E
SAO)
tec
h
n
iq
u
e,
d
ep
ictin
g
th
e
in
teg
r
atio
n
o
f
AO
an
d
C
MA
E
S
f
o
r
en
h
an
ce
d
o
p
tim
izatio
n
p
er
f
o
r
m
an
ce
.
T
h
e
p
r
o
ce
s
s
in
itiates
with
th
e
in
p
u
t
o
f
alg
o
r
i
th
m
-
s
p
ec
if
ic
p
ar
am
eter
s
s
u
ch
as
p
o
p
u
latio
n
s
ize
(
n
u
m
b
e
r
o
f
a
q
u
ilas
)
,
d
im
en
s
io
n
o
f
th
e
p
r
o
b
lem
s
p
ac
e,
a
l
p
h
a
(
α
)
,
d
elta
(
δ)
,
an
d
th
e
m
a
x
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
.
An
in
itial
p
o
p
u
latio
n
o
f
ca
n
d
id
ate
s
o
lu
tio
n
s
is
g
e
n
er
ated
r
an
d
o
m
ly
with
in
th
e
d
ef
in
ed
b
o
u
n
d
s
,
an
d
ea
ch
s
o
lu
tio
n
'
s
f
itn
ess
is
ev
al
u
ated
u
s
in
g
a
p
r
e
d
ef
in
e
d
o
b
je
ctiv
e
f
u
n
ctio
n
.
T
h
e
b
est
s
o
lu
ti
o
n
is
i
d
en
tifie
d
an
d
s
to
r
ed
f
o
r
f
u
tu
r
e
r
e
f
er
en
ce
[
3
1
]
.
Fo
llo
win
g
in
itializatio
n
,
th
e
m
ain
o
p
tim
izatio
n
lo
o
p
b
eg
i
n
s
,
wh
er
e
th
e
aq
u
ila
o
p
tim
izatio
n
(
AO)
s
tr
at
eg
ies
ar
e
ap
p
lied
to
g
u
id
e
g
lo
b
al
s
ea
r
ch
.
T
h
ese
s
tr
ateg
ies
m
im
ic
d
if
f
er
en
t
h
u
n
tin
g
b
eh
av
io
r
s
o
f
Aq
u
ilas
an
d
allo
w
ca
n
d
id
ates
to
ex
p
lo
r
e
d
iv
er
s
e
r
eg
io
n
s
o
f
th
e
s
o
lu
tio
n
s
p
ac
e.
B
ased
o
n
a
s
elec
tio
n
m
ec
h
an
is
m
in
f
lu
e
n
ce
d
b
y
α
a
n
d
δ,
th
e
alg
o
r
ith
m
d
ec
id
es
wh
eth
er
to
p
e
r
f
o
r
m
ex
p
l
o
r
ati
o
n
o
r
m
o
v
e
to
war
d
s
ex
p
lo
itatio
n
.
W
h
en
s
tag
n
atio
n
o
r
p
r
o
m
is
in
g
r
eg
i
o
n
s
ar
e
d
et
ec
ted
,
th
e
C
MA
E
S
m
o
d
u
le
is
tr
ig
g
er
e
d
.
C
MA
E
S
th
en
co
n
d
u
cts
a
f
o
cu
s
ed
lo
ca
l
s
ea
r
ch
u
s
in
g
ad
ap
tiv
e
m
u
ltiv
ar
iate
n
o
r
m
al
s
am
p
lin
g
,
u
p
d
a
tin
g
th
e
m
ea
n
an
d
co
v
ar
ian
ce
m
atr
ix
to
r
e
f
in
e
th
e
s
o
lu
tio
n
ar
o
u
n
d
th
e
b
est
ca
n
d
id
ate.
T
h
is
h
y
b
r
id
p
r
o
ce
s
s
co
n
tin
u
es
iter
ativ
ely
,
with
th
e
alg
o
r
ith
m
alter
n
atin
g
b
etwe
en
AO’
s
g
lo
b
al
s
ea
r
ch
an
d
C
MA
E
S’s
lo
ca
l
r
e
f
in
em
en
t
b
ased
o
n
co
n
v
er
g
en
ce
b
e
h
av
io
r
[
3
2
]
.
