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Mar
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2
0
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In
th
is
p
a
p
e
r,
M
in
e
Blas
t
Alg
o
rit
h
m
(M
BA)
h
a
s
b
e
e
n
in
term
i
n
g
led
wit
h
Ha
rm
o
n
y
S
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rc
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a
lg
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rit
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m
fo
r
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n
g
o
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ti
m
a
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re
a
c
ti
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e
p
o
we
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d
isp
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tch
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ro
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lem
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BA
is
b
a
se
d
o
n
e
x
p
lo
si
o
n
o
f
la
n
d
m
in
e
s
a
n
d
HS
is
b
a
se
d
o
n
Cr
e
a
ti
v
e
n
e
ss
p
ro
g
re
ss
io
n
o
f
m
u
sic
ian
s
-
b
o
th
a
re
h
y
b
rid
ize
d
to
so
lv
e
th
e
p
ro
b
lem
.
In
M
BA
I
n
it
ial
d
istan
c
e
o
f
sh
ra
p
n
e
l
p
iec
e
s
a
re
re
d
u
c
e
d
g
r
a
d
u
a
ll
y
t
o
a
ll
o
w
th
e
m
in
e
b
o
m
b
s
se
a
rc
h
th
e
p
ro
b
a
b
le
g
l
o
b
a
l
m
i
n
imu
m
lo
c
a
ti
o
n
in
o
rd
e
r
to
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m
p
li
fy
t
h
e
g
lo
b
a
l
e
x
p
l
o
re
c
a
p
a
b
i
li
t
y
.
Ha
rm
o
n
y
se
a
rc
h
(HS)
imitate
s
th
e
m
u
sic
c
re
a
ti
v
it
y
p
ro
c
e
ss
wh
e
re
th
e
m
u
sic
ian
s
su
p
e
rv
ise
th
e
ir
in
stru
m
e
n
ts’
p
it
c
h
b
y
se
a
rc
h
in
g
f
o
r
a
b
e
st
sta
te
o
f
h
a
rm
o
n
y
.
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b
r
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iza
ti
o
n
o
f
M
in
e
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t
Alg
o
rit
h
m
with
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rm
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rc
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a
lg
o
r
it
h
m
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H)
imp
ro
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e
s
t
h
e
se
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ef
fe
c
ti
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ly
i
n
t
h
e
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lu
ti
o
n
sp
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c
e
.
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in
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b
las
t
a
l
g
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ri
th
m
imp
ro
v
e
s
t
h
e
e
x
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lo
ra
ti
o
n
a
n
d
h
a
rm
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se
a
rc
h
a
lg
o
rit
h
m
a
u
g
m
e
n
ts
t
h
e
e
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p
l
o
it
a
ti
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n
.
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first
th
e
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ro
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o
se
d
a
lg
o
rit
h
m
sta
rts
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th
e
x
p
l
o
ra
ti
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n
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ra
d
u
a
ll
y
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t
m
o
v
e
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h
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se
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e
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ti
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n
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r
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p
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se
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ri
d
ize
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i
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e
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t
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th
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h
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rc
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rit
h
m
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H)
h
a
s
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e
n
tes
ted
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n
sta
n
d
a
r
d
I
EE
E
1
4
,
3
0
0
b
u
s
tes
t
sy
ste
m
s.
Re
a
l
p
o
we
r
lo
ss
h
a
s
b
e
e
n
re
d
u
c
e
d
c
o
n
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e
ra
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ly
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y
t
h
e
p
ro
p
o
se
d
a
l
g
o
ri
th
m
.
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h
e
n
Hy
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r
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d
ize
d
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n
e
Blas
t
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g
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rit
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th
m
(
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n
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E
3
0
,
b
u
s
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ste
m
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h
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g
v
o
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g
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sta
b
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it
y
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n
d
e
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)
-
re
a
l
p
o
we
r
lo
ss
m
in
imiz
a
ti
o
n
,
v
o
lt
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g
e
d
e
v
iatio
n
m
in
imiz
a
ti
o
n
,
a
n
d
v
o
lt
a
g
e
sta
b
il
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y
in
d
e
x
e
n
h
a
n
c
e
m
e
n
t
h
a
s
b
e
e
n
a
tt
a
in
e
d
.
