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[
1
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an
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cr
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to
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[
2
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ae
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lets
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[
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.
Mo
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tr
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co
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s
tem
s
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s
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ex
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eq
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s
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o
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tlin
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in
[
4
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,
r
e
q
u
ir
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m
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s
p
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to
ac
cu
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a
s
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T
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lv
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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20
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5
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01
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0
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102
ev
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to
c
r
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te
m
o
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els
f
o
r
r
esear
ch
p
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[
5
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o
r
ad
a
p
tiv
e
co
n
tr
o
l
lo
o
p
s
[
6
]
.
I
n
p
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s
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d
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th
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f
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s
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m
ath
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atica
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tial.
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an
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Mü
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ch
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f
[
7
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,
ex
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o
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m
o
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[
8
]
.
I
n
co
n
tr
ast,
p
ar
am
etr
ic
m
o
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[
9
]
h
a
v
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well
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in
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r
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f
in
ite
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s
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s
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p
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n
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s
o
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tial
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q
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s
.
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ased
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ith
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ase
d
o
p
tim
izatio
n
tec
h
n
iq
u
es
ar
e
v
u
ln
er
a
b
le
to
lo
ca
l
o
p
tim
a.
T
h
ey
o
v
er
c
o
m
e
t
r
ad
itio
n
al
o
b
s
tacle
s
with
h
eu
r
is
tics
an
d
r
a
n
d
o
m
s
ea
r
ch
[
1
0
]
,
[
1
1
]
.
Me
tah
eu
r
is
tics
,
o
n
th
e
o
th
er
h
a
n
d
,
ar
e
s
to
ch
asti
c
o
p
tim
izatio
n
alg
o
r
ith
m
s
th
at
s
e
ar
ch
th
e
s
ea
r
ch
s
p
ac
e
f
o
r
th
e
b
est
s
o
lu
tio
n
with
o
u
t
u
s
in
g
g
r
a
d
ien
ts
b
u
t
r
at
h
er
h
e
u
r
is
tics
an
d
r
an
d
o
m
s
ea
r
c
h
[
1
2
]
.
Fak
h
ar
et
a
l.
[
1
3
]
ex
p
lain
ed
m
etah
eu
r
is
tics
ar
e
a
g
o
o
d
o
p
tio
n
.
T
h
e
y
ar
e
id
ea
l
f
o
r
n
o
n
-
c
o
n
v
e
x
an
d
m
u
l
tim
o
d
al
o
p
tim
izatio
n
p
r
o
b
lem
s
b
ec
au
s
e
s
to
ch
asti
c
o
p
tim
izatio
n
alg
o
r
ith
m
s
ex
p
lo
r
e
s
ea
r
ch
s
p
ac
es with
o
u
t g
r
ad
i
en
ts
.
2.
P
ARAM
E
T
R
I
C
M
O
DE
L
I
D
E
NT
I
F
I
C
AT
I
O
N
T
h
is
p
ap
er
q
u
a
n
tizes
co
n
tin
u
o
u
s
r
o
tatio
n
d
ata
u
s
in
g
th
e
f
l
o
o
r
f
u
n
ctio
n
an
d
em
u
lates
th
e
tr
an
s
f
er
f
u
n
ctio
n
with
an
a
r
m
atu
r
e
-
co
n
tr
o
lled
DC
s
er
v
o
m
o
to
r
.
A
D
C
s
e
r
v
o
m
o
t
o
r
'
s
b
e
h
a
v
i
o
r
c
a
n
b
e
q
u
a
n
t
i
t
a
t
i
v
e
l
y
e
x
p
r
e
s
s
e
d
u
s
i
n
g
d
i
f
f
e
r
e
n
t
i
a
l
e
q
u
a
t
i
o
n
s
[
1
4
]
.
F
i
g
u
r
e
1
s
h
o
w
s
h
o
w
a
D
C
s
e
r
v
o
m
o
t
o
r
w
o
r
k
s
:
a
c
u
r
r
e
n
t
p
a
s
s
es
t
h
r
o
u
g
h
a
c
o
i
l
,
c
r
e
a
ti
n
g
a
m
a
g
n
e
t
i
c
f
i
e
l
d
t
h
a
t
i
n
t
e
r
a
ct
s
w
i
t
h
a
p
e
r
m
a
n
e
n
t
m
a
g
n
e
t
t
o
r
o
t
a
t
e
t
h
e
s
h
a
f
t
[
1
5
]
.
C
r
ea
tin
g
elec
tr
ical
an
d
m
ec
h
a
n
ical
eq
u
atio
n
s
in
d
ep
en
d
en
tly
an
d
m
er
g
in
g
th
em
d
escr
ib
es e
lectr
o
m
ec
h
an
ical
r
e
latio
n
s
h
ip
s
[
1
6
]
.
Fig
u
r
e
1
.
DC
m
o
to
r
cir
cu
it d
ia
g
r
am
T
h
e
s
y
s
tem
's
in
p
u
t
is
ar
m
atu
r
e
v
o
ltag
e,
an
d
its
o
u
tp
u
t
is
th
e
m
ea
s
u
r
ed
s
h
af
t
an
g
le
in
d
eg
r
ee
s
.
C
o
n
s
id
er
th
e
in
p
u
ts
(
)
an
d
(
)
,
an
d
th
e
o
u
tp
u
t
(
)
.
W
r
ap
KVL
ar
o
u
n
d
th
e
a
r
m
atu
r
e
-
m
ec
h
a
n
ical
d
y
n
a
m
ics
:
(
)
=
×
(
)
+
×
(
(
)
)
+
(
)
(
1
)
(
)
=
×
(
(
)
)
+
×
(
)
(
2
)
t
ak
in
g
L
ap
lace
tr
a
n
s
f
o
r
m
o
n
(
1
)
ass
u
m
in
g
in
itial c
o
n
d
itio
n
s
to
b
e
ze
r
o
,
th
en
:
(
)
=
.