At
ea
ch
s
tep
,
th
e
p
o
p
u
latio
n
i
s
u
p
d
ated
,
an
d
th
e
f
itn
ess
o
f
n
ew
ca
n
d
id
ates
is
co
m
p
ar
ed
to
id
e
n
tify
im
p
r
o
v
e
m
en
ts
.
T
h
e
lo
o
p
ter
m
in
ates
wh
en
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
i
ter
atio
n
s
is
r
ea
ch
ed
o
r
an
ac
ce
p
tab
le
s
o
lu
tio
n
is
f
o
u
n
d
.
T
h
e
f
lo
wch
ar
t
in
Fig
u
r
e
4
clea
r
ly
o
u
tlin
es
th
e
i
n
tellig
en
t
s
witch
in
g
an
d
co
o
p
er
atio
n
b
etwe
en
AO
an
d
C
MA
E
S,
il
lu
s
tr
atin
g
h
o
w
th
eir
s
y
n
er
g
y
r
esu
lts
in
im
p
r
o
v
ed
co
n
v
er
g
en
ce
s
p
ee
d
,
r
ed
u
ce
d
c
o
m
p
u
tatio
n
al
ef
f
o
r
t,
an
d
m
o
r
e
r
eliab
le
g
l
o
b
al
o
p
tim
izatio
n
o
u
tco
m
es [
3
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
14
,
No
.
4
,
Dec
em
b
er
20
25
:
8
4
2
-
858
848
Fig
u
r
e
5
.
C
MA
E
SAO
tech
n
iq
u
e
f
lo
wch
ar
t a
l
g
o
r
ith
m
6.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
C
MA
E
SAO
tech
n
iq
u
e
h
as
b
ee
n
r
ig
o
r
o
u
s
ly
e
v
alu
at
ed
u
s
in
g
a
co
m
p
r
eh
e
n
s
iv
e
s
et
o
f
b
en
ch
m
ar
k
f
u
n
ctio
n
s
.
T
ab
le
1
p
r
esen
t
s
th
e
d
etails
o
f
th
ese
b
en
ch
m
ar
k
f
u
n
ctio
n
s
,
wh
ich
in
clu
d
e
a
d
iv
er
s
e
r
an
g
e
o
f
m
ath
em
atica
l
test
p
r
o
b
lem
s
co
m
m
o
n
l
y
u
s
ed
in
o
p
tim
izat
io
n
r
esear
ch
.
T
h
ese
f
u
n
ctio
n
s
v
a
r
y
i
n
co
m
p
lex
ity
,
m
o
d
ality
(
u
n
im
o
d
al
o
r
m
u
ltimo
d
al)
,
d
im
en
s
io
n
ality
,
an
d
lan
d
s
ca
p
e
ch
a
r
ac
ter
is
tics
,
m
ak
in
g
th
e
m
i
d
ea
l
f
o
r
ass
ess
in
g
th
e
r
o
b
u
s
tn
ess
an
d
v
er
s
atility
o
f
t
h
e
o
p
tim
izatio
n
alg
o
r
ith
m
.
T
h
ey
test
a
n
o
p
tim
izer
’
s
ab
ilit
y
to
lo
ca
te
g
lo
b
al
o
p
tim
a,
esc
ap
e
lo
ca
l
m
in
im
a,
a
n
d
co
n
v
er
g
e
ef
f
icien
tly
ac
r
o
s
s
d
if
f
er
en
t
s
ea
r
ch
lan
d
s
ca
p
es.
I
n
th
is
s
tu
d
y
,
a
to
tal
o
f
2
3
b
en
ch
m
ar
k
f
u
n
ctio
n
s
,
as
o
u
tlin
ed
in
T
ab
le
1
h
av
e
b
ee
n
em
p
lo
y
ed
to
v
alid
ate
th
e
e
f
f
ec
tiv
en
ess
o
f
t
h
e
C
MA
E
SAO
h
y
b
r
id
tec
h
n
iq
u
e.