K
ey
w
o
r
d
s
:
Har
m
o
n
y
s
ea
r
ch
o
p
tim
al
r
ea
ctiv
e
p
o
wer
,
Min
e
b
last
,
T
r
an
s
m
is
s
io
n
lo
s
s
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
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SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Kan
ag
asab
ai
L
en
in
,
Dep
ar
tm
en
t o
f
E
E
E
,
Pra
s
ad
V.
Po
tlu
r
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d
h
ar
th
a
I
n
s
titu
te
o
f
T
ec
h
n
o
lo
g
y
,
Kan
u
r
u
,
Vijay
awa
d
a
,
An
d
h
r
a
Pra
d
esh
-
5
2
0
0
0
7
,
I
n
d
ia.
E
m
ail:
g
k
len
in
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
I
n
th
is
wo
r
k
th
e
k
ey
o
b
jectiv
e
is
Actu
al
p
o
wer
lo
s
s
r
ed
u
ctio
n
.
Op
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
h
as
b
ee
n
s
o
lv
e
d
b
y
a
v
a
r
iety
o
f
m
eth
o
d
s
[
1
-
6
]
.
Ho
wev
e
r
,
m
an
y
tech
n
ical
h
itch
es
a
r
e
f
o
u
n
d
wh
ile
s
o
lv
in
g
p
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o
b
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d
u
e
to
an
ass
o
r
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en
t
o
f
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n
s
tr
ain
ts
.
E
v
o
lu
tio
n
ar
y
tech
n
iq
u
e
s
[
7
-
1
8
]
ar
e
ap
p
lied
to
s
o
lv
e
t
h
e
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
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b
u
t
th
e
k
e
y
p
r
o
b
le
m
is
s
o
m
e
alg
o
r
ith
m
s
s
tu
ck
i
n
lo
ca
l
o
p
tim
al
s
o
lu
tio
n
&
f
a
iled
to
b
alan
ce
th
e
E
x
p
lo
r
atio
n
&
E
x
p
lo
itatio
n
d
u
r
in
g
th
e
s
ea
r
ch
o
f
g
lo
b
al
s
o
l
u
tio
n
.
I
n
th
is
p
ap
e
r
,
Min
e
B
la
s
t
Alg
o
r
ith
m
(
MBA)
h
as
b
ee
n
in
ter
m
in
g
led
with
Har
m
o
n
y
Sear
ch
(
HS)
alg
o
r
ith
m
f
o
r
s
o
lv
in
g
o
p
tim
al
r
ea
ctiv
e
p
o
wer
d
is
p
atch
p
r
o
b
lem
.
MBA
is
b
ased
o
n
ex
p
lo
s
io
n
o
f
lan
d
m
i
n
es
an
d
HS
is
b
ased
o
n
C
r
ea
tiv
e
n
ess
p
r
o
g
r
ess
io
n
o
f
m
u
s
ician
s
–
b
o
th
ar
e
h
y
b
r
id
ize
d
to
s
o
lv
e
th
e
p
r
o
b
lem
.
Mo
r
e
f
ir
s
t
s
h
o
t
p
o
in
ts
ar
e
u
s
ed
a
n
d
it
will
in
cr
ea
s
e
th
e
in
itial
p
o
p
u
latio
n
.
I
t
c
o
n
s
eq
u
e
n
tly
in
cr
ea
s
es
th
e
n
u
m
b
e
r
o
f
f
u
n
ctio
n
ev
alu
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n
s
an
d
th
e
e
x
is
tin
g
lo
ca
tio
n
o
f
a
m
in
e
b
o
m
b
.
I
n
o
r
d
e
r
to
ac
co
m
p
lis
h
u
n
v
ar
y
in
g
s
ea
r
ch
in
th
e
d
o
m
ain
s
p
ac
e
th
e
v
alu
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o
f
is
s
et
b
y
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s
an
d
th
r
o
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g
h
th
is
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ass
in
g
o
f
in
d
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id
u
als
in
a
s
p
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if
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eg
io
n
o
f
th
e
ar
ea
s
ea
r
ch
ca
n
b
e
p
r
ev
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ted
.