(
)
.
+
.
(
)
+
(
)
(
3
)
(
)
=
[
1
.
+
]
.
[
(
)
−
(
)
]
(
4
)
t
ak
in
g
L
ap
lace
tr
a
n
s
f
o
r
m
o
n
m
ec
h
an
ical
s
y
s
tem
d
y
n
a
m
ics o
n
(
2
)
,
th
en
:
(
)
=
[
∙
+
]
∙
(
)
⇒
(
)
=
[
1
∙
+
]
∙
(
)
(
5
)
[
(
)
(
)
]
=
[
∙
∙
2
+
(
∙
+
∙
)
.
+
(
∙
+
∙
)
]
(
6
)
s
o
lv
in
g
f
o
r
(
)
=
[
1
]
∙
(
)
ca
n
b
e
g
iv
e
n
as (
7
)
.
[
(
)
(
)
]
=
[
∙
∙
3
+
(
∙
+
∙
)
.
2
+
(
∙
+
∙
)
∙
]
(
7
)
Fig
u
r
e
2
d
ep
icts
a
co
n
tr
o
l
s
y
s
tem
f
o
r
an
ac
tu
al
s
er
v
o
m
o
to
r
.
I
n
itially
,
an
in
p
u
t
s
ig
n
al
u
n
d
er
g
o
es
m
o
d
if
icatio
n
th
r
o
u
g
h
th
e
tr
an
s
f
er
f
u
n
ctio
n
o
f
th
e
s
er
v
o
m
o
to
r
,
e
x
p
r
ess
ed
as
1
/La
.
s
+Ra.
Su
b
s
eq
u
en
tly
,
th
e
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
Meta
h
eu
r
is
tic
a
lg
o
r
ith
ms fo
r
p
a
r
a
mete
r
esti
ma
tio
n
o
f D
C
s
ervo
mo
to
r
s
…
(
Deb
a
n
i P
r
a
s
a
d
Mis
h
r
a
)
103
s
y
s
tem
tr
av
er
s
es
s
ev
er
al
s
tag
e
s
,
in
clu
d
in
g
a
t
o
r
q
u
e
co
n
s
tan
t
Kt,
a
m
ec
h
a
n
ical
tr
an
s
f
er
f
u
n
c
tio
n
1
/(
J
.
s
+f
o
)
,
an
d
a
f
lo
o
r
o
p
er
atio
n
,
cu
lm
in
atin
g
in
th
e
“ser
v
o
m
ea
s
u
r
e
d
o
u
t
p
u
t.
”
A
f
ee
d
b
ac
k
lo
o
p
in
teg
r
ates
a
b
ac
k
elec
tr
o
m
o
tiv
e
f
o
r
ce
co
n
s
tan
t K
b
,
co
n
tr
ib
u
tin
g
t
o
th
e
o
v
er
all
clo
s
ed
-
lo
o
p
c
o
n
tr
o
l sy
s
tem
.
Fig
u
r
e
2
.
Actu
al
o
r
m
o
d
eled
b
lo
ck
d
iag
r
a
m
o
f
th
e
DC
-
s
er
v
o
m
o
to
r
alo
n
g
with
th
e
r
o
tar
y
en
co
d
er
3.
M
O
DE
L
VE
RIFI
CAT
I
O
N
AND
RE
SPO
NSE
A
s
y
s
tem
with
an
in
teg
r
ato
r
will
in
cr
ea
s
e
o
u
tp
u
t
o
v
er
tim
e
with
a
s
tep
in
p
u
t.
Sin
ce
th
e
in
teg
r
ato
r
ac
cu
m
u
lates
in
p
u
t,
th
e
o
u
tp
u
t g
r
o
ws
with
tim
e.
T
h
e
s
y
s
tem
h
as
a
p
o
le
at
th
e
o
r
ig
in
,
h
en
ce
s
tep
in
p
u
t
r
esp
o
n
s
e
is
in
f
in
itely
lar
g
e
[
1
7
]
,
as
s
ee
n
in
F
ig
u
r
e
3
.
T
h
u
s
,
wh
en
g
iv
en
a
s
tep
in
p
u
t,
th
e
s
y
s
tem
's
o
u
tp
u
t
r
is
es
in
d
ef
in
itely
.
T
h
is
u
n
b
o
u
n
d
ed
g
r
o
wth
is
im
p
o
r
tan
t to
co
n
s
id
er
in
i
n
teg
r
at
o
r
s
y
s
tem
d
esig
n
an
d
an
aly
s
is
b
ec
au
s
e
it c
an
af
f
ec
t
r
ea
l
-
wo
r
ld
ap
p
licatio
n
s
.
T
h
is
u
s
es a
1
V
s
tep
in
p
u
t.
Fig
u
r
e
4
m
ag
n
if
ies F
ig
u
r
e
3
to
s
h
o
w
s
e
n
s
o
r
q
u
a
n
tizatio
n
.
T
h
e
in
teg
r
al
ab
s
o
lu
te
er
r
o
r
(
I
AE
)
co
s
t
f
u
n
ctio
n
was
u
s
ed
to
ev
alu
ate
o
p
tim
izatio
n
s
tr
ateg
ies
in
th
e
p
ap
er
to
r
ed
u
ce
co
m
p
u
tin
g
c
o
m
p
lex
ity
[
1
8
]
.
Heu
r
is
tics
ar
e
u
s
ed
to
m
in
im
ize
I
AE
,
th
e
co
s
t
f
u
n
ctio
n
in
th
is
s
tu
d
y
.