T
h
ese
i
n
clu
d
e
well
-
k
n
o
wn
f
u
n
ctio
n
s
s
u
ch
as
Sp
h
er
e,
R
astrig
in
,
Ack
ley
,
Gr
iewa
n
k
,
an
d
R
o
s
en
b
r
o
ck
,
am
o
n
g
o
th
er
s
.
B
y
co
m
p
ar
in
g
th
e
r
esu
lts
o
f
C
MA
E
SAO
with
s
ta
n
d
ar
d
o
p
tim
izatio
n
a
lg
o
r
ith
m
s
,
th
e
s
tu
d
y
d
em
o
n
s
tr
ates
its
s
u
p
er
io
r
co
n
v
er
g
e
n
ce
r
ate,
h
ig
h
er
s
o
lu
tio
n
ac
cu
r
ac
y
,
an
d
s
tr
o
n
g
er
b
alan
ce
b
etwe
en
ex
p
lo
r
atio
n
a
n
d
ex
p
lo
itatio
n
.
T
h
e
wid
e
v
ar
iety
o
f
b
en
ch
m
ar
k
f
u
n
ctio
n
s
en
s
u
r
es
th
at
th
e
p
e
r
f
o
r
m
an
ce
e
v
alu
a
tio
n
is
co
m
p
r
eh
e
n
s
iv
e
an
d
r
ea
li
s
tic,
u
ltima
tely
p
r
o
v
in
g
th
e
h
y
b
r
i
d
alg
o
r
ith
m
’
s
ca
p
ab
ilit
y
to
h
an
d
le
d
iv
er
s
e
an
d
co
m
p
lex
o
p
tim
izatio
n
task
s
ef
f
icien
tly
.
T
ab
le
1
o
u
tlin
es
th
e
c
o
m
p
r
eh
e
n
s
iv
e
s
et
o
f
b
en
ch
m
a
r
k
f
u
n
cti
o
n
s
u
s
ed
to
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
C
MA
E
SAO
alg
o
r
ith
m
.
T
h
ese
f
u
n
ctio
n
s
s
p
an
a
wid
e
r
an
g
e
o
f
o
p
tim
izatio
n
la
n
d
s
ca
p
es,
in
clu
d
in
g
u
n
im
o
d
al
a
n
d
m
u
ltimo
d
al
f
u
n
ctio
n
s
,
m
ak
in
g
th
em
s
u
itab
l
e
f
o
r
ass
ess
in
g
b
o
th
ex
p
lo
r
ati
o
n
an
d
ex
p
lo
itatio
n
ca
p
ab
ilit
ies.
E
ac
h
f
u
n
ctio
n
is
test
ed
in
a
s
p
ec
if
ied
d
im
en
s
io
n
al
s
p
ac
e
—
m
o
s
tly
3
0
d
im
en
s
io
n
s
,
ex
ce
p
t
f
o
r
a
f
e
w
lo
wer
-
d
im
en
s
io
n
al
f
u
n
ctio
n
s
(
e.
g
.
,
2
D,
3
D,
4
D,
a
n
d
6
D)
to
r
ef
lect
r
ea
l
-
wo
r
ld
p
r
o
b
lem
s
with
v
a
r
y
i
n
g
co
m
p
lex
ity
.
T
h
e
f
u
n
ctio
n
s
o
p
er
ate
with
in
d
ef
in
ed
in
p
u
t
r
a
n
g
es,
s
u
ch
as
[
−1
0
0
,
1
0
0
]
[
-
1
0
0
,
1
0
0
]
[
−1
0
0
,
1
0
0
]
,
[
−6
0
0
,
6
0
0
]
[
-
6
0
0
,
6
0
0
]
[
−6
0
0
,
6
0
0
]
,
o
r
[
0
,
1
]
[
0
,
1
]
[
0
,
1
]
,
w
h
ich
in
f
lu
en
ce
th
e
d
if
f
icu
lty
lev
el
o
f
lo
ca
tin
g
g
lo
b
al
o
p
tim
a.
