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[
1
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.
I
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id
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[
2
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
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Sci
,
Vo
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39
,
No
.
1
,
J
u
ly
20
25
:
45
-
61
46
Ma
n
y
tech
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iq
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es
h
av
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b
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d
ev
elo
p
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r
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lead
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[
3
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p
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p
tim
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s
tr
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co
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s
id
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s
f
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co
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an
d
aim
s
to
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as
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tech
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[
4
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T
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[
5
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[
6
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er
en
ce
d
in
[
7
]
,
an
d
th
e
n
o
n
-
lin
ea
r
m
ix
ed
-
in
teg
er
win
d
d
r
iv
en
o
p
tim
izatio
n
(
W
DO)
me
n
tio
n
ed
in
[
8
]
,
h
av
e
b
ee
n
ap
p
lied
to
m
an
ag
e
f
le
x
ib
le
an
d
tim
e
-
s
h
if
tab
le
h
o
m
e
d
e
v
ic
es.
A
s
tu
d
y
in
[
9
]
co
m
p
ar
es
th
e
h
a
r
m
o
n
y
s
ea
r
c
h
alg
o
r
ith
m
(
HSA)
with
t
h
e
f
ir
ef
ly
alg
o
r
ith
m
(
FA)
,
f
in
d
in
g
th
at
th
e
FA
is
s
u
p
er
io
r
f
o
r
r
e
d
u
cin
g
th
e
p
e
ak
-
to
-
av
e
r
ag
e
r
atio
(
PAR
)
w
h
ile
th
e
HSA
ex
ce
ls
in
co
s
t
-
ef
f
ec
tiv
en
ess
.
T
h
e
u
s
ef
u
ln
ess
o
f
th
is
s
tu
d
y
co
u
ld
b
e
en
h
an
ce
d
b
y
in
cl
u
d
in
g
d
ata
o
n
h
o
w
q
u
ick
ly
ea
ch
al
g
o
r
ith
m
co
n
v
er
g
es.
T
h
is
p
ap
er
[
1
0
]
in
t
r
o
d
u
ce
s
a
DSM
f
r
am
ewo
r
k
th
at
u
tili
ze
s
th
e
an
t
co
lo
n
y
o
p
tim
izatio
n
(
AC
O)
ap
p
r
o
ac
h
with
in
a
s
m
ar
t
g
r
id
co
n
tex
t.
No
n
eth
e
less
,
th
e
AC
O
m
eth
o
d
in
itially
f
ac
ed
is
s
u
es
with
ea
r
ly
co
n
v
er
g
en
ce
.
T
h
e
c
o
n
v
e
r
g
e
n
c
e
p
r
o
c
e
s
s
w
a
s
r
e
f
i
n
e
d
b
y
i
n
c
o
r
p
o
r
a
t
i
n
g
a
m
u
t
a
t
i
o
n
m
e
c
h
a
n
i
s
m
i
n
t
o
t
h
e
s
t
a
n
d
a
r
d
A
C
O
a
l
g
o
r
i
t
h
m
[
1
1
]
.
C
o
n
s
eq
u
en
tly
,
th
is
m
o
d
if
ied
a
p
p
r
o
ac
h
is
em
p
lo
y
e
d
to
ac
h
iev
e
co
s
t r
ed
u
ctio
n
s
an
d
lo
wer
t
h
e
PAR
.
T
h
e
im
p
lem
en
tatio
n
o
f
DSM
u
s
in
g
a
g
en
etic
alg
o
r
ith
m
(
G
A)
h
as
b
ee
n
u
s
ed
to
allo
ca
te
r
esid
en
tial
lo
ad
s
.
T
h
e
o
b
jectiv
e
h
er
e
is
to
en
h
a
n
ce
u
s
er
s
atis
f
ac
tio
n
a
n
d
m
in
im
ize
e
n
er
g
y
co
s
ts
s
im
u
ltan
eo
u
s
ly
.
T
h
is
ap
p
r
o
ac
h
in
v
o
lv
es
d
er
iv
in
g
a
co
s
t
-
p
er
-
u
n
it
s
atis
f
ac
tio
n
in
d
ex
,
wh
ich
s
er
v
es
as
a
n
esti
m
ato
r
f
o
r
u
s
e
r
s
atis
f
ac
tio
n
d
u
r
in
g
lo
ad
s
h
if
t
in
g
[
1
2
]
.
L
o
a
d
s
h
if
tin
g
DSM
was
ap
p
lied
t
o
tr
a
d
itio
n
a
l,
s
m
ar
t,
an
d
s
o
lar
p
h
o
to
v
o
ltaic
(
PV
)
-
in
teg
r
ated
h
o
m
es
u
s
in
g
b
in
a
r
y
p
ar
ticle
s
wa
r
m
o
p
tim
izatio
n
(
B
PS
O
)
,
GA,
an
d
C
u
ck
o
o
s
ea
r
ch
alg
o
r
ith
m
s
,
r
esu
ltin
g
in
r
ed
u
c
ed
p
ea
k
lo
ad
s
a
n
d
co
s
ts
,
with
th
e
C
u
ck
o
o
s
ea
r
ch
alg
o
r
ith
m
o
u
tp
er
f
o
r
m
in
g
th
e
o
t
h
er
s
[
1
3
]
.
T
h
e
s
tu
d
y
p
r
esen
ted
in
[
1
4
]
u
s
es
th
e
GA
f
o
r
DSM
to
lo
wer
p
ea
k
lo
a
d
s
in
an
in
d
u
s
tr
ial
DC
m
icr
o
-
g
r
id
with
s
o
lar
p
o
wer
a
n
d
b
atter
ies.
Sig
n
if
ican
t
p
ea
k
lo
a
d
an
d
c
o
s
t
r
ed
u
ctio
n
s
wer
e
a
ch
iev
ed
,
b
en
ef
itin
g
v
ar
io
u
s
s
ec
to
r
s
.
A
DSM
s
tr
at
eg
y
u
s
in
g
th
e
m
o
th
f
lam
e
o
p
ti
m
izatio
n
(
MFO)
alg
o
r
ith
m
ef
f
ec
tiv
ely
r
ed
u
ce
d
p
ea
k
lo
a
d
s
in
r
esid
en
tial
a
n
d
co
m
m
er
cial
a
r
ea
s
[
1
5
]
.
B
in
ar
y
g
r
ey
wo
lf
o
p
tim
izatio
n
(B
GW
O
)
alg
o
r
ith
m
o
u
tp
er
f
o
r
m
s
B
PS
O
in
o
p
tim
izin
g
r
esid
en
tial
elec
tr
ical
ap
p
lian
ce
s
,
s
ig
n
if
ican
tly
r
ed
u
cin
g
en
er
g
y
co
s
ts
an
d
lo
wer
in
g
p
ea
k
l
o
ad
s
an
d
PA
R
[
1
6
]
.
A
h
y
b
r
id
GA
-
PS
O
alg
o
r
ith
m
is
h
ar
n
ess
ed
t
o
ef
f
ec
tiv
ely
cu
r
tail
e
n
er
g
y
co
s
ts
th
r
o
u
g
h
th
e
o
p
tim
al
all
o
ca
tio
n
o
f
g
en
er
atio
n
s
an
d
l
o
ad
s
in
a
d
ay
-
ah
ea
d
m
ar
k
et
[
1
7
]
.
N
o
tab
ly
,
PS
O
ex
h
ib
its
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
o
v
er
GA
in
th
is
co
n
tex
t.
I
n
th
e
s
co
p
e
o
f
DSM
tech
n
iq
u
es,
d
iv
er
s
e
s
tr
ateg
ies
in
clu
d
in
g
lo
ad
s
h
if
tin
g
,
p
ea
k
clip
p
in
g
,
v
alley
f
illi
n
g
,
s
tr
ateg
ic
co
n
s
er
v
atio
n
,
an
d
s
tr
ateg
ic
lo
ad
g
r
o
wth
h
av
e
b
ee
n
em
p
l
o
y
ed
to
m
o
d
if
y
co
n
s
u
m
er
lo
ad
b
eh
av
io
r
[
1
8
]
.