L
a
, R
a
, K
t
, K
b
,
J
,
an
d
F
o
ar
e
th
e
DC
-
s
er
v
o
m
o
to
r
tr
an
s
f
er
f
u
n
ctio
n
p
r
ed
ictin
g
p
ar
a
m
eter
s
.
E
ac
h
s
et
o
f
s
ix
v
ar
iab
les is
a
s
o
lu
tio
n
.
Fig
u
r
e
3
.
Step
r
esp
o
n
s
e
o
f
th
e
m
o
to
r
to
1
V
ar
m
at
u
r
e
v
o
ltag
e
Fig
u
r
e
4
.
Ma
g
n
if
ied
p
o
r
tio
n
o
f
F
ig
u
r
e
3
4.
DE
T
E
R
M
I
N
AT
I
O
N
AND
I
M
P
L
E
M
E
N
T
A
T
I
O
N
O
F
T
H
E
AL
G
O
RI
T
H
M
S
4
.
1
.
G
enet
ic
a
lg
o
rit
h
m
(
G
A)
T
h
e
g
en
etic
alg
o
r
ith
m
(
GA)
is
an
o
p
tim
izatio
n
tech
n
iq
u
e
b
ased
o
n
n
atu
r
al
s
elec
tio
n
an
d
g
en
etic
ev
o
lu
tio
n
.
I
n
1
9
7
5
,
J
o
h
n
Ho
lla
n
d
in
tr
o
d
u
ce
d
g
e
n
etic
alg
o
r
ith
m
s
.
T
h
ey
u
s
e
g
e
n
etic
o
p
e
r
atio
n
s
in
clu
d
in
g
s
elec
ts
,
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
to
iter
ativ
ely
ev
o
lv
e
a
p
o
p
u
latio
n
o
f
ca
n
d
id
ate
s
o
lu
tio
n
s
to
d
is
co
v
er
th
e
b
est
an
s
wer
[
1
9
]
.
Fig
u
r
e
5
(
a
)
s
h
o
ws
th
e
b
asic
s
t
ep
s
o
f
a
g
en
etic
alg
o
r
it
h
m
[
2
0
]
.
T
h
e
alg
o
r
ith
m
g
en
er
ates
a
p
o
p
u
latio
n
o
f
p
o
ten
tial
s
o
lu
tio
n
s
.
A
s
et
o
f
r
an
d
o
m
p
eo
p
le
r
ep
r
esen
tin
g
d
if
f
er
en
t
p
r
o
b
lem
s
o
lu
tio
n
s
is
u
s
u
ally
u
s
ed
.
T
h
e
p
o
p
u
latio
n
d
ep
e
n
d
s
o
n
th
e
p
r
o
b
lem
an
d
co
m
p
u
tatio
n
al
r
e
s
o
u
r
ce
s
.
Fit
n
ess
is
u
s
ed
to
ass
es
s
ea
ch
p
er
s
o
n
'
s
p
r
o
b
lem
-
s
o
lv
in
g
ab
ilit
y
.
T
h
e
f
i
tn
ess
f
u
n
ctio
n
,
ad
ap
te
d
to
th
e
i
n
d
iv
id
u
al
s
itu
atio
n
,
estab
lis
h
es
th
e
p
ar
am
eter
s
f
o
r
ev
alu
atin
g
th
e
s
o
lu
tio
n
’
s
q
u
ali
ty
[
2
1
]
,
[
22]
.
Ap
p
ly
in
g
th
e
f
itn
ess
f
u
n
ctio
n
to
ea
ch
p
er
s
o
n
g
iv
es
a
f
itn
ess
s
co
r
e.
E
ac
h
p
o
p
u
latio
n
m
e
m
b
er
'
s
f
itn
ess
s
co
r
e
is
ca
lcu
lated
d
u
r
in
g
ev
alu
ati
o
n
.
T
h
e
g
en
etic
alg
o
r
ith
m
is
ex
te
n
s
iv
ely
u
s
ed
in
o
p
tim
izatio
n
p
r
o
b
lem
s
s
u
ch
as
f
in
d
i
n
g
th
e
o
p
tim
al
s
o
lu
tio
n
to
a
m
at
h
em
atica
l
e
q
u
atio
n
,
d
esig
n
in
g
o
p
tim
al
en
g
in
ee
r
in
g
s
tr
u
ct
u
r
es,
an
d
o
p
tim
izin
g
f
i
n
an
cial
p
o
r
t
f
o
lio
s
.
4
.
2
.
Pa
rt
icle
s
wa
rm
o
ptim
iz
a
t
io
n
(
P
SO
)
PS
O
i
s
a
p
o
p
u
latio
n
-
b
ased
o
p
tim
izatio
n
m
eth
o
d
in
s
p
ir
e
d
b
y
b
ir
d
a
n
d
f
is
h
b
eh
a
v
io
r
.
K
en
n
d
y
a
n
d
E
b
er
h
ar
t
i
n
tr
o
d
u
ce
d
PS
O
in
1
9
9
5
.
PS
O
m
im
ics
th
e
s
o
cial
b
eh
av
io
r
o
f
a
s
war
m
o
f
p
ar
ticles
s
ea
r
ch
in
g
a
m
u
lti
-
d
im
en
s
io
n
al
s
p
ac
e
to
s
o
lv
e
o
p
tim
izatio
n
p
r
o
b
lem
s
.
T
h
e
p
ar
ti
cles
u
p
d
ate
th
eir
p
o
s
itio
n
s
an
d
v
elo
cities
b
ased
o
n
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.
1
4
,
No
.