T
h
ese
b
en
c
h
m
ar
k
f
u
n
ctio
n
s
in
clu
d
e
wid
ely
s
tu
d
ied
m
ath
em
atica
l
f
o
r
m
u
latio
n
s
s
u
ch
as
Sp
h
er
e,
R
astrig
in
,
Ack
ley
,
R
o
s
en
b
r
o
c
k
,
an
d
G
r
iewa
n
k
f
u
n
ctio
n
s
,
a
m
o
n
g
o
th
er
s
.
T
h
e
y
ar
e
k
n
o
w
n
f
o
r
t
h
eir
v
a
r
ied
ch
ar
ac
ter
is
tics
lik
e
h
ig
h
n
o
n
-
l
in
ea
r
ity
,
r
u
g
g
ed
n
ess
,
d
ec
ep
tiv
e
lo
ca
l
m
in
im
a,
an
d
s
h
ar
p
v
al
ley
s
.
B
y
in
clu
d
in
g
s
u
ch
a
d
iv
er
s
e
s
et,
th
e
ev
alu
atio
n
test
s
h
o
w
ef
f
icien
tly
C
MA
E
SAO
n
av
ig
ates
th
r
o
u
g
h
d
if
f
er
en
t
ty
p
es
o
f
o
b
jectiv
e
s
u
r
f
ac
es.
T
h
is
r
an
g
e
o
f
f
u
n
ctio
n
s
en
s
u
r
es
th
at
th
e
alg
o
r
ith
m
is
n
o
t
b
iased
to
war
d
s
p
ec
if
ic
p
r
o
b
lem
ty
p
es
an
d
m
ain
tain
s
g
en
er
aliz
ab
ilit
y
ac
r
o
s
s
o
p
tim
izatio
n
s
ce
n
ar
io
s
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
C
MA
E
SAO
h
as
b
ee
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
P
o
w
er smo
o
th
in
g
in
elec
tr
ica
l d
is
tr
ib
u
tio
n
s
ystem
u
s
in
g
co
va
r
ia
n
ce
ma
tr
ix
…
(
S
mru
tir
ek
h
a
Ma
h
a
n
t
a
)
849
f
u
r
th
er
co
m
p
ar
ed
ag
ain
s
t
a
b
r
o
ad
ar
r
a
y
o
f
estab
lis
h
ed
m
etah
eu
r
is
tic
alg
o
r
ith
m
s
in
cl
u
d
in
g
g
r
ass
h
o
p
p
er
o
p
tim
izatio
n
alg
o
r
ith
m
(
GOA)
,
eq
u
ilib
r
iu
m
o
p
tim
izatio
n
(
E
O)
,
p
ar
ticle
s
war
m
o
p
tim
izati
o
n
(
PS
O)
,
d
r
ag
o
n
f
ly
alg
o
r
ith
m
(
DA)
,
an
t
lio
n
o
p
ti
m
izatio
n
(
AL
O)
,
g
r
ey
wo
lf
o
p
tim
izer
(
GW
O)
,
m
ar
in
e
p
r
ed
at
o
r
alg
o
r
ith
m
(
MPA)
,
s
alp
s
war
m
alg
o
r
ith
m
(
SS
A)
,
s
in
e
co
s
in
e
alg
o
r
ith
m
(
SC
A)
,
wh
ale
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
OA)
,
an
d
s
lim
e
m
o
u
ld
al
g
o
r
ith
m
(
SMA)
.
E
ac
h
o
f
th
ese
alg
o
r
ith
m
s
h
as
p
r
o
v
en
ef
f
ec
tiv
e
in
d
if
f
er
e
n
t
s
tu
d
ies,
y
et
th
e
h
y
b
r
id
C
MA
E
SA
O
h
as d
em
o
n
s
tr
ated
en
h
an
ce
d
r
o
b
u
s
tn
ess
an
d
co
n
v
er
g
en
ce
c
o
n
s
is
ten
cy
ac
r
o
s
s
al
l b
en
ch
m
ar
k
ca
s
es.