Hy
b
r
id
ized
Min
e
B
last
Alg
o
r
it
h
m
with
Har
m
o
n
y
Sear
ch
alg
o
r
ith
m
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M
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im
p
r
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th
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s
ea
r
ch
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f
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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2
2
5
2
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8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
9
,
No
.
2,
Au
g
u
s
t
20
20
:
83
–
9
1
84
s
p
ac
e.
Min
e
b
last
alg
o
r
ith
m
im
p
r
o
v
es
th
e
ex
p
lo
r
atio
n
an
d
h
a
r
m
o
n
y
s
ea
r
ch
al
g
o
r
it
h
m
au
g
m
en
ts
th
e
ex
p
lo
itatio
n
.
At
f
ir
s
t
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
tar
ts
with
ex
p
lo
r
atio
n
&
g
r
a
d
u
ally
it
m
o
v
es
to
th
e
p
h
ase
o
f
ex
p
lo
itatio
n
.
Pr
o
p
o
s
ed
Hy
b
r
i
d
ized
Min
e
B
last
Alg
o
r
ith
m
with
Har
m
o
n
y
Sear
ch
al
g
o
r
it
h
m
(
MH
)
h
as
b
ee
n
test
ed
o
n
s
tan
d
ar
d
I
E
E
E
1
4
,
3
0
0
b
u
s
test
s
y
s
tem
s
.
R
ea
l
p
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wer
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s
s
h
as
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ee
n
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ed
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ce
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c
o
n
s
id
er
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ly
b
y
th
e
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r
o
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o
s
ed
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o
r
ith
m
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h
e
n
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r
id
ized
Min
e
B
last
Alg
o
r
ith
m
with
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m
o
n
y
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ch
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o
r
ith
m
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MH
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test
ed
i
n
I
E
E
E
3
0
,
b
u
s
s
y
s
tem
(
with
co
n
s
id
er
in
g
v
o
lta
g
e
s
tab
ilit
y
in
d
e
x
)
-
r
ea
l
p
o
wer
lo
s
s
m
i
n
im
izatio
n
,
v
o
ltag
e
d
ev
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n
m
in
im
izatio
n
,
an
d
v
o
ltag
e
s
tab
ilit
y
in
d
ex
e
n
h
an
ce
m
en
t h
as b
ee
n
at
tain
e
d
.
2.
P
RO
B
L
E
M
F
O
R
M
U
L
AT
I
O
N
Ob
jectiv
e
o
f
th
e
p
r
o
b
lem
i
s
to
r
ed
u
ce
th
e
t
r
u
e
p
o
wer
lo
s
s
F
=
P
L
=
∑
g
k
k
∈
Nbr
(
V
i
2
+
V
j
2
−
2
V
i
V
j
c
os
θ
ij
)
(
1
)
Vo
l
tag
e
d
ev
iatio
n
g
iv
en
as f
o
l
lo
ws
F
=
P
L
+
ω
v
×
Vol
ta
ge
De
via
tion
(
2
)
Vo
ltag
e
d
ev
iatio
n
g
iv
en
b
y
:
Vol
ta
ge
De
via
tion
=
∑
|
V
i
−
1
|
N
p
q
i
=
1
(
3
)
C
o
n
s
tr
a
in
t (
E
q
u
a
lity)
P
G
=
P
D
+
P
L
(
4
)
C
o
n
s
tr
a
in
ts
(
I
n
eq
u
a
lity)
P
g
s
l
ack
m
i
n
≤
P
g
s
l
ack
≤
P
g
s
l
ac
k
m
ax
(
5
)
Q
gi
m
i
n
≤
Q
gi
≤
Q
gi
m
ax
,
i
∈
N
g
(
6
)
V
i
m
i
n
≤
V
i
≤
V
i
m
ax
,
i
∈
N
(
7
)
T
i
m
i
n
≤
T
i
≤
T
i
m
ax
,
i
∈
N
T
(
8
)
Q
c
m
i
n
≤
Q
c
≤
Q
C
m
ax
,
i
∈
N
C
(
9
)
3.