1
,
Ma
r
ch
20
2
5
:
1
01
-
1
0
8
104
th
eir
b
est
p
o
s
itio
n
,
th
e
n
est
p
o
s
itio
n
f
o
u
n
d
b
y
an
y
p
ar
ticle
in
t
h
e
s
war
m
,
an
d
t
h
eir
cu
r
r
en
t
p
o
s
itio
n
as
th
ey
s
ea
r
ch
th
e
s
p
ac
e.
Fig
u
r
e
5
(
b
)
s
h
o
ws th
e
P
SO stag
es [
2
2
]
.
4
.
3
.
F
ire
f
ly
a
lg
o
ri
t
hm
(
F
A)
T
h
e
f
lash
in
g
p
atter
n
s
an
d
attr
ac
tio
n
b
eh
a
v
io
r
s
ee
n
in
f
ir
ef
li
es
s
er
v
ed
as
th
e
in
s
p
ir
atio
n
f
o
r
th
e
FA,
wh
ich
Xin
-
Sh
e
Yan
g
f
ir
s
t
p
u
b
lis
h
ed
in
2
0
0
8
[
23
]
,
[
2
4
]
.
T
h
e
b
asic
o
b
jectiv
e
o
f
th
is
m
eth
o
d
is
to
id
e
n
tify
th
e
b
est
s
o
lu
tio
n
b
y
m
im
ick
i
n
g
th
e
f
lash
in
g
an
d
attr
ac
tin
g
b
eh
a
v
io
r
o
f
ea
c
h
f
ir
ef
l
y
,
wh
ich
s
y
m
b
o
lizes
a
p
o
ten
tial
s
o
lu
tio
n
.
I
t
s
h
o
ws
ef
f
icien
cy
in
d
ea
lin
g
with
is
s
u
es
wh
er
e
th
er
e
ar
e
n
u
m
er
o
u
s
lo
ca
l
o
p
t
im
a.
T
h
e
f
o
llo
win
g
s
tep
s
ar
e
a
p
ar
t
o
f
th
e
FA,
wh
ich
is
d
ep
icted
in
F
ig
u
r
e
5
(
c)
[
2
5
]
,
[
26
]
.
FA is a
p
o
we
r
f
u
l o
p
tim
izatio
n
m
eth
o
d
u
s
ed
t
o
s
o
lv
e
co
m
p
licated
p
r
o
b
lem
s
.
FA
i
s
h
ig
h
ly
ef
f
ec
tiv
e
in
s
o
lv
in
g
a
wid
e
r
an
g
e
o
f
ch
al
len
g
es
th
at
r
eq
u
ir
e
o
p
tim
izatio
n
,
s
u
ch
as
o
p
tim
izin
g
en
g
i
n
ee
r
in
g
d
esig
n
s
[
2
7
]
,
[
2
8
]
.
On
e
o
f
th
e
s
tr
en
g
th
s
o
f
th
e
FA
is
it
s
ca
p
ab
ilit
y
to
d
is
co
v
er
th
e
g
lo
b
al
o
p
tim
u
m
s
o
lu
tio
n
in
a
s
ea
r
c
h
s
p
ac
e
w
ith
m
u
lti
-
m
o
d
es [
2
9
].
(
a)
(
b
)
(
c)
Fig
u
r
e
5
.
Flo
wch
ar
t
f
o
r
p
s
eu
d
o
co
d
e
t
o
p
r
o
g
r
am
(
a)
GA,
(
b
)
PS
O,
an
d
(
c)
FA
5.
RE
SU
L
T
S AN
D
AN
AL
Y
SI
S
T
h
is
r
esear
ch
in
itialized
all
th
r
ee
o
p
tim
izatio
n
m
eth
o
d
s
with
5
s
ets
o
f
s
o
lu
tio
n
s
r
an
d
o
m
ly
d
is
tr
ib
u
ted
o
v
er
th
e
s
ea
r
ch
s
p
ac
e
with
lo
wer
an
d
h
ig
h
e
r
b
o
u
n
d
s
o
f
[
0
.
0
0
0
1
0
.
0
0
0
1
0
.
0
0
0
1
0
.
0
0
0
1
0
.
0
0
0
1
]
an
d
[
1
.
5
1
.
5
1
.
5
1
.
5
1
.
5
1
.
5
]
f
o
r
L
a,
R
a,
Kt,
J
,
f
o
,
an
d
K
b
.
T
h
r
ee
alg
o
r
ith
m
s
m
u
s
t
m
i
n
im
ize
th
e
I
AE
co
s
t
f
u
n
ctio
n
.
Simu
lin
k
m
o
d
els
ar
e
s
im
ilar
in
all
th
r
ee
tech
n
iq
u
es.
Af
ter
d
ef
in
i
n
g
al
g
o
r
ith
m
p
ar
am
ete
r
s
,
s
im
u
latio
n
s
b
eg
an
.
B
est
-
co
s
t
ad
v
an
ce
m
e
n
t a
cr
o
s
s
ea
ch
cy
cl
e
f
o
r
th
e
th
r
ee
o
p
tim
izatio
n
ca
lcu
latio
n
s
was
p
lo
tted
.
Fig
u
r
e
6
s
h
o
ws
th
e
c
o
s
t
-
v
alu
e
ev
o
lu
tio
n
f
o
r
g
e
n
etic
,
PS
O,
an
d
f
i
r
ef
ly
a
lg
o
r
ith
m
s
.
Fig
u
r
e
6
(
a)
s
h
o
ws
th
e
co
s
t
-
v
al
u
e
ev
o
lu
tio
n
f
o
r
GA
an
d
it
h
as
th
e
wo
r
s
t
b
est
-
co
s
t
an
d
tim
e
p
e
r
f
o
r
m
a
n
ce
.