T
ab
le
2
p
r
esen
ts
th
e
d
etailed
p
er
f
o
r
m
an
ce
co
m
p
ar
is
o
n
b
et
wee
n
th
e
s
tan
d
alo
n
e
aq
u
ila
o
p
tim
izatio
n
(
AO)
an
d
th
e
h
y
b
r
id
C
MA
E
SAO
alg
o
r
ith
m
u
s
in
g
v
ar
io
u
s
b
en
ch
m
ar
k
f
u
n
ctio
n
s
.
Fo
r
ea
c
h
f
u
n
ctio
n
,
m
etr
ics
s
u
ch
as
m
ea
n
,
s
tan
d
ar
d
d
e
v
i
atio
n
,
b
est,
a
n
d
wo
r
s
t
v
alu
es
wer
e
co
m
p
u
ted
ac
r
o
s
s
m
u
ltip
le
r
u
n
s
t
o
ass
ess
co
n
s
is
ten
cy
,
ac
cu
r
ac
y
,
an
d
r
o
b
u
s
tn
ess
.
T
h
e
f
ir
s
t
m
aj
o
r
o
b
s
er
v
atio
n
is
th
e
s
u
b
s
tan
tial
im
p
r
o
v
e
m
en
t
i
n
co
n
v
er
g
en
ce
p
r
ec
is
io
n
ac
h
iev
ed
b
y
C
MA
E
SAO.
Fo
r
ex
a
m
p
le,
in
f
u
n
ctio
n
s
wh
er
e
A
O
alr
ea
d
y
p
r
o
d
u
ce
d
ex
tr
em
ely
s
m
all
m
e
an
v
alu
es
(
in
th
e
o
r
d
er
o
f
1
0
−
218
,
C
MA
E
SAO
p
u
s
h
ed
p
er
f
o
r
m
an
ce
e
v
en
f
u
r
th
er
in
to
t
h
e
r
an
g
e
o
f
1
0
−
266
,
s
h
o
wca
s
in
g
it
s
s
u
p
er
io
r
ab
ilit
y
to
ap
p
r
o
ac
h
g
lo
b
al
o
p
tim
a
with
h
ig
h
er
p
r
e
cisi
o
n
an
d
s
tab
ilit
y
.
T
h
e
co
m
p
ar
is
o
n
also
h
ig
h
lig
h
ts
C
MA
E
SAO
's
r
em
ar
k
ab
le
ad
v
an
ta
g
e
in
s
tan
d
ar
d
d
ev
ia
tio
n
v
alu
es,
w
h
ich
r
em
ain
s
ig
n
if
ican
tly
lo
wer
o
r
ev
en
ze
r
o
in
m
an
y
ca
s
es,
in
d
ica
tin
g
h
ig
h
co
n
s
is
ten
cy
an
d
m
in
im
al
v
ar
iatio
n
in
o
u
tco
m
es.
T
h
is
is
esp
ec
ially
e
v
id
en
t
in
f
u
n
ctio
n
s
in
v
o
lv
in
g
m
u
ltimo
d
al
lan
d
s
ca
p
es,
wh
er
e
AO
ten
d
s
to
s
h
o
w
n
o
ti
ce
ab
le
v
a
r
ian
ce
b
etwe
en
b
est
an
d
wo
r
s
t
ca
s
es.
I
n
co
n
tr
ast,
C
MA
E
SAO
m
ain
tain
s
tig
h
t
p
er
f
o
r
m
an
ce
b
o
u
n
d
s
,
p
r
o
v
in
g
its
r
o
b
u
s
tn
ess
.
Fo
r
ex
am
p
le,
in
o
n
e
o
f
th
e
b
en
ch
m
ar
k
f
u
n
ctio
n
s
,
AO’
s
wo
r
s
t
-
ca
s
e
r
esu
lt
is
s
ev
er
al
m
ag
n
itu
d
es
h
ig
h
er
t
h
an
its
b
est,
wh
ile
C
MA
E
S
AO
r
em
ain
s
tig
h
tly
clu
s
ter
e
d
ar
o
u
n
d
o
p
tim
al
p
er
f
o
r
m
an
ce
with
n
eg
lig
ib
le
s
tan
d
ar
d
d
e
v
iatio
n
.