M
I
N
E
B
L
AS
T
A
L
G
O
RIT
H
M
E
x
am
in
atio
n
o
f
a
m
in
e
b
o
m
b
ex
p
lo
s
io
n
is
im
itated
to
d
esig
n
th
e
m
in
e
b
last
alg
o
r
ith
m
[
1
9
-
2
0
]
.
Nu
m
b
er
o
f
s
h
r
ap
n
el
p
iece
s
(
N
s
)
is
co
n
s
id
er
ed
as
th
e
th
e
n
u
m
b
er
o
f
in
itial
p
o
p
u
latio
n
(
N
pop
)
.
First
s
h
o
t
p
o
in
t
v
alu
e
is
g
en
er
ated
b
y
a
d
im
in
u
ti
v
e
ar
b
itra
r
ily
g
en
er
ated
v
alu
e
g
iv
en
as:
0
=
+
×
(
−
)
(
1
0
)
Mo
r
e
f
ir
s
t
s
h
o
t
p
o
in
ts
ar
e
u
s
e
d
an
d
it
will
i
n
cr
ea
s
e
th
e
in
iti
al
p
o
p
u
latio
n
.
I
t
c
o
n
s
eq
u
e
n
tly
in
cr
ea
s
es
th
e
n
u
m
b
er
o
f
f
u
n
ctio
n
ev
al
u
a
tio
n
s
an
d
th
e
e
x
is
tin
g
lo
ca
tio
n
o
f
a
m
in
e
b
o
m
b
g
iv
e
n
as:
Y
=
{Y
m
},
m
=
1
,
2
,
3
,
.
.
.
,
N
d
(
1
1
)
Delib
er
ately
N
s
s
h
r
ap
n
el
p
iec
es
ar
e
cr
ea
te
d
b
y
t
h
e
m
i
n
e
b
o
m
b
e
x
p
lo
s
io
n
is
th
e
s
o
u
r
ce
f
o
r
a
n
o
th
e
r
m
in
e
to
b
lo
w
u
p
at
Yn
+
1
p
o
s
it
io
n
,
+
1
=
(
+
1
)
+
(
−
√
+
1
+
1
)
,
=
0
,
1
,
2
,
3
,
.
(
1
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Tr
u
e
p
o
w
er lo
s
s
r
ed
u
ctio
n
b
y
a
u
g
men
ted
min
e
b
la
s
t a
lg
o
r
ith
m
(
K
a
n
a
g
a
s
a
b
a
i Len
in
)
85
E
x
p
lo
d
in
g
m
in
e
b
o
m
b
lo
ca
tio
n
(
+
1
)
is
d
ef
in
ed
as:
(
+
1
)
=
×
×
(
)
,
=
0
,
1
,
2
,
.
.
(
1
3
)
I
n
o
r
d
er
to
ac
c
o
m
p
lis
h
u
n
v
ar
y
in
g
s
ea
r
ch
in
th
e
d
o
m
ain
s
p
ac
e
th
e
v
al
u
e
o
f
is
s
et
b
y
3
6
0
/N
s
an
d
th
r
o
u
g
h
th
is
am
ass
in
g
o
f
i
n
d
iv
i
d
u
als in
a
s
p
ec
if
ic
r
eg
io
n
o
f
th
e
ar
ea
s
ea
r
ch
ca
n
b
e
p
r
ev
e
n
ted
.
Dir
ec
tio
n
o
f
s
h
r
a
p
n
el
p
iece
s
+
1
an
d
d
is
tan
ce
+
1
ar
e
d
ef
in
e
d
as:
+
1
=
√
(
+
1
−
)
2
+
(
+
1
−
)
2
=
0
,
1
,
2
,
.
.
(
1
4
)
+
1
=
+
1
−
+
1
−
=
0
,
1
,
2
,
…
(
1
5
)
I
n
th
e
s
o
lu
tio
n
s
p
ac
e
ex
p
l
o
r
ati
o
n
is
d
o
n
e
b
y
:
+
1
=
×
(
|
|
)
2
=
0
,
1
,
2
,
.