Fig
u
r
es
6
(
b
)
an
d
6
(
c
)
s
h
o
w
th
at
PS
O
an
d
f
ir
ef
ly
al
g
o
r
ith
m
s
co
n
v
er
g
e
to
s
im
ilar
s
o
lu
tio
n
s
.
GA
h
as
t
h
e
wo
r
s
t
b
est
-
co
s
t
an
d
tim
e
p
er
f
o
r
m
a
n
ce
.
PS
O
ex
ce
ed
s
o
th
er
s
in
b
est
-
co
s
t
ev
o
lu
tio
n
s
p
ee
d
.
As
s
h
o
wn
a
b
o
v
e,
PS
O
r
ea
ch
es
its
lo
west
co
s
t
ar
o
u
n
d
th
e
2
7
0
th
iter
atio
n
,
wh
e
r
ea
s
FA
an
d
GA
lag
b
eh
i
n
d
.
PS
O
is
k
n
o
wn
f
o
r
its
f
ast
co
n
v
er
g
e
n
c
e
d
u
e
to
its
ef
f
icien
t
s
ea
r
ch
s
p
ac
e
ex
p
lo
r
atio
n
a
n
d
a
b
ilit
y
to
ap
p
r
o
ac
h
th
e
b
est
s
o
lu
tio
n
.
T
h
e
F
A
m
ay
n
ee
d
m
o
r
e
r
o
u
n
d
s
to
co
n
v
er
g
e,
esp
ec
ially
f
o
r
co
m
p
lex
task
s
.
Gen
etic
o
p
er
ato
r
s
m
ak
e
t
h
e
GA
co
m
p
u
tatio
n
ally
co
m
p
le
x
an
d
s
lo
w
[
3
0
]
,
[
31
]
.
T
h
e
tab
le
co
m
p
ar
es
tech
n
iq
u
es
b
ased
o
n
g
lo
b
al
b
est
co
s
t
[
3
2
]
,
DC
-
m
o
to
r
p
ar
a
m
eter
v
alu
es,
an
d
g
ain
an
d
p
h
ase
m
ar
g
in
f
r
o
m
th
e
th
r
ee
a
n
ticip
ated
m
o
d
els'
f
r
eq
u
en
cy
r
esp
o
n
s
e
esti
m
atio
n
.
Fig
u
r
e
7
d
ep
icts
b
o
d
e
p
lo
ts
o
f
t
h
e
ac
tu
al
s
y
s
tem
,
PSO
,
GA
,
an
d
F
A.
Fro
m
F
ig
u
r
e
7
,
it
ca
n
b
e
co
n
clu
d
e
d
th
at
in
s
p
ite
o
f
th
e
f
ac
t
th
at
all
f
o
u
r
DC
-
s
er
v
o
m
o
to
r
m
o
d
el
s
p
r
o
d
u
ce
d
th
e
s
am
e
tim
e
d
o
m
ain
r
esp
o
n
s
e,
th
ey
d
o
n
’
t
a
p
p
ea
r
to
h
av
e
t
h
e
s
am
e
f
r
eq
u
e
n
cy
r
esp
o
n
s
e.
B
y
co
m
p
ar
in
g
th
e
g
ain
m
a
r
g
in
s
an
d
p
h
ase
m
ar
g
in
s
o
f
t
h
e
m
o
d
els,
it
is
s
ee
n
th
at
th
ey
ar
e
s
tab
le
in
a
clo
s
ed
lo
o
p
in
all
th
e
m
o
d
els.
T
ab
le
1
g
iv
es
a
co
m
p
ar
is
o
n
o
f
d
if
f
er
en
t
ca
lcu
latio
n
s
b
ased
o
n
t
h
e
b
est
co
s
t
f
etch
ed
,
v
al
u
es
o
f
DC
-
m
o
to
r
p
ar
am
eter
s
,
a
n
d
t
h
e
f
r
eq
u
en
cy
r
esp
o
n
s
e
g
ai
n
m
ar
g
in
s
o
f
t
h
e
th
r
ee
m
o
d
e
ls
al
o
n
g
with
th
e
ac
tu
al
s
y
s
te
m.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
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n
g
I
SS
N:
2252
-
8
7
9
2
Meta
h
eu
r
is
tic
a
lg
o
r
ith
ms fo
r
p
a
r
a
mete
r
esti
ma
tio
n
o
f D
C
s
ervo
mo
to
r
s
…
(
Deb
a
n
i P
r
a
s
a
d
Mis
h
r
a
)
105
(
a)
(
b
)
(
c)
Fig
u
r
e
6
.
T
h
e
co
s
t
-
v
alu
e
ev
o
lu
tio
n
f
o
r
(
a)
g
en
etic,
(
b
)
PS
O,
an
d
(
c)
f
ir
ef
ly
a
l
g
o
r
ith
m
s
Fig
u
r
e
7
.
B
o
d
e
p
lo
ts
o
f
th
e
ac
t
u
al
s
y
s
tem
,
PSO
,
GA
,
an
d
F
A
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
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n
t J Ap
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g
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l.
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4
,
No
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1
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Ma
r
ch
20
2
5
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01
-
1
0
8
106
T
ab
le
1
.
Simu
latio
n
r
esu
lts
A
l
g
o
r
i
t
h
m
PSO
GA
F
i
r
e
f
l
y
a
l
g
o
r
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t
h
m
A
c
t
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l
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y
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B
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2
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8
7
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2
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g
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7
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8
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1
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2
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P
h
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se
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2
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g
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9
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e
g
2
4
d
e
g
2
3
.
7
d
e
g
6.
CO
NCLU
SI
O
N
E
f
f
ec
tiv
e
o
p
tim
izatio
n
m
eth
o
d
f
ir
ef
ly
a
lg
o
r
ith
m
s
o
lv
es
co
m
p
lex
is
s
u
es.