T
h
e
C
MA
E
SAO
o
u
tp
er
f
o
r
m
s
AO
in
b
est
an
d
wo
r
s
t
-
ca
s
e
s
ce
n
ar
io
s
,
in
d
icatin
g
b
etter
r
eliab
ilit
y
in
ex
tr
e
m
e
o
u
tco
m
es.
Fo
r
in
s
tan
ce
,
in
s
ev
er
al
b
en
ch
m
ar
k
ca
s
es,
C
MA
E
SA
O’
s
wo
r
s
t
p
er
f
o
r
m
a
n
ce
is
s
till
s
ig
n
if
ican
tly
b
etter
th
an
AO’
s
b
est.
T
h
is
illu
s
tr
ate
s
th
e
h
y
b
r
id
m
o
d
el'
s
ca
p
ab
ilit
y
to
av
o
id
p
o
o
r
lo
ca
l
o
p
tim
a
an
d
n
av
ig
ate
co
m
p
lex
s
ea
r
ch
s
p
ac
es
m
o
r
e
ef
f
ec
tiv
ely
.
T
h
e
in
clu
s
io
n
o
f
C
MA
E
S
en
h
an
ce
s
lo
ca
l
ex
p
lo
itatio
n
,
f
in
e
-
tu
n
in
g
th
e
s
o
lu
tio
n
s
id
en
tifie
d
b
y
AO’
s
b
r
o
ad
e
r
g
lo
b
al
ex
p
lo
r
ati
o
n
m
ec
h
an
is
m
s
.
Ov
er
all,
th
e
r
esu
lts
in
T
ab
le
2
v
alid
ate
th
e
e
f
f
ec
tiv
en
ess
o
f
th
e
C
MA
E
SAO
h
y
b
r
id
a
p
p
r
o
ac
h
ac
r
o
s
s
d
iv
er
s
e
b
en
ch
m
ar
k
f
u
n
ctio
n
s
.
I
t
co
n
s
is
ten
tly
s
u
r
p
ass
es
AO
in
all
s
tatis
t
ical
m
etr
ic
s
,
in
clu
d
in
g
m
ea
n
ac
cu
r
ac
y
,
v
ar
ia
n
ce
co
n
tr
o
l,
an
d
ex
tr
e
m
u
m
p
er
f
o
r
m
a
n
ce
.
T
h
e
in
teg
r
atio
n
o
f
C
MA
E
S
en
s
u
r
es
n
o
t
o
n
ly
f
aster
co
n
v
er
g
en
ce
b
u
t
also
a
m
o
r
e
p
r
ec
is
e
an
d
r
eliab
le
o
p
tim
izat
io
n
p
r
o
ce
s
s
.
T
h
is
p
er
f
o
r
m
an
c
e
g
ain
co
n
f
ir
m
s
th
e
h
y
b
r
id
alg
o
r
ith
m
’
s
s
u
itab
ilit
y
f
o
r
s
o
lv
in
g
h
ig
h
-
d
im
e
n
s
io
n
a
l
an
d
c
o
m
p
u
tatio
n
ally
c
h
allen
g
in
g
o
p
tim
izatio
n
p
r
o
b
lem
s
in
r
ea
l
-
wo
r
ld
a
p
p
lic
atio
n
s
.
6
.
1
.
P
er
f
o
r
m
a
nce
ind
ex
us
in
g
s
t
a
t
is
t
ica
l a
na
ly
s
is
T
h
e
s
tatis
tical
an
aly
s
i
s
o
f
th
e
p
er
f
o
r
m
an
ce
in
d
e
x
h
ig
h
lig
h
ts
th
e
en
h
an
ce
d
ef
f
icien
cy
o
f
th
e
p
r
o
p
o
s
ed
C
MA
E
SA
O
alg
o
r
ith
m
b
y
d
i
r
ec
tly
co
m
p
ar
in
g
it
with
th
e
o
r
ig
in
al
AO
u
n
d
er
i
d
en
tica
l
co
n
d
itio
n
s
.