T
h
e
c
o
r
e
o
b
jec
tiv
e
is
to
r
ed
u
ce
p
ea
k
en
e
r
g
y
d
em
an
d
b
y
s
h
if
tin
g
it
to
o
f
f
-
p
ea
k
tim
es.
Utilit
ie
s
ca
n
d
ir
ec
tly
c
o
n
tr
o
l
co
n
s
u
m
e
r
lo
ad
s
o
r
in
d
ir
ec
tly
g
u
id
e
co
n
s
u
m
er
s
to
s
elf
-
m
an
ag
e
u
s
ag
e,
with
in
ce
n
tiv
es
f
o
r
co
m
p
lian
ce
an
d
p
e
n
alties
f
o
r
n
o
n
-
c
o
m
p
lian
ce
.
Pric
in
g
s
ch
e
d
u
les
en
co
u
r
ag
e
c
o
n
s
u
m
p
tio
n
ad
ju
s
tm
e
n
t.
Sev
er
al
tech
n
iq
u
es
f
o
r
d
em
an
d
-
s
id
e
m
an
ag
em
en
t
in
s
m
ar
t
g
r
id
s
a
r
e
av
ailab
le
as
d
ep
icted
in
Fig
u
r
e
1
in
cl
u
d
in
g
,
l
o
a
d
s
h
if
tin
g
:
m
o
v
in
g
en
er
g
y
u
s
e
f
r
o
m
p
ea
k
to
o
f
f
-
p
ea
k
tim
es
;
p
ea
k
clip
p
i
n
g
:
c
u
ttin
g
d
o
wn
p
e
ak
en
e
r
g
y
d
em
a
n
d
;
v
alley
f
illi
n
g
:
u
s
in
g
ex
tr
a
en
er
g
y
d
u
r
i
n
g
lo
w
-
d
em
a
n
d
p
er
i
o
d
s
;
lo
ad
b
u
ild
in
g
:
r
esh
ap
in
g
en
er
g
y
u
s
e
to
in
cr
ea
s
e
ef
f
icien
cy
;
s
tr
ateg
ic
co
n
s
er
v
a
tio
n
:
en
co
u
r
ag
in
g
en
er
g
y
-
s
av
in
g
b
eh
a
v
io
r
s
;
an
d
f
lex
ib
le
l
o
ad
m
an
a
g
em
en
t:
wo
r
k
in
g
with
c
o
n
s
u
m
er
s
to
ad
ju
s
t
th
eir
en
er
g
y
u
s
e,
o
f
f
e
r
in
g
in
ce
n
tiv
es
f
o
r
co
o
p
er
ati
o
n
.
T
h
ese
m
eth
o
d
s
en
h
an
ce
g
r
id
r
esil
ien
ce
an
d
e
f
f
icien
cy
.
Ho
we
v
er
,
lo
a
d
s
h
if
tin
g
s
tan
d
s
o
u
t
as
th
e
m
o
s
t
ex
ten
s
iv
ely
ex
p
lo
r
ed
tech
n
iq
u
e
in
ex
is
tin
g
liter
atu
r
e
[
1
9
]
.
T
h
e
s
tu
d
y
’
s
s
im
u
latio
n
s
h
o
ws
th
at
DSM,
as
a
m
in
im
izatio
n
p
r
o
b
lem
u
s
in
g
th
e
ad
ap
tiv
e
m
o
t
h
f
la
m
e
o
p
tim
izatio
n
(
AM
FO)
alg
o
r
ith
m
,
ef
f
ec
tiv
el
y
r
e
d
u
ce
s
p
ea
k
lo
ad
s
an
d
en
er
g
y
co
s
ts
in
v
ar
io
u
s
s
ec
to
r
s
[
2
0
]
.
T
h
ese
s
tu
d
ies
f
o
c
u
s
o
n
co
s
t
r
ed
u
cti
o
n
i
n
s
m
ar
t
g
r
id
s
a
n
d
h
o
m
e
e
n
er
g
y
s
y
s
tem
s
ac
r
o
s
s
v
ar
io
u
s
s
ec
to
r
s
,
u
s
in
g
o
p
tim
izatio
n
alg
o
r
ith
m
s
lik
e
B
GW
O
an
d
HSA,
as
well
a
s
th
e
u
tili
z
atio
n
o
f
s
y
m
b
i
o
tic
o
r
g
an
is
m
s
s
ea
r
ch
(
SOS)
an
d
C
u
ck
o
o
s
ea
r
ch
(
C
S)
alg
o
r
ith
m
s
[
2
1
]
.
Op
tim
ized
en
er
g
y
s
to
r
a
g
e
an
d
m
an
ag
em
en
t
ar
e
c
r
itical
f
o
r
ef
f
ec
tiv
e
DSM
in
s
m
ar
t
g
r
id
s
[
2
2
]
.
T
h
ey
e
n
ab
le
lo
ad
b
alan
cin
g
b
y
s
to
r
in
g
e
x
ce
s
s
en
er
g
y
d
u
r
in
g
lo
w
-
d
em
an
d
p
er
io
d
s
an
d
r
elea
s
in
g
it
d
u
r
i
n
g
p
ea
k
tim
es,
wh
ich
r
ed
u
c
es
g
r
id
s
tr
ess
an
d
o
p
er
atio
n
al
co
s
ts
.
T
h
is
o
p
tim
izatio
n
en
h
an
ce
s
d
e
m
an
d
r
esp
o
n
s
e
p
r
o
g
r
a
m
s
,
p
r
o
m
o
te
s
r
en
ewa
b
le
e
n
er
g
y
in
teg
r
atio
n
,
an
d
allo
ws
co
n
s
u
m
er
s
to
p
ar
ticip
ate
i
n
e
n
er
g
y
ar
b
it
r
ag
e,
lead
in
g
to
co
s
t
s
av
i
n
g
s
an
d
g
r
ea
ter
g
r
id
r
esil
ien
ce
[
2
3
]
.
B
y
r
ed
u
cin
g
r
elian
ce
o
n
t
h
e
g
r
i
d
d
u
r
in
g
h
ig
h
-
d
em
an
d
p
e
r
io
d
s
,
o
p
tim
ized
s
to
r
ag
e
s
tr
en
g
th
en
s
DSM
'
s
r
o
le
in
en
s
u
r
in
g
a
s
tab
le
an
d
ef
f
icien
t
en
er
g
y
s
y
s
tem
[
2
4
]
.
A
m
u
lti
-
o
b
jectiv
e
o
p
t
im
izatio
n
m
o
d
e
l
f
o
r
h
y
b
r
id
p
o
wer
s
y
s
tem
s
,
co
n
s
id
er
in
g
f
u
el
co
s
t
v
ar
iatio
n
s
an
d
em
p
lo
y
in
g
v
ar
io
u
s
alg
o
r
ith
m
s
,
h
as
b
ee
n
c
r
ea
ted
[
2
5
]
.
Ad
d
itio
n
ally
,
a
to
o
l
to
esti
m
ate
th
e
life
s
p
an
o
f
lead
-
ac
id
b
atter
ies
in
th
ese
s
y
s
te
m
s
is
d
ev
elo
p
ed
.
A
co
m
p
ar
ativ
e
s
tu
d
y
b
etwe
en
lit
h
iu
m
an
d
lead
-
ac
id
b
atter
ies in
h
y
b
r
id
m
u
lti
-
s
o
u
r
ce
s
y
s
tem
s
is
d
ev
elo
p
ed
[
2
6
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
A
h
yb
r
id
A
P
S
O
–
A
N
F
I
S
o
p
timiz
a
tio
n
b
a
s
ed
lo
a
d
s
h
ifti
n
g
tec
h
n
iq
u
e
f
o
r
…
(
Mo
h
a
med
F
a
r
a
d
ji)
47
Fig
u
r
e
1
.
DSM
tech
n
iq
u
es
T
h
e
p
r
esen
t
p
ap
er
f
o
cu
s
es
o
n
o
p
tim
izin
g
DSM
u
s
in
g
th
e
l
o
ad
s
h
if
tin
g
tech
n
iq
u
e
in
s
m
ar
t
g
r
id
s
.