A
well
-
p
lan
n
ed
p
r
o
ce
s
s
wi
th
i
n
itializatio
n
:
a
s
war
m
o
f
f
ir
e
f
lies
r
ep
r
esen
ts
s
ea
r
ch
s
p
ac
e
s
o
lu
tio
n
s
in
th
e
alg
o
r
ith
m
.
Fire
f
lies
ar
e
r
a
n
d
o
m
ly
p
lace
d
in
th
is
s
p
ac
e
an
d
g
iv
en
f
itn
ess
v
alu
es
r
ef
lectin
g
o
p
tim
izatio
n
ef
f
icien
cy
.
T
h
is
f
itn
ess
v
alu
e
b
eg
in
s
with
th
e
f
ir
ef
ly
p
o
s
itio
n
.
Fire
f
ly
f
it
n
ess
test
in
g
is
ess
en
tial.
Ded
i
ca
ted
f
itn
ess
f
u
n
ctio
n
s
ev
alu
a
te
f
ir
ef
ly
s
o
lu
tio
n
s
.
Ho
w
well
th
e
f
ir
ef
ly
'
s
lo
ca
tio
n
f
its
p
r
o
b
lem
g
o
als
is
ass
ess
ed
b
y
th
is
f
u
n
ctio
n
.
A
n
u
m
er
ical
s
co
r
e
s
h
o
ws
f
ir
ef
ly
's
f
itn
ess
an
d
p
e
r
f
o
r
m
an
ce
.
Fire
f
ly
b
ea
u
t
y
d
e
p
en
d
s
o
n
lu
m
in
o
s
ity
an
d
f
itn
ess
.
Sh
in
y
f
ir
ef
lies
n
atu
r
ally
p
u
ll
th
eir
s
war
m
m
ates
h
ar
d
er
.
Fire
f
lies
attr
ac
t
ea
ch
o
th
er
v
ia
d
is
tan
ce
an
d
b
r
ig
h
tn
ess
.
Fire
f
lies
'
b
r
ig
h
tn
ess
attr
ac
ts
p
eo
p
le.
T
h
e
m
o
s
t
g
o
r
g
eo
u
s
f
ir
ef
ly
at
tr
ac
ts
f
ir
ef
lies
.
Attr
ac
tio
n
r
atin
g
,
w
h
ich
c
o
n
s
id
er
s
b
r
i
g
h
tn
e
s
s
an
d
in
ter
-
f
ir
ef
l
y
d
is
tan
ce
,
in
f
lu
en
ce
s
th
is
m
o
v
em
en
t.
Fire
f
lies
n
atu
r
ally
ap
p
r
o
ac
h
th
e
m
o
s
t
ap
p
ea
lin
g
o
n
e
s
.
Fire
f
lies
ca
n
also
b
r
ig
h
ten
t
o
attr
ac
t
s
war
m
s
.
R
ep
ea
t
f
itn
ess
ev
alu
atio
n
,
attr
ac
ti
o
n
,
an
d
m
o
v
em
en
t
till
h
altin
g
.
T
h
is
iter
atio
n
h
elp
s
th
e
al
g
o
r
ith
m
f
in
d
o
p
tim
al
s
o
lu
tio
n
s
.
T
h
e
f
ir
ef
l
y
alg
o
r
ith
m
o
p
tim
izes
co
m
p
lex
p
r
o
b
le
m
s
u
tili
zin
g
th
ese
m
im
ick
ed
f
ir
ef
lies
’
c
o
llectiv
e
in
tellig
en
ce
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
is
r
esear
ch
wo
r
k
was su
p
p
o
r
ted
b
y
“
W
o
o
s
o
n
g
Un
iv
e
r
s
ity
’
s
Aca
d
em
ic
R
esear
ch
Fu
n
d
in
g
-
202
4
”
.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
K
a
r
a
mu
k
a
n
d
O
.
B
.
A
l
a
n
k
u
s,
“
D
e
v
e
l
o
p
me
n
t
a
n
d
E
x
p
e
r
i
m
e
n
t
a
l
I
mp
l
e
m
e
n
t
a
t
i
o
n
o
f
A
c
t
i
v
e
Ti
l
t
C
o
n
t
r
o
l
S
y
st
e
m
U
si
n
g
a
S
e
r
v
o
M
o
t
o
r
A
c
t
u
a
t
o
r
f
o
r
N
a
r
r
o
w
T
i
l
t
i
n
g
E
l
e
c
t
r
i
c
V
e
h
i
c
l
e
,
”
E
n
e
r
g
i
e
s
,
v
o
l
.
1
5
,
n
o
.
6
,
p
.
1
9
9
6
,
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
e
n
1
5
0
6
1
9
9
6
.
[
2
]
D
.
N
a
t
a
l
i
a
n
a
,
R
.
S
y
a
f
r
u
d
d
i
n
,
G
.
D
e
v
i
r
a
R
a
ma
d
y
,
Y
.
Li
k
l
i
k
w
a
t
i
l
,
a
n
d
A
.
G
h
e
a
M
a
h
a
r
d
i
k
a
,
“
S
e
r
v
o
C
o
n
t
r
o
l
f
o
r
M
i
ss
i
l
e
S
y
st
e
m,
”
J
o
u
rn
a
l
o
f
Ph
y
si
c
s:
C
o
n
f
e
re
n
c
e
S
e
ri
e
s
,
v
o
l
.
1
4
2
4
,
n
o
.
1
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
8
8
/
1
7
4
2
-
6
5
9
6
/
1
4
2
4
/
1
/
0
1
2
0
4
0
.
[
3
]
X
.
M
u
,
F
.
C
a
i
,
R
.
Z
h
e
n
g
,
D
.