.
(
1
6
)
(
+
1
)
=
×
(
)
,
=
0
,
1
,
2
,
.
.
(
1
7
)
I
n
itial
d
is
tan
ce
o
f
s
h
r
a
p
n
el
p
iece
s
ar
e
r
e
d
u
ce
d
g
r
ad
u
ally
to
allo
w
th
e
m
in
e
b
o
m
b
s
s
ea
r
ch
th
e
p
r
o
b
a
b
le
g
lo
b
al
m
in
im
u
m
lo
c
atio
n
in
o
r
d
e
r
to
am
p
lify
th
e
g
lo
b
al
ex
p
l
o
r
e
ca
p
ab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
R
ed
u
ctio
n
in
0
is
g
iv
en
as:
=
−
1
(
⁄
)
=
1
,
2
,
3
,
.
.
(
1
8
)
E
x
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
p
r
o
g
r
ess
io
n
is
g
iv
en
as b
elo
w:
If
> k
Exploration
+
1
=
×
(
|
|
)
2
=
0
,
1
,
2
,
.
.
(
+
1
)
=
×
(
)
,
=
0
,
1
,
2
,
.
.
Else
Exploitation
(
+
1
)
=
×
×
(
)
,
=
0
,
1
,
2
,
.
+
1
=
√
(
+
1
−
)
2
+
(
+
1
−
)
2
=
0
,
1
,
2
,
.
.
+
1
=
+
1
−
+
1
−
=
0
,
1
,
2
,
…
=
−
1
(
⁄
)
=
1
,
2
,
3
,
.
.
End
a.
I
n
itializatio
n
o
f
p
a
r
am
eter
s
b.
C
o
n
d
itio
n
o
f
ex
p
lo
r
atio
n
f
ac
t
o
r
(
)
is
ch
ec
k
ed
c.
C
alcu
late
th
e
d
is
tan
ce
o
f
s
h
r
ap
n
el
p
iece
s
an
d
th
eir
lo
ca
tio
n
s
b
y
(
1
6
)
an
d
(
1
7
)
o
n
ce
t
h
e
co
n
d
itio
n
o
f
ex
p
lo
r
atio
n
f
ac
to
r
is
s
atis
f
ied
if
n
o
t g
o
to
Step
i.
d.
Dir
ec
tio
n
o
f
s
h
r
a
p
n
el
p
iece
s
ar
e
ca
lcu
lated
b
y
(
1
5
)
.
e.
Sh
r
ap
n
el
p
iece
s
ar
e
p
r
o
d
u
ce
d
an
d
th
eir
im
p
r
o
v
ed
lo
ca
tio
n
s
ar
e
ca
lcu
lated
b
y
(
1
2
)
.
f.
Fo
r
en
g
en
d
er
ed
s
h
r
ap
n
el
p
iece
s
co
n
s
tr
ain
ts
lim
its
ar
e
c
h
ec
k
ed
.
g.
B
est s
h
r
ap
n
el
p
iece
is
s
av
ed
as th
e
b
est s
eq
u
en
tial so
lu
tio
n
.
h.
I
f
f
u
n
ctio
n
v
alu
e
th
an
th
e
b
e
s
t
tem
p
o
r
al
s
o
lu
tio
n
is
g
r
ea
ter
th
an
th
e
s
h
r
ap
n
el
p
iece
?
I
f
tr
u
e,
s
wap
th
e
p
o
s
itio
n
o
f
t
h
e
s
h
r
ap
n
el
p
iece
with
th
e
b
est tem
p
o
r
al
s
o
lu
tio
n
.
I
f
n
o
t g
o
to
Step
i.
i.
Dis
tan
ce
o
f
s
h
r
ap
n
el
p
iece
s
an
d
th
eir
lo
ca
ti
o
n
s
ar
e
ca
lcu
lat
ed
th
e
u
s
in
g
(
1
3
)
an
d
(
1
4
)
an
d
th
en
r
et
u
r
n
t
o
Step
d
.
j.