I
t
ex
p
lo
r
es
th
e
im
p
ac
t
o
f
lo
a
d
s
h
if
tin
g
o
n
co
n
s
u
m
er
b
e
h
av
io
r
,
elec
tr
icity
p
r
icin
g
m
ec
h
an
is
m
s
,
an
d
o
v
er
all
g
r
id
p
er
f
o
r
m
an
ce
.
Am
o
n
g
th
e
ex
a
m
in
ed
p
ap
e
r
s
,
ce
r
tain
au
th
o
r
s
h
av
e
f
o
c
u
s
ed
o
n
o
p
tim
izin
g
th
e
co
s
t
-
m
in
im
izatio
n
o
b
jectiv
e
f
u
n
ctio
n
,
wh
ile
o
th
e
r
s
co
n
ce
n
tr
ate
d
s
o
lely
o
n
m
in
i
m
izin
g
p
ea
k
lo
a
d
s
.
T
h
es
e
o
b
je
ctiv
e
f
u
n
ctio
n
s
ca
n
b
e
ca
teg
o
r
ize
d
as
s
in
g
le
-
o
b
je
ctiv
e
m
in
im
izatio
n
p
r
o
b
lem
s
.
I
n
th
e
co
n
tex
t
o
f
a
s
in
g
le
o
b
jectiv
e,
o
p
tim
izin
g
co
s
ts
in
h
er
en
tly
lead
s
to
a
r
ed
u
ctio
n
i
n
p
ea
k
lo
ad
s
,
an
d
v
ice
v
er
s
a
o
p
tim
izin
g
p
ea
k
l
o
ad
s
co
n
tr
i
b
u
tes
to
d
ec
r
ea
s
ed
en
er
g
y
c
o
s
ts
.
So
m
e
r
esear
ch
er
s
h
av
e
in
v
esti
g
ated
th
e
am
alg
am
atio
n
o
f
r
en
e
wab
le
en
er
g
y
with
DSM
with
in
h
o
m
e
en
er
g
y
m
a
n
ag
em
en
t sy
s
tem
s
.
Ho
wev
er
,
wh
en
d
ea
lin
g
with
v
ast
ar
ea
s
an
d
a
m
u
ltit
u
d
e
o
f
d
ev
ices,
th
e
in
teg
r
atio
n
o
f
r
en
ewa
b
le
en
er
g
y
with
DSM
h
as
n
o
t
b
e
en
ex
ten
s
iv
ely
ex
p
lo
r
ed
th
u
s
f
ar
.
T
h
r
o
u
g
h
a
co
m
p
r
eh
e
n
s
iv
e
r
ev
iew
o
f
ex
is
tin
g
liter
atu
r
e,
ca
s
e
s
tu
d
ies,
an
d
s
im
u
latio
n
-
b
ased
an
aly
s
es,
th
is
ar
ticle
aim
s
to
p
r
o
v
id
e
an
ef
f
e
ctiv
e
a
d
ap
tiv
e
PS
O
s
tr
ateg
y
.
An
al
g
o
r
ith
m
-
b
ased
lo
ad
s
h
if
tin
g
tech
n
iq
u
e
is
u
s
e
d
to
r
ed
u
ce
o
p
e
r
atio
n
al
c
o
s
ts
an
d
p
ea
k
d
em
an
d
in
d
if
f
er
en
t
co
n
s
u
m
p
tio
n
ar
ea
s
.
T
h
e
f
in
d
in
g
s
a
n
d
r
ec
o
m
m
e
n
d
atio
n
s
p
r
esen
ted
in
th
is
p
ap
er
d
em
o
n
s
tr
ate
th
e
ad
v
an
ce
m
e
n
t
o
f
DSM
s
tr
ateg
ies,
en
ab
lin
g
s
tak
e
h
o
ld
er
s
t
o
m
ak
e
in
f
o
r
m
ed
d
ec
is
io
n
s
r
e
g
ar
d
in
g
lo
ad
s
h
if
tin
g
o
p
tim
izatio
n
a
n
d
u
ltima
tely
f
o
s
ter
in
g
a
m
o
r
e
s
u
s
tain
ab
le,
r
eliab
le,
an
d
ec
o
n
o
m
ically
ef
f
icien
t
s
m
ar
t
g
r
id
ec
o
s
y
s
tem
.
T
h
e
n
ee
d
f
o
r
in
te
g
r
atin
g
ad
a
p
tiv
e
n
e
u
r
o
-
f
u
zz
y
in
f
er
en
ce
s
y
s
tem
(
ANFI
S)
in
a
d
ap
tiv
e
PSO
is
th
at
th
is
m
eth
o
d
ca
n
o
u
tp
er
f
o
r
m
PS
O
alo
n
e
b
y
co
m
b
i
n
in
g
th
e
s
tr
en
g
t
h
s
o
f
b
o
t
h
m
eth
o
d
s
.
PS
O,
a
g
lo
b
al
o
p
tim
izatio
n
alg
o
r
ith
m
,
is
ef
f
ec
tiv
e
at
ex
p
l
o
r
in
g
co
m
p
le
x
,
m
u
lti
-
d
im
en
s
io
n
al
s
ea
r
ch
s
p
ac
es
an
d
f
i
n
d
in
g
o
p
tim
al
s
o
lu
tio
n
s
,
b
u
t
it
ca
n
s
o
m
etim
es
s
tr
u
g
g
le
with
s
lo
w
co
n
v
er
g
en
ce
o
r
g
ettin
g
tr
ap
p
e
d
in
lo
ca
l
o
p
tim
a,
p
ar
ticu
lar
ly
in
h
ig
h
ly
n
o
n
-
lin
ea
r
s
y
s
tem
s
.
On
th
e
o
th
er
h
a
n
d
,
ANFI
S
in
teg
r
ates
n
eu
r
al
n
etwo
r
k
s
with
f
u
zz
y
in
f
er
en
ce
s
y
s
tem
s
,
allo
win
g
it
to
m
o
d
el
co
m
p
lex
,
n
o
n
-
lin
ea
r
r
elatio
n
s
h
ip
s
b
y
lear
n
in
g
f
r
o
m
d
ata
an
d
ad
ju
s
tin
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
39
,
No
.
1
,
J
u
ly
20
25
:
45
-
61
48
its
elf
ac
co
r
d
in
g
ly
.
I
n
th
e
PS
O
–
ANFI
S
h
y
b
r
id
,
PS
O
o
p
t
im
iz
es
th
e
p
ar
am
ete
r
s
o
f
th
e
ANFI
S
m
o
d
el,
s
u
ch
as
f
u
zz
y
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
,
wh
ile
ANFI
S
r
ef
in
es
th
ese
r
esu
lts
th
r
o
u
g
h
ad
ap
tiv
e
lear
n
in
g
b
ased
o
n
r
ea
l
-
wo
r
ld
d
ata.
T
h
is
h
y
b
r
id
a
p
p
r
o
ac
h
p
r
o
v
id
es
th
e
ad
v
a
n
tag
es
o
f
g
lo
b
al
s
ea
r
ch
f
r
o
m
PS
O
an
d
lo
ca
l
tu
n
in
g
f
r
o
m
ANFI
S,
r
esu
ltin
g
in
f
aster
co
n
v
er
g
e
n
ce
,
im
p
r
o
v
ed
ac
cu
r
ac
y
,
an
d
b
etter
ad
a
p
tab
ilit
y
to
ch
an
g
in
g
o
r
u
n
ce
r
tain
en
v
ir
o
n
m
en
ts
.
Fig
u
r
e
2
illu
s
tr
ates
th
e
u
s
e
o
f
an
in
tellig
en
t
a
lg
o
r
ith
m
f
o
r
DSM
in
a
s
m
ar
t
g
r
id
.
T
h
is
alg
o
r
ith
m
o
p
tim
izes
th
e
b
alan
ce
b
etwe
en
elec
tr
icity
s
u
p
p
ly
an
d
f
lu
ctu
atin
g
co
n
s
u
m
er
d
em
a
n
d
,
a
cr
itical
asp
ec
t
o
f
m
o
d
er
n
g
r
id
s
d
u
e
to
th
e
in
cr
e
asin
g
r
elian
ce
o
n
v
ar
iab
le
r
e
n
ewa
b
le
en
er
g
y
.