Z
h
a
n
g
,
a
n
d
D
.
G
u
,
“
A
P
r
e
d
i
c
t
i
v
e
C
u
r
r
e
n
t
C
o
n
t
r
o
l
f
o
r
A
e
r
o
s
p
a
c
e
S
e
r
v
o
M
o
t
o
r
,
”
i
n
Pr
o
c
e
e
d
i
n
g
s
-
2
0
2
1
3
rd
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
A
p
p
l
i
e
d
M
a
c
h
i
n
e
L
e
a
r
n
i
n
g
,
I
C
A
ML
2
0
2
1
,
2
0
2
1
,
p
p
.
3
6
6
–
3
6
9
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
A
M
L
5
4
3
1
1
.
2
0
2
1
.
0
0
0
8
4
.
[
4
]
L.
Lj
u
n
g
,
“
S
y
s
t
e
m
I
d
e
n
t
i
f
i
c
a
t
i
o
n
,
”
i
n
S
i
g
n
a
l
a
n
a
l
y
s
i
s
a
n
d
p
re
d
i
c
t
i
o
n
,
B
o
s
t
o
n
,
M
A
,
U
S
A
:
B
i
r
k
h
ä
u
s
e
r
B
o
s
t
o
n
,
1
9
9
8
,
p
p
.
163
–
1
7
3
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
1
-
4
6
1
2
-
1
7
6
8
-
8
_
1
1
.
[
5
]
M
.
J
i
r
g
l
,
L
.
O
b
s
i
l
o
v
a
,
J
.
B
o
r
i
l
,
a
n
d
R
.
Jal
o
v
e
c
k
y
,
“
P
a
r
a
me
t
e
r
i
d
e
n
t
i
f
i
c
a
t
i
o
n
f
o
r
p
i
l
o
t
b
e
h
a
v
i
o
u
r
m
o
d
e
l
u
si
n
g
t
h
e
M
A
TLA
B
sy
s
t
e
m
i
d
e
n
t
i
f
i
c
a
t
i
o
n
t
o
o
l
b
o
x
,
”
i
n
I
C
MT
2
0
1
7
-
6
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
Mi
l
i
t
a
ry
T
e
c
h
n
o
l
o
g
i
e
s
,
2
0
1
7
,
p
p
.
5
8
2
–
5
8
7
,
d
o
i
:
1
0
.
1
1
0
9
/
M
I
LTEC
H
S
.
2
0
1
7
.
7
9
8
8
8
2
4
.
[
6
]
S
.
R
.
S
a
l
k
u
t
i
,
“
Emer
g
i
n
g
a
n
d
A
d
v
a
n
c
e
d
G
r
e
e
n
E
n
e
r
g
y
Te
c
h
n
o
l
o
g
i
e
s f
o
r
S
u
st
a
i
n
a
b
l
e
a
n
d
R
e
si
l
i
e
n
t
F
u
t
u
r
e
G
r
i
d
,
”
En
e
r
g
i
e
s
,
v
o
l
.
1
5
,
n
o
.
1
8
,
p
.
6
6
6
7
,
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
e
n
1
5
1
8
6
6
6
7
.
[
7
]
R
.
I
serm
a
n
n
a
n
d
M
.
M
ü
n
c
h
h
o
f
,
I
d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
d
y
n
a
m
i
c
sys
t
e
m
s:
A
n
i
n
t
r
o
d
u
c
t
i
o
n
w
i
t
h
a
p
p
l
i
c
a
t
i
o
n
s
,
G
e
r
m
a
n
y
:
S
p
r
i
n
g
e
r
B
e
r
l
i
n
,
H
e
i
d
e
l
b
e
r
g
,
2
0
1
1
,
p
p
.
3
7
9
–
4
0
8
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
5
4
0
-
7
8
8
7
9
-
9.
[
8
]
J.
W
a
n
g
a
n
d
A
.
B
o
u
k
e
r
c
h
e
,
“
N
o
n
-
p
a
r
a
met
r
i
c
m
o
d
e
l
s
w
i
t
h
o
p
t
i
mi
z
e
d
t
r
a
i
n
i
n
g
st
r
a
t
e
g
y
f
o
r
v
e
h
i
c
l
e
s
t
r
a
f
f
i
c
f
l
o
w
p
r
e
d
i
c
t
i
o
n
,
”
C
o
m
p
u
t
e
r
N
e
t
w
o
rks
,
v
o
l
.
1
8
7
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
m
n
e
t
.
2
0
2
0
.
1
0
7
7
9
1
.
[
9
]
A
.
M
.
H
u
ma
d
a
e
t
a
l
.
,
“
M
o
d
e
l
i
n
g
o
f
P
V
sy
st
e
m
a
n
d
p
a
r
a
me
t
e
r
e
x
t
r
a
c
t
i
o
n
b
a
se
d
o
n
e
x
p
e
r
i
m
e
n
t
a
l
d
a
t
a
:
R
e
v
i
e
w
a
n
d
i
n
v
e
st
i
g
a
t
i
o
n
,
”
S
o
l
a
r E
n
e
r
g
y
,
v
o
l
.
1
9
9
,
p
p
.
7
4
2
–
7
6
0
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
so
l
e
n
e
r
.
2
0
2
0
.
0
2
.
0
6
8
.
[
1
0
]
S
.
M
.
A
.
A
l
t
b
a
w
i
e
t
a
l
.
,
“
A
n
I
mp
r
o
v
e
d
G
r
a
d
i
e
n
t
-
B
a
se
d
O
p
t
i
m
i
z
a
t
i
o
n
A
l
g
o
r
i
t
h
m
f
o
r
S
o
l
v
i
n
g
C
o
mp
l
e
x
O
p
t
i
m
i
z
a
t
i
o
n
P
r
o
b
l
e
ms,
”
Pro
c
e
sses
,
v
o
l
.