B
o
th
alg
o
r
ith
m
s
wer
e
s
u
b
jecte
d
to
th
e
s
am
e
f
itn
ess
f
u
n
ctio
n
f
(
x
)
an
d
ev
al
u
ated
u
s
in
g
id
e
n
tical
o
p
tim
izatio
n
p
ar
am
eter
s
,
en
s
u
r
in
g
a
f
air
an
d
co
n
s
is
ten
t
co
m
p
ar
is
o
n
.
T
h
e
an
aly
s
is
f
o
cu
s
ed
o
n
k
ey
p
er
f
o
r
m
an
ce
in
d
icato
r
s
s
u
ch
as
th
e
in
teg
r
al
o
f
tim
e
-
we
ig
h
ted
ab
s
o
lu
te
er
r
o
r
(
I
T
AE
)
,
wh
ich
is
wid
ely
u
s
ed
t
o
ass
ess
co
n
v
er
g
en
ce
q
u
ality
an
d
co
n
t
r
o
l
p
r
ec
is
io
n
.
As
s
ee
n
in
Fig
u
r
e
5
,
C
MA
E
SAO
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
s
AO
b
y
ac
h
iev
in
g
s
ig
n
if
ican
tly
lo
wer
I
T
AE
v
alu
es,
in
d
icatin
g
f
aster
an
d
m
o
r
e
ac
cu
r
ate
co
n
v
er
g
en
ce
to
war
d
o
p
tim
al
s
o
lu
tio
n
s
.
Mo
r
eo
v
er
,
th
e
r
esu
lts
r
ev
ea
l
th
at
C
MA
E
SAO
ex
h
ib
its
le
s
s
v
ar
iatio
n
in
p
e
r
f
o
r
m
an
ce
ac
r
o
s
s
m
u
ltip
le
r
u
n
s
,
r
e
f
lecte
d
b
y
its
r
ed
u
ce
d
s
tan
d
ar
d
d
ev
iatio
n
.
T
h
is
co
n
s
is
ten
cy
in
d
icate
s
th
e
h
y
b
r
id
alg
o
r
ith
m
’
s
r
o
b
u
s
tn
ess
an
d
r
eliab
ilit
y
in
h
an
d
lin
g
c
o
m
p
lex
o
p
tim
izatio
n
lan
d
s
ca
p
es.
T
h
e
in
teg
r
atio
n
o
f
C
MA
E
S
in
to
th
e
AO
f
r
am
ewo
r
k
im
p
r
o
v
es
lo
ca
l
ex
p
lo
itatio
n
,
en
ab
lin
g
p
r
ec
is
e
f
in
e
-
tu
n
in
g
o
f
s
o
lu
tio
n
s
af
ter
in
itial
g
lo
b
al
ex
p
lo
r
atio
n
.
As a
r
esu
lt,
C
MA
E
SAO
n
o
t o
n
l
y
r
ea
c
h
es o
p
tim
al
s
o
lu
tio
n
s
m
o
r
e
ef
f
icien
tl
y
b
u
t a
ls
o
d
o
es so
with
g
r
ea
ter
r
ep
ea
ta
b
ilit
y
an
d
m
in
i
m
al
d
ev
iatio
n
,
v
alid
atin
g
its
s
tatis
tical
s
u
p
er
io
r
ity
o
v
er
s
tan
d
alo
n
e
AO.
Fig
u
r
e
6
p
r
esen
ts
a
p
lo
t
o
f
th
e
in
teg
r
al
o
f
tim
e
-
weig
h
ted
a
b
s
o
lu
te
er
r
o
r
(
I
T
AE
)
v
alu
es
v
er
s
u
s
th
e
n
u
m
b
er
o
f
r
u
n
s
f
o
r
b
o
th
th
e
A
O
an
d
C
MA
E
SAO
alg
o
r
ith
m
s
,
clea
r
ly
d
em
o
n
s
tr
atin
g
th
e
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
o
f
C
MA
E
SAO.
T
h
e
b
lu
e
lin
e
r
ep
r
esen
tin
g
AO
s
h
o
ws
h
ig
h
er
I
T
AE
v
alu
es
with
s
ig
n
if
ican
t
f
lu
c
tu
atio
n
s
ac
r
o
s
s
th
e
3
0
r
u
n
s
,
in
d
icatin
g
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ates
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I
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I
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