B
y
an
aly
zin
g
r
ea
l
-
tim
e
d
ata,
th
e
s
m
ar
t
alg
o
r
ith
m
ad
ju
s
ts
DS
M
s
tr
ateg
ies
to
m
an
ag
e
th
ese
f
lu
ctu
atin
g
lo
ad
s
,
en
s
u
r
in
g
g
r
id
s
tab
ilit
y
an
d
ef
f
icien
cy
.
T
h
e
s
m
ar
t
g
r
id
n
e
two
r
k
,
eq
u
i
p
p
ed
with
ad
v
an
c
ed
co
m
m
u
n
icatio
n
an
d
au
to
m
atio
n
tech
n
o
lo
g
ies,
en
ab
les
r
ea
l
-
tim
e
in
ter
ac
tio
n
b
etwe
en
en
er
g
y
p
r
o
v
id
er
s
an
d
co
n
s
u
m
er
s
,
allo
win
g
f
o
r
d
y
n
am
ic
ad
ju
s
tm
en
ts
th
at
r
ed
u
ce
p
ea
k
d
em
a
n
d
,
im
p
r
o
v
e
en
er
g
y
u
tili
za
tio
n
,
an
d
e
n
h
an
ce
o
v
er
all
g
r
i
d
p
er
f
o
r
m
an
ce
.
T
h
e
o
r
g
an
izatio
n
o
f
th
is
p
a
p
er
is
g
iv
en
as
f
o
llo
ws:
t
h
e
m
eth
o
d
s
s
u
m
m
ar
izin
g
th
e
m
ain
k
ey
s
o
f
th
e
co
n
tr
i
b
u
tio
n
ar
e
p
r
esen
ted
in
s
ec
tio
n
1
.
Als
o
,
th
e
p
r
o
b
lem
f
o
r
m
u
latio
n
is
illu
s
tr
ated
.
Sectio
n
two
is
c
o
n
ce
r
n
e
d
with
th
e
d
escr
ip
tio
n
o
f
s
im
u
latio
n
s
ce
n
ar
io
s
,
m
o
d
elin
g
,
an
d
s
im
u
latio
n
d
ata
o
r
g
a
n
izatio
n
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
o
f
o
p
tim
izatio
n
an
d
o
p
tim
izatio
n
d
iag
r
am
ar
e
g
iv
en
in
s
ec
tio
n
th
r
ee
.
T
h
e
r
esu
lts
o
f
th
e
s
im
u
l
atio
n
ar
e
d
is
cu
s
s
ed
in
s
ec
tio
n
f
o
u
r
.
Fin
ally
,
a
c
o
n
c
lu
s
io
n
is
p
r
esen
ted
s
u
m
m
ar
izi
n
g
th
e
f
i
n
d
in
g
s
,
a
n
d
d
is
cu
s
s
in
g
th
e
im
p
licatio
n
s
.
Fig
u
r
e
2
.
A
p
p
licatio
n
o
f
a
s
m
a
r
t a
lg
o
r
ith
m
f
o
r
DSM
with
in
a
s
m
ar
t g
r
id
n
etwo
r
k
th
at
h
an
d
l
es f
lu
ctu
atin
g
lo
ad
s
2.
M
AT
E
R
I
AL
S AN
D
M
E
T
H
O
DS
T
h
e
s
u
g
g
ested
m
eth
o
d
in
tr
o
d
u
ce
d
in
th
is
p
ap
er
co
n
s
is
ts
o
f
in
v
esti
g
atin
g
o
p
tim
izatio
n
o
f
th
e
DSM
in
v
ar
io
u
s
s
m
ar
t
g
r
id
ar
ea
s
,
in
clu
d
in
g
r
esid
en
tial,
c
o
m
m
er
c
ial,
an
d
in
d
u
s
tr
ial
s
ec
to
r
s
.
T
h
e
s
m
ar
t
g
r
id
is
in
teg
r
ated
with
th
e
p
r
im
ar
y
g
r
id
,
o
p
er
atin
g
at
a
v
o
lta
g
e
o
f
4
1
0
V.
T
h
e
lin
k
le
n
g
th
s
f
o
r
th
e
d
if
f
er
en
t
s
ec
to
r
s
ar
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
A
h
yb
r
id
A
P
S
O
–
A
N
F
I
S
o
p
timiz
a
tio
n
b
a
s
ed
lo
a
d
s
h
ifti
n
g
tec
h
n
iq
u
e
f
o
r
…
(
Mo
h
a
med
F
a
r
a
d
ji)
49
2
k
m
f
o
r
r
esid
en
tial
,
3
k
m
f
o
r
co
m
m
e
r
cial,
an
d
5
k
m
f
o
r
in
d
u
s
tr
ial
zo
n
es.
Un
if
o
r
m
m
ar
k
et
p
r
ices
f
o
r
elec
tr
icity
ar
e
ap
p
lied
t
o
all
s
ec
to
r
s
with
in
th
e
s
m
ar
t
g
r
i
d
.
B
y
an
aly
zin
g
an
d
o
p
tim
izin
g
en
er
g
y
c
o
n
s
u
m
p
tio
n
in
th
ese
d
is
tin
ct
s
ec
to
r
s
,
th
is
s
tu
d
y
aim
s
to
c
o
n
tr
ib
u
te
v
alu
ab
le
in
s
ig
h
ts
in
to
ef
f
ec
tiv
e
lo
ad
m
an
a
g
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en
t
a
n
d
co
s
t
-
s
av
in
g
s
tr
ateg
ies with
in
s
m
ar
t g
r
id
e
n
v
ir
o
n
m
en
ts
.
Fig
u
r
e
2
d
ep
icts
th
e
(
DSM)
f
r
am
ewo
r
k
with
in
a
m
u
lti
-
s
ec
to
r
s
m
ar
t
g
r
id
.
I
n
th
is
f
r
am
ewo
r
k
,
en
er
g
y
is
p
r
o
v
id
e
d
to
t
h
r
ee
s
ec
to
r
s
:
r
esid
en
tial,
co
m
m
e
r
cial,
a
n
d
in
d
u
s
tr
ial,
all
s
o
u
r
ce
d
f
r
o
m
th
e
g
r
id
.
T
h
e
o
p
tim
izatio
n
in
tr
o
d
u
ce
d
i
n
th
is
s
tu
d
y
is
a
m
u
lti
-
s
tr
ateg
y
ad
ap
tiv
e
PSO
lo
ad
s
h
if
tin
g
tech
n
iq
u
e
f
o
r
th
e
a
f
o
r
em
en
tio
n
ed
DSM.
T
h
e
m
o
d
ellin
g
an
d
s
im
u
latio
n
o
f
th
e
s
m
ar
t
g
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id
as
well
a
s
th
e
o
p
tim
izatio
n
alg
o
r
ith
m
im
p
lem
en
ted
in
th
is
s
tu
d
y
ar
e
im
p
lem
en
ted
o
n
MA
T
L
AB
s
o
f
twar
e.
Fig
u
r
e
2
s
h
o
ws
a
g
e
n
er
al
d
escr
ip
tio
n
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
.
T
h
e
p
r
o
p
o
s
ed
s
tr
ateg
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f
o
r
d
em
an
d
m
a
n
ag
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t
r
e
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im
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h
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m
ea
s
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r
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o
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ap
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lian
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s
.
T
h
e
o
b
jectiv
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is
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tim
ize
th
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s
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ile,
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r
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ely
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o
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h
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m
en
t,
a
s
p
ec
if
ic
m
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eq
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is
em
p
l
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as p
ar
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ateg
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s
m
ath
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atica
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x
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ted
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ter
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W
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ep
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ices
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ty
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n
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m
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ty
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ely
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ter
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Dis
co
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t)
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ctio
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ad
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elay
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n
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o
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ich
wer
e
o
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ig
in
all
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ch
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led
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t
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n
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u
m
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tim
e
“
”.