1
1
,
n
o
.
2
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
p
r
1
1
0
2
0
4
9
8
.
[
1
1
]
Z.
G
u
,
G
.
X
i
o
n
g
,
a
n
d
X
.
F
u
,
“
P
a
r
a
me
t
e
r
E
x
t
r
a
c
t
i
o
n
o
f
S
o
l
a
r
P
h
o
t
o
v
o
l
t
a
i
c
C
e
l
l
a
n
d
M
o
d
u
l
e
M
o
d
e
l
s
w
i
t
h
M
e
t
a
h
e
u
r
i
st
i
c
A
l
g
o
r
i
t
h
ms
:
A
R
e
v
i
e
w
,
”
S
u
st
a
i
n
a
b
i
l
i
t
y
,
v
o
l
.
1
5
,
n
o
.
4
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
su
1
5
0
4
3
3
1
2
.
[
1
2
]
A
.
K
u
m
a
r
,
G
.
W
u
,
M
.
Z
.
A
l
i
,
R
.
M
a
l
l
i
p
e
d
d
i
,
P
.
N
.
S
u
g
a
n
t
h
a
n
,
a
n
d
S
.
D
a
s
,
“
A
t
e
st
-
s
u
i
t
e
o
f
n
o
n
-
c
o
n
v
e
x
c
o
n
s
t
r
a
i
n
e
d
o
p
t
i
m
i
z
a
t
i
o
n
p
r
o
b
l
e
ms
f
r
o
m
t
h
e
r
e
a
l
-
w
o
r
l
d
a
n
d
so
me
b
a
se
l
i
n
e
r
e
s
u
l
t
s,
”
S
w
a
rm
a
n
d
E
v
o
l
u
t
i
o
n
a
ry
C
o
m
p
u
t
a
t
i
o
n
,
v
o
l
.
5
6
,
2
0
2
0
,
d
o
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v
e
lo
p
m
e
n
t
.
He
c
a
n
b
e
c
o
n
tac
ted
at
e
m
a
il
:
a
ru
lk
u
m
a
rd
a
sh
8
9
@
g
m
a
il
.
c
o
m
.
Pra
jn
a
J
e
e
t
O
jh
a
is
c
u
rre
n
tl
y
p
u
rsu
i
n
g
B.
Tec
h
.
d
e
g
re
e
i
n
e
lec
tri
c
a
l
a
n
d
e
lec
tro
n
ics
e
n
g
i
n
e
e
rin
g
a
t
th
e
In
tern
a
ti
o
n
a
l
I
n
stit
u
te
o
f
In
fo
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
,
Bh
u
b
a
n
e
sw
a
r,
Od
ish
a
,
I
n
d
ia
(
b
a
tc
h
2
0
2
1
-
2
0
2
5
).
D
u
rin
g
h
is
a
c
a
d
e
m
ic
jo
u
r
n
e
y
,
h
e
h
a
s
a
c
ti
v
e
ly
e
n
g
a
g
e
d
i
n
re
se
a
rc
h
in
t
h
e
fi
e
ld
o
f
b
lo
c
k
c
h
a
in
tec
h
n
o
l
o
g
y
a
n
d
we
b
d
e
v
e
lo
p
m
e
n
t
.
F
u
rth
e
rm
o
re
,
h
is
a
c
h
iev
e
m
e
n
ts
in
c
l
u
d
e
a
u
th
o
ri
n
g
a
n
o
tew
o
rt
h
y
c
o
n
fe
re
n
c
e
p
a
p
e
r
o
n
a
d
v
a
n
c
in
g
In
d
ian
a
g
ric
u
lt
u
re
t
h
r
o
u
g
h
d
e
c
e
n
tralize
d
b
lo
c
k
c
h
a
in
c
r
o
p
i
n
su
ra
n
c
e
.
He
c
a
n
b
e
c
o
n
tac
ted
at
e
m
a
il
:
p
ra
j
n
a
jee
t0
2
@g
m
a
il
.
c
o
m
.
S
u
r
e
n
d
e
r
Re
d
d
y
S
a
lk
u
ti
re
c
e
iv
e
d
t
h
e
P
h
.
D.
d
e
g
re
e
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
ri
n
g
fro
m
th
e
In
d
ian
I
n
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
,
Ne
w
De
lh
i,
I
n
d
ia,
in
2
0
1
3
.
He
wa
s
a
p
o
std
o
c
to
ra
l
re
se
a
rc
h
e
r
at
Ho
wa
rd
Un
iv
e
rsity
,
Was
h
in
g
to
n
,
DC,
USA,
fro
m
2
0
1
3
t
o
2
0
1
4
.
He
is
c
u
rre
n
tl
y
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
at
th
e
De
p
a
rtme
n
t
o
f
Ra
il
r
o
a
d
a
n
d
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Wo
o
so
n
g
Un
iv
e
rsity
,
Da
e
jeo
n
,
S
o
u
th
Ko
re
a
.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
m
a
rk
e
t
c
lea
rin
g
,
in
c
lu
d
in
g
re
n
e
wa
b
le
e
n
e
rg
y
so
u
rc
e
s,
d
e
m
a
n
d
re
sp
o
n
se
,
a
n
d
sm
a
rt
g
rid
d
e
v
e
lo
p
m
e
n
t
wit
h
th
e
in
teg
ra
ti
o
n
o
f
win
d
a
n
d
s
o
lar
p
h
o
to
v
o
lt
a
ic
e
n
e
rg
y
so
u
rc
e
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
su
re
n
d
e
r@ws
u
.
a
c
.
k
r
.
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