I
t
also
en
co
m
p
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es
th
e
l
o
ad
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r
ea
s
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ltin
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m
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elay
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n
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f
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at
wer
e
ex
p
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ted
to
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m
m
e
n
ce
co
n
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u
m
p
tio
n
at
tim
es
p
r
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ed
in
g
“
”.
T
h
e
m
ath
em
atica
l
e
x
p
r
ess
io
n
o
f
th
e
d
is
co
n
n
ec
tio
n
ter
m
is
g
iv
en
as
(
4
)
.
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c
on
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tion
(
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t
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∑
(
−
1
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x
.
(
1
+
)
=
1
+
y
=
t
+
1
−
1
=
1
(
4
)
I
n
th
e
p
r
o
v
id
e
d
eq
u
atio
n
s
,
d
en
o
tes th
e
n
u
m
b
er
o
f
d
ev
ices o
f
ty
p
e
“
”
th
at
h
av
e
b
ee
n
tr
an
s
f
er
r
ed
f
r
o
m
tim
e
in
s
tan
ce
“
”
to
“
”.
T
o
clar
if
y
th
e
p
r
o
ce
s
s
o
f
l
o
ad
s
h
if
tin
g
,
Fig
u
r
e
3
d
is
p
lay
s
th
e
t
im
ef
r
am
es
d
u
r
in
g
wh
ich
lo
ad
s
ar
e
c
o
n
n
e
cted
o
r
d
is
co
n
n
ec
te
d
.
T
h
er
e
ar
e
two
ca
teg
o
r
ies
o
f
lo
ad
s
:
f
i
x
ed
a
n
d
m
o
v
ab
le.
Fix
ed
lo
ad
s
r
em
ain
co
n
s
tan
t
an
d
u
n
alter
ab
le
with
in
t
h
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o
r
ig
in
al
p
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io
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s
,
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ad
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e
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to
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t tim
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t
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o
llab
le
ch
ar
ac
ter
is
tic
s
.
I
n
Fig
u
r
e
3
,
th
e
lo
ad
s
ar
e
i
n
itially
d
ep
icted
ac
co
r
d
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g
t
o
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r
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ig
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al
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n
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n
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tio
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ts
.
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e
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h
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l
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ad
s
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s
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ated
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ter
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d
is
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n
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ase
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T
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r
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u
ctu
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m
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izatio
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n
m
o
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en
t,
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p
r
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y
(
5
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.
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<
(
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(
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d
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tes th
e
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u
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t o
f
d
e
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f
ty
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av
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le
f
o
r
co
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l a
t tim
e
in
s
tan
ce
“
”.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
39
,
No
.
1
,
J
u
ly
20
25
:
45
-
61
50
Fig
u
r
e
3
.
C
o
n
n
ec
tio
n
a
n
d
d
is
c
o
n
n
ec
tio
n
o
f
lo
a
d
s
in
DSM
3.
DE
SCR
I
P
T
I
O
N
O
F
SCE
NA
RIOS
F
O
R
SI
M
U
L
AT
I
O
N
T
h
e
s
tu
d
y
was
ca
r
r
ied
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t
ac
r
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s
s
v
ar
io
u
s
s
ec
to
r
s
o
f
th
e
s
m
ar
t
g
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id
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in
clu
d
in
g
r
esid
en
tial,
co
m
m
er
cial,
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d
in
d
u
s
tr
ial
zo
n
es.
Op
er
atin
g
alo
n
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s
id
e
th
e
m
ain
g
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id
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th
e
s
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tem
f
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n
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at
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el
o
f
4
1
0
V.
T
h
e
se
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r
s
ar
e
p
o
s
itio
n
ed
at
d
is
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ce
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o
f
2
k
m
,
3
k
m
,
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d
5
k
m
,
r
esp
ec
tiv
ely
.
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u
n
i
f
o
r
m
m
ar
k
et
p
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ice
is
ap
p
lied
ac
r
o
s
s
all
s
ec
to
r
s
.
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h
e
p
r
im
ar
y
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is
to
r
ed
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ce
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tili
ty
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s
ts
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y
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r
in
g
th
e
o
b
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e
f
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n
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n
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o
b
e
in
v
er
s
ely
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elate
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e
m
a
r
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et
p
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ice.
T
h
e
p
o
wer
d
e
m
an
d
f
o
r
th
e
r
e
s
id
en
tial,
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m
m
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n
d
in
d
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to
r
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is
1
.
5
MW,
2
MW,
an
d
3
MW,
r
esp
ec
tiv
ely
.
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t
is
im
p
o
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tan
t
to
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icity
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em
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d
is
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wer
d
u
r
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n
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f
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m
o
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n
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g
h
o
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r
s
,
ty
p
ically
b
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o
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8
am
.
As
a
r
e
s
u
lt,
th
is
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d
is
ex
clu
d
ed
f
r
o
m
h
ig
h
-
p
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k
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s
h
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g
.
T
h
e
tim
e
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d
o
w
f
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ad
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g
b
eg
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e
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h
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r
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en
t
d
ay
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ay
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e
o
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ax
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g
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eled
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I
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m
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r
ated
in
T
ab
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4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
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4
7
5
2
I
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d
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J
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1
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20
25
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61
52
T
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3
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i
n
g
m
a
c
h
i
n
e
0
.
5
-
-
2
6
8
K
e
t
t
l
e
3
2
.
5
-
1
2
3
O
v
e
n
5
-
-
77
C
o
f
f
e
e
m
a
k
e
r
2
2
-
99
A
i
r
c
o
n
d
i
t
i
o
n
e
r
4
3
.
5
3
56
Li
g
h
t
s
2
.
5
1
.
7
5
1
.
5
87
To
t
a
l
-
-
-
8
0
8
T
ab
le
4
.
C
o
n
tr
o
llab
le
d
ev
ice
d
ata
in
th
e
in
d
u
s
tr
ial
ar
ea
Ty
p
e
D
e
v
i
c
e
’
s
h
o
u
r
l
y
c
o
n
s
u
m
p
t
i
o
n
(
k
W
)
1
st
2
nd
3
rd
4
th
5
th
6
th
D
e
v
i
c
e
W
a
t
e
r
h
e
a
t
e
r
1
2
.
5
1
2
.
5
1
2
.
5
-
-
-
39
W
e
l
d
i
n
g
m
a
c
h
i
n
e
25
25
25
25
-
-
35
F
a
n
A
C
30
30
30
30
-
-
16
A
r
c
f
u
r
n
a
c
e
50
50
50
50
50
50
8
I
n
d
u
c
t
i
o
n
m
o
t
o
r
1
0
0
1
0
0
1
0
0
1
0
0
1
0
0
1
0
0
5
D
C
m
o
t
o
r
1
5
0
1
5
0
1
5
0
-
-
-
6
To
t
a
l
-
-
-
-
-
-
1
0
9
4.
A
H
YB
RID
AP
SO
–
ANF
I
S
O
P
T
I
M
I
Z
AT
I
O
N
AL
G
O
R
I
T
H
M
T
h
e
p
r
esen
ted
PS
O
-
f
u
zz
y
h
y
b
r
id
alg
o
r
ith
m
in
teg
r
ates
PS
O
an
d
f
u
zz
y
l
o
g
ic
to
tack
le
o
p
tim
izatio
n
p
r
o
b
lem
s
.
I
n
itiated
b
y
s
ettin
g
p
ar
am
eter
s
f
o
r
b
o
th
PS
O
an
d
f
u
zz
y
lo
g
ic
,
t
h
e
alg
o
r
ith
m
in
itializes
p
ar
ticles
with
r
an
d
o
m
p
o
s
itio
n
s
an
d
v
el
o
cities.
I
n
th
e
m
ain
PS
O
lo
o
p
,
f
itn
ess
is
ev
alu
ated
,
an
d
p
er
s
o
n
al
an
d
g
lo
b
al
b
est
p
o
s
itio
n
s
ar
e
u
p
d
ate
d
b
ased
o
n
th
e
o
b
jectiv
e
f
u
n
ctio
n
.
No
tab
ly
,
th
e
alg
o
r
ith
m
in
c
o
r
p
o
r
ates
f
u
zz
y
l
o
g
ic
b
y
u
s
in
g
ea
ch
p
ar
ti
cle'
s
p
o
s
itio
n
as
in
p
u
t
to
a
f
u
zz
y
lo
g
ic
s
y
s
tem
,
in
f
lu
e
n
cin
g
th
e
p
a
r
ticle'
s
p
o
s
itio
n
u
p
d
ate
.
T
h
is
in
teg
r
atio
n
en
h
a
n
ce
s
ad
ap
ta
b
ilit
y
an
d
ac
co
m
m
o
d
ates
u
n
c
er
tain
ties
with
in
th
e
o
p
tim
iz
atio
n
p
r
o
ce
s
s
.
T
h
e
g
lo
b
al
b
est
p
o
s
itio
n
a
n
d
v
alu
e,
r
ep
r
esen
tin
g
th
e
o
p
tim
iz
ed
s
o
lu
tio
n
,
ar
e
d
is
p
lay
ed
at
th
e
co
n
clu
s
io
n
o
f
th
e
iter
atio
n
s
.
T
h
e
alg
o
r
ith
m
'
s
s
tr
en
g
th
lies
in
th
e
co
llab
o
r
ativ
e
d
ec
is
io
n
-
m
ak
in
g
s
y
n
e
r
g
y
b
et
wee
n
PS
O
'
s
g
lo
b
al
o
p
tim
izatio
n
a
n
d
f
u
zz
y
lo
g
ic'
s
in
ter
p
r
eta
b
ilit
y
,
m
a
k
in
g
it
ef
f
ec
tiv
e
f
o
r
n
a
v
ig
atin
g
co
m
p
lex
s
ea
r
ch
s
p
ac
es
a
n
d
ad
d
r
ess
in
g
p
r
o
b
lem
s
with
in
h
e
r
en
t u
n
ce
r
tain
ties
.
4
.
1
.
A
m
ulti
-
s
t
ra
t
eg
y
a
da
pti
v
e
pa
rt
icle
s
wa
rm
o
pti
m
iza
t
i
o
n a
lg
o
rit
hm
An
ef
f
ec
tiv
e
DSM
tech
n
iq
u
e
s
h
o
u
ld
b
e
ca
p
ab
le
o
f
h
a
n
d
lin
g
a
v
ar
iety
o
f
c
o
n
tr
o
llab
le
l
o
ad
s
,
ea
ch
with
u
n
iq
u
e
ch
ar
ac
ter
is
tics
.
L
in
ea
r
p
r
o
g
r
am
m
in
g
an
d
d
y
n
am
ic
p
r
o
g
r
a
m
m
in
g
p
r
o
v
e
in
a
d
eq
u
a
te
f
o
r
m
an
ag
in
g
a
co
n
s
id
er
ab
le
n
u
m
b
er
o
f
d
iv
e
r
s
e
lo
ad
s
s
im
u
ltan
eo
u
s
ly
[
2
7
]
.
T
h
e
PS
O
alg
o
r
ith
m
f
u
n
ctio
n
s
as
a
p
o
p
u
latio
n
-
b
ased
s
to
ch
asti
c
s
ea
r
ch
tech
n
iq
u
e.
W
ith
in
th
is
f
r
am
ewo
r
k
,
ea
ch
p
ar
ticle’
s
p
o
s
itio
n
s
ig
n
if
ies
a
p
o
ten
tial
s
o
lu
tio
n
to
th
e
o
p
tim
izatio
n
p
r
o
b
lem
at
h
an
d
.
E
v
alu
atio
n
o
f
a
p
ar
ticle’
s
p
o
s
itio
n
o
cc
u
r
s
th
r
o
u
g
h
an
ass
ess
m
en
t
o
f
its
m
er
it,
q
u
an
tifie
d
b
y
th
e
f
itn
ess
v
alu
e
ex
tr
ac
ted
f
r
o
m
th
e
o
p
tim
izatio
n
f
u
n
ctio
n
.
I
n
th
e
in
itializatio
n
p
h
ase
o
f
th
e
PS
O
alg
o
r
ith
m
,
th
e
p
ar
ticle
p
o
p
u
latio
n
is
r
an
d
o
m
ly
estab
lis
h
ed
as
a
co
llectio
n
o
f
ca
n
d
i
d
ate
s
o
lu
tio
n
s
.
Su
b
s
eq
u
en
tly
,
ea
ch
p
ar
ticle
tr
av
er
s
es
th
e
s
ea
r
ch
s
p
ac
e
at
a
s
p
ec
if
ic
v
elo
city
,
s
u
b
ject
to
d
y
n
am
ic
ad
ju
s
tm
en
ts
b
ased
o
n
its
in
d
iv
id
u
al
f
lig
h
t h
is
to
r
y
an
d
th
at
o
f
its
co
m
p
an
io
n
s
[
2
7
]
.
T
h
e
alg
o
r
ith
m
co
n
v
er
g
es
to
war
d
th
e
o
p
tim
al
s
o
lu
tio
n
th
r
o
u
g
h
iter
ativ
e
cy
cles
u
n
til
th
e
p
r
ed
ef
in
e
d
co
n
v
er
g
en
ce
co
n
d
itio
n
is
s
atis
f
ied
.
T
h
is
iter
ativ
e
r
ef
in
e
m
en
t p
r
o
ce
s
s
co
llectiv
ely
co
n
tr
i
b
u
te
s
to
th
e
attain
m
en
t
o
f
th
e
m
o
s
t
f
av
o
r
a
b
le
s
o
lu
ti
o
n
.
PS
O
s
tan
d
s
as
a
n
in
tell
ig
en
t
alg
o
r
ith
m
e
x
h
ib
itin
g
g
lo
b
al
co
n
v
er
g
e
n
ce
,
m
in
im
izin
g
th
e
n
ee
d
f
o
r
ex
ten
s
iv
e
p
ar
am
eter
ad
ju
s
tm
en
ts
.
No
n
eth
eless
,
co
n
v
en
tio
n
a
l
PS
O
en
co
u
n
ter
s
ch
allen
g
es
lik
e
s
u
s
ce
p
tib
ilit
y
to
lo
ca
l
o
p
tim
a
an
d
g
r
a
d
u
al
co
n
v
e
r
g
en
ce
.
T
h
e
h
y
b
r
id
APSO
–
ANFI
S
o
p
tim
izatio
n
alg
o
r
ith
m
(
HA
PA)
ad
d
r
ess
es
th
ese
is
s
u
es
b
y
m
itig
atin
g
th
e
s
ea
r
ch
p
r
o
ce
s
s
’
s
in
h
er
e
n
t
lim
itatio
n
s
,
lead
in
g
to
en
h
an
c
ed
co
n
v
e
r
g
en
ce
p
r
ec
is
io
n
an
d
s
p
ee
d
.
T
h
is
ad
ap
tatio
n
em
p
o
w
er
s
th
e
alg
o
r
ith
m
to
ef
f
icien
tly
tack
le
in
t
r
icate
o
p
t
im
izatio
n
p
r
o
b
lem
s
,
r
e
d
u
cin
g
s
ea
r
ch
p
r
o
ce
s
s
b
ias
an
d
en
h
a
n
cin
g
its
s
u
itab
ilit
y
f
o
r
co
m
p
lex
s
ce
n
ar
io
s
[
2
7
]
.
T
h
e
v
elo
city
ex
p
r
ess
io
n
o
f
t
h
e
alg
o
r
ith
m
is
g
iv
en
in
(
7
)
,
an
d
th
e
esti
m
ated
p
o
s
itio
n
is
illu
s
tr
ated
b
y
(
8
)
.
(
+
1
)
=
×
(
)
+
1
×
(
)
×
(
(
)
−
(
)
)
+
2
×
(
)
×
(
(
)
−
(
)
)
(
7
)
(
+
1
)
=
(
)
+
(
+
1
)
(
8
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
A
h
yb
r
id
A
P
S
O
–
A
N
F
I
S
o
p
timiz
a
tio
n
b
a
s
ed
lo
a
d
s
h
ifti
n
g
tec
h
n
iq
u
e
f
o
r
…
(
Mo
h
a
med
F
a
r
a
d
ji)
53
W
h
er
e
“
”
is
th
e
s
u
p
p
o
s
ed
p
o
p
u
latio
n
with
“
”
p
ar
ticles
is
th
e
v
elo
city
o
f
ℎ
p
ar
ticle,
(
)
s
ig
n
if
ies
th
e
p
er
s
o
n
al
b
est
p
o
s
itio
n
t
h
at
p
ar
ticle
“
”
h
as
e
n
co
u
n
ter
ed
s
in
ce
th
e
in
itial
tim
e
s
tep
.
Fu
r
th
er
m
o
r
e,
“
”
d
esig
n
ates
th
e
m
o
s
t
o
p
tim
al
p
o
s
itio
n
d
is
co
v
er
ed
b
y
th
e
c
o
llectiv
e
p
ar
ticles
u
p
to
th
at
p
o
in
t
in
th
e
p
r
o
ce
s
s
.
I
n
th
is
co
n
tex
t,
wh
er
e
i
r
an
g
es
f
r
o
m
1
to
,
ce
r
tain
v
ar
ia
b
le
s
ar
e
d
ef
in
ed
.
T
h
e
in
er
tia
weig
h
t
is
r
ep
r
esen
ted
as
“
”,
with
“
c1
”
,
a
n
d
“
2
”
s
er
v
in
g
as
co
n
s
tan
ts
in
tr
in
s
ic
to
th
e
PS
O
alg
o
r
ith
m
an
d
ta
k
in
g
v
a
lu
es
with
in
th
e
r
an
g
e
o
f
[
0
,
2
]
.
Me
an
wh
ile,
“
(
)
”
s
tan
d
s
f
o
r
r
an
d
o
m
n
u
m
b
er
s
co
n
f
in
ed
with
in
th
e
in
ter
v
a
l
[
0
,
1
]
.
An
illu
s
tr
ativ
e
r
ep
r
esen
tatio
n
o
f
th
e
p
ar
ticle
m
o
v
em
en
t
p
r
o
ce
s
s
b
ased
o
n
PS
O
iter
atio
n
s
is
p
r
e
s
en
ted
in
Fig
u
r
e
4
.
Fig
u
r
e
4
.
A
s
eq
u
e
n
tial p
r
o
g
r
es
s
io
n
o
f
p
a
r
ticle
m
o
tio
n
with
in
th
e
PS
O
f
r
am
ewo
r
k
T
h
e
s
tr
ateg
ies f
o
r
en
h
a
n
cin
g
i
n
er
tia
weig
h
ts
“
”
an
d
lear
n
i
n
g
f
ac
to
r
s
(
1
,
2
)
en
co
m
p
ass
a
s
p
ec
tr
u
m
o
f
class
if
icatio
n
s
,
in
clu
d
in
g
c
o
n
s
tan
cy
o
r
s
to
ch
asti
city
,
lin
e
ar
ity
o
r
n
o
n
-
lin
ea
r
ity
,
an
d
ad
ap
tab
ilit
y
.
E
x
is
tin
g
r
esear
ch
h
as
ex
p
e
r
im
en
tally
d
em
o
n
s
tr
ated
th
e
ef
f
icac
y
o
f
n
o
n
-
lin
e
ar
ly
d
ec
r
ea
s
in
g
wei
g
h
ts
o
v
er
lin
ea
r
ly
d
ec
r
ea
s
in
g
o
n
es
wi
th
in
th
e
co
n
tex
t
o
f
th
e
d
u
al
d
y
n
am
ic
ad
ap
tatio
n
m
ec
h
an
is
m
.
T
h
e
u
tili
za
tio
n
o
f
n
o
n
lin
ea
r
lear
n
in
g
f
ac
to
r
s
o
f
f
er
s
h
eig
h
te
n
ed
co
m
p
atib
ilit
y
with
in
tr
icat
e
o
p
tim
izatio
n
o
b
jectiv
es,
alig
n
in
g
well
with
th
e
co
m
p
lex
ities
in
h
er
en
t
in
s
u
ch
p
u
r
s
u
its
.
A
n
o
tewo
r
th
y
ap
p
r
o
a
ch
lev
er
ag
es
in
er
tia
weig
h
ts
to
f
in
e
-
tu
n
e
lear
n
in
g
f
ac
to
r
s
,
th
er
eb
y
ac
h
ie
v
in
g
a
b
alan
ce
b
etwe
en
in
d
iv
id
u
al
p
ar
ticle
lear
n
in
g
ca
p
ab
ilit
ies
an
d
co
llectiv
e
g
r
o
u
p
lear
n
in
g
ca
p
ab
ilit
ies.
T
h
is
eq
u
ilib
r
iu
m
s
ig
n
if
ican
tly
en
h
a
n
c
es
th
e
alg
o
r
ith
m
’
s
o
p
tim
izatio
n
ac
cu
r
ac
y
.
I
n
th
is
p
ap
er
,
a
h
y
b
r
i
d
ap
p
r
o
ac
h
am
alg
am
atin
g
b
o
t
h
s
tr
ateg
ies
h
as
b
ee
n
ad
o
p
ted
,
y
ield
in
g
s
u
p
er
io
r
r
esu
lts
.
T
h
e
f
lo
wch
ar
t
d
ep
icted
in
F
ig
u
r
e
5
d
em
o
n
s
tr
ates
th
e
p
r
o
p
o
s
ed
o
p
tim
izatio
n
alg
o
r
ith
m
.
T
h
e
p
a
r
am
eter
“
”
s
er
v
es
as
a
p
iv
o
tal
d
eter
m
in
an
t
in
f
lu
en
cin
g
th
e
p
er
f
o
r
m
a
n
ce
an
d
e
f
f
icac
y
o
f
th
e
PS
O
alg
o
r
ith
m
.
R
ed
u
ce
d
v
alu
es
o
f
“
”
b
o
ls
ter
th
e
alg
o
r
ith
m
’
s
ca
p
ac
ity
f
o
r
lo
ca
l
s
ea
r
ch
,
elev
a
tin
g
co
n
v
er
g
e
n
ce
ac
cu
r
ac
y
.
C
o
n
v
er
s
ely
,
lar
g
e
r
“
”
v
alu
es
e
n
h
an
ce
g
l
o
b
al
s
ea
r
ch
ca
p
ab
ilit
ies,
p
r
ev
e
n
tin
g
p
ar
ticles
f
r
o
m
b
ein
g
c
o
n
f
in
e
d
to
lo
ca
l
o
p
tim
a;
h
o
wev
er
,
th
is
m
ig
h
t r
esu
lt
in
a
s
lo
wer
co
n
v
er
g
en
ce
r
ate.
A
s
i
g
n
if
ican
t p
r
o
p
o
r
tio
n
o
f
o
n
g
o
in
g
en
h
an
ce
m
e
n
ts
in
PS
O
p
er
tain
to
th
e
o
p
tim
izatio
n
o
f
“
”.
=
+
(
+
)
×
e
xp
[
−
20
×
(
)
6
]
(
9
)
W
h
er
e
T
is
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
tim
e
s
tep
s
,
u
s
u
ally
.
T
h
e
lear
n
in
g
f
ac
to
r
(
1
,
2
)
v
ar
ies ac
co
r
d
in
g
to
“
”.
T
h
e
v
alu
es
o
f
“
1
”
an
d
“
2
”
wit
h
in
th
e
v
elo
city
u
p
d
ate
eq
u
at
io
n
p
lay
s
a
cr
u
cial
r
o
le
in
d
eter
m
in
in
g
th
e
d
eg
r
ee
o
f
lea
r
n
in
g
ex
h
ib
ited
b
y
a
p
ar
ticle
to
war
d
i
ts
o
p
tim
a
l
p
o
s
itio
n
.
Sp
ec
if
ically
,
“
1
”
g
o
v
er
n
s
th
e
d
eg
r
ee
o
f
s
elf
-
lear
n
in
g
o
f
th
e
p
ar
ticle,
wh
ile
“
2
”
in
f
lu
en
ce
s
th
e
ex
te
n
t
o
f
its
s
o
cial
lear
n
in
g
.
T
h
ese
co
ef
f
icien
ts
also
co
n
tr
ib
u
te
to
alter
in
g
th
e
p
ar
ti
cle’
s
tr
ajec
to
r
y
o
v
e
r
tim
e.
B
u
ild
in
g
u
p
o
n
p
r
io
r
in
s
ig
h
ts
,
th
is
s
tu
d
y
ad
o
p
ts
an
en
h
an
ce
d
ad
ju
s
tm
en
t
s
tr
ateg
y
f
o
r
th
es
e
lear
n
in
g
f
ac
to
r
s
an
d
in
er
tia
weig
h
ts
.
T
h
is
s
tr
ateg
y
ca
p
italizes
o
n
th
e
ad
v
an
tag
es
o
f
em
p
lo
y
in
g
n
o
n
-
lin
ea
r
f
u
n
ctio
n
s
.
T
h
e
co
ef
f
icien
ts
ar
e
h
ar
m
o
n
iz
ed
with
th
e
v
alu
es
“
A
=
0
.
5
”
,
“
B
=
1
”,
a
n
d
“
C
=
0
.
5
”,
r
esu
lt
in
g
in
f
o
r
m
u
la
1
0
.
B
y
lev
er
ag
in
g
t
h
is
r
ef
in
ed
co
m
b
in
atio
n
,
th
e
PS
O
alg
o
r
ith
m
ca
n
ac
h
iev
e
im
p
r
o
v
ed
p
er
f
o
r
m
an
ce
a
n
d
co
n
v
er
g
en
ce
o
u
tco
m
es.
Fo
r
m
u
latio
n
s
o
f
th
e
f
ac
to
r
s
“
C
1
”
an
d
“
C
2
”
ar
e
r
esp
ec
tiv
ely
g
iv
en
b
y
(
1
0
)
an
d
(
1
1
)
.
1
=
2
+
+
(
1
0
)
2
=
2
.
5
+
1
(
1
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
39
,
No
.
1
,
J
u
ly
20
25
:
45
-
61
54
T
h
e
alg
o
r
it
h
m
’
s
co
n
v
er
g
e
n
ce
an
d
th
e
s
p
ee
d
at
wh
ich
it
co
n
v
er
g
es
ar
e
s
ig
n
i
f
ican
tly
in
ter
t
win
ed
with
th
e
p
o
s
itio
n
weig
h
tin
g
f
ac
to
r
.
Ho
wev
er
,
th
e
p
r
im
ar
y
p
ar
am
e
ter
-
tu
n
in
g
a
p
p
r
o
ac
h
o
f
ten
c
o
n
ce
n
tr
ates
s
o
lely
o
n
r
ef
in
in
g
v
elo
city
u
p
d
ates,
n
e
g
lectin
g
p
o
s
itio
n
u
p
d
ates.
T
o
a
d
d
r
ess
th
is
lim
itatio
n
an
d
r
e
g
u
late
th
e
im
p
ac
t
o
f
v
elo
city
o
n
p
o
s
itio
n
,
an
in
n
o
v
ativ
e
p
o
s
itio
n
u
p
d
ate
f
o
r
m
u
la
in
co
r
p
o
r
ates
a
co
n
s
tr
ain
t
f
ac
to
r
“
”.
T
h
e
in
tr
o
d
u
ctio
n
o
f
“
”
s
er
v
es
th
e
p
u
r
p
o
s
e
o
f
c
alib
r
atin
g
t
h
e
i
n
f
lu
en
ce
o
f
v
elo
city
,
aim
in
g
to
m
itig
ate
s
ea
r
ch
p
r
o
ce
s
s
lim
itatio
n
s
an
d
s
u
b
s
eq
u
en
tly
en
h
an
ce
th
e
al
g
o
r
ith
m
’
s
co
n
v
er
g
e
n
ce
r
ate.
I
n
th
e
b
asic PSO f
r
am
ewo
r
k
,
a
p
ar
ticle’
s
n
ew
p
o
s
itio
n
is
d
eter
m
in
ed
b
y
ad
d
in
g
its
cu
r
r
en
t
v
elo
city
to
its
p
r
esen
t
p
o
s
itio
n
.
Ho
wev
er
,
th
e
d
ir
ec
t
ad
d
itio
n
o
f
p
o
s
iti
o
n
an
d
v
elo
city
v
ec
to
r
s
r
eq
u
ir
es
th
e
in
tr
o
d
u
ctio
n
o
f
a
co
n
s
tr
ain
t
f
ac
to
r
with
in
th
e
p
o
s
itio
n
u
p
d
ate
f
o
r
m
u
la.
T
r
ad
itio
n
ally
,
th
is
co
n
s
tr
ain
t
f
ac
to
r
in
th
e
PS
O
alg
o
r
ith
m
is
ty
p
ically
s
et
to
1
.
T
h
e
r
o
le
o
f
“
α
”
is
to
s
teer
th
e
p
ar
ticle
t
o
war
d
p
r
o
x
im
ity
to
i
ts
o
p
tim
al
p
o
s
itio
n
,
an
d
its
en
h
an
ce
m
e
n
t
co
n
t
r
o
ls
th
e
d
eg
r
ee
t
o
wh
ich
v
elo
city
i
n
f
lu
en
ce
s
p
o
s
itio
n
.
B
y
r
eg
u
lat
in
g
th
is
in
f
lu
en
ce
,
th
e
alg
o
r
ith
m
’
s
co
n
v
er
g
en
ce
i
s
n
o
tab
ly
en
h
a
n
ce
d
.
I
n
th
is
s
tu
d
y
,
an
“
”
th
at
ev
o
lv
es
b
ased
o
n
ch
an
g
es
in
is
em
p
lo
y
ed
.
Du
r
in
g
t
h
e
in
itial
s
tag
es,
“
”
is
h
ea
v
ily
in
f
lu
en
ce
d
b
y
p
ar
ticle
v
elo
city
,
f
ac
ilit
atin
g
r
o
b
u
s
t
ex
p
lo
r
atio
n
.
Su
b
s
eq
u
en
tly
,
in
later
s
tag
es,
’
s
s
en
s
itiv
ity
to
p
a
r
ticle
v
elo
city
d
im
in
is
h
e
s
,
r
ein
f
o
r
cin
g
its
ef
f
icac
y
in
lo
ca
l sear
c
h
ac
tiv
ities
.
(
+
1
)
=
(
)
+
(
+
1
)
(
1
2
)
=
0
.
1
+
(
1
3
)
Fig
u
r
e
5
.
Flo
wch
ar
t
f
o
r
HAP
A
alg
o
r
ith
m
4
.
2
.
An a
da
ptiv
e
net
wo
rk
-
b
a
s
ed
f
uzzy
infe
re
nce
s
y
s
t
em
Neu
r
al
n
etwo
r
k
s
(
NN)
r
e
p
r
es
en
t
p
o
ten
t
an
d
v
er
s
atile
to
o
ls
f
o
r
f
o
r
ec
asti
n
g
,
lev
e
r
ag
in
g
s
im
p
licity
alo
n
g
s
id
e
r
em
ar
k
ab
le
ca
p
a
b
ilit
ies.
T
h
eir
ef
f
ec
tiv
en
ess
h
in
g
es
o
n
th
e
av
ailab
ilit
y
o
f
s
u
f
f
i
cien
t
tr
ain
in
g
d
ata,
a
ju
d
icio
u
s
s
elec
tio
n
o
f
in
p
u
t
-
o
u
t
p
u
t
s
am
p
les,
an
ap
p
r
o
p
r
iate
n
u
m
b
e
r
o
f
h
id
d
en
u
n
its
,
an
d
am
p
l
e
co
m
p
u
tatio
n
al
r
eso
u
r
c
es.
No
ta
b
ly
,
NN
p
o
s
s
ess
es
th
e
ab
ilit
y
to
ap
p
r
o
x
i
m
ate
an
y
n
o
n
lin
ea
r
f
u
n
ctio
n
an
d
tack
le
Evaluation Warning : The document was created with Spire.PDF for Python.