Dis
tan
ce
o
f
th
e
s
h
r
ap
n
el
p
iece
s
ar
e
r
ed
u
ce
d
b
y
(
1
8
)
.
k.
Ver
if
y
th
e
co
n
v
er
g
en
ce
cr
iter
i
a
an
d
if
s
atis
f
ied
,
th
e
alg
o
r
ith
m
will b
e
s
to
p
p
ed
i
f
n
o
t r
et
u
r
n
to
Step
b
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
9
,
No
.
2,
Au
g
u
s
t
20
20
:
83
–
9
1
86
4.
H
ARMON
Y
S
E
ARCH
A
L
G
O
RIT
H
M
Har
m
o
n
y
s
ea
r
ch
(
HS)
is
a
n
e
w
-
f
an
g
led
p
o
p
u
latio
n
-
b
ased
m
etah
eu
r
is
tic
o
p
tim
izatio
n
alg
o
r
ith
m
[
2
1
]
th
at
im
itates
th
e
m
u
s
ic
cr
ea
tiv
ity
p
r
o
ce
s
s
wh
er
e
th
e
m
u
s
ician
s
s
u
p
er
v
is
e
th
eir
in
s
tr
u
m
en
ts
’
p
itch
b
y
s
ea
r
ch
in
g
f
o
r
a
b
est
s
tate
o
f
h
ar
m
o
n
y
.
T
h
e
p
ar
am
eter
s
o
f
th
e
HS
ar
e:
I
n
th
is
s
tep
,
th
e
s
o
lu
tio
n
s
ar
e
ar
b
itra
r
ily
b
u
ilt
an
d
r
eo
r
g
a
n
ize
in
a
r
e
v
er
s
ed
o
r
d
er
to
HM
,
b
ased
o
n
th
eir
o
b
jectiv
e
f
u
n
ctio
n
v
alu
es su
ch
as
f
(
a
1
)
≤
f
(
a
2
)
.
.
.
.
.
≤
f
(
a
HMS
)
.
HM
=
[
1
1
⋯
1
⋮
⋱
⋮
1
⋯
|
|
(
1
)
.
.
.
(
)
]
(
1
9
)
T
h
e
f
o
llo
win
g
eq
u
atio
n
co
n
cise th
e
two
s
tep
s
i.e
.
m
em
o
r
y
co
n
s
id
er
atio
n
an
d
ar
b
itra
r
y
co
n
s
id
er
atio
n
.
′
←
{
′
∈
{
{
1
,
2
,
…
…
.
.
}
.
.
′
∈
.
.
(
1
−
)
(
2
0
)
Dec
is
io
n
v
ar
iab
les (
a
i
′
)
ar
e
s
cr
u
tin
ized
an
d
to
b
e
t
u
n
ed
with
t
h
e
p
r
o
b
a
b
ilit
y
o
f
PAR
∈
[
0
,
1
]
b
y
(
2
1
)
′
←
{
ℎ
.
.
ℎ
.
.
(
1
−
)
(
2
1
)
(
′
)
=
(
′
)
±
r
a
n
d
(
)
∗
bw
(
2
2
)
′
∈
˄
∉
(
2
3
)
T
h
e
PAR
v
alu
e
is
lin
ea
r
ly
in
cr
ea
s
ed
in
iter
atio
n
’
s
o
f
HS b
y
u
s
in
g
th
e
f
o
llo
win
g
eq
u
atio
n
,
PAR
(
g
n
)
=PAR
min
+
−
х
(
2
4
)
b
w(
g
n
)
=
b
w
min
+
−
х
(
2
5
)
Step
a:
p
r
elim
in
ar
y
p
o
p
u
latio
n
ar
e
ar
b
itra
r
ily
g
en
er
ated
an
d
c
alcu
late
th
e
f
itn
ess
o
f
ea
ch
in
d
iv
id
u
al;
Step
b
:
d
eter
m
in
e
th
e
b
est an
d
th
e
wo
r
s
t in
d
iv
id
u
als in
th
e
e
x
is
tin
g
p
o
p
u
latio
n
in
HM
;
Step
c:
co
n
tr
o
l
a
n
ew
-
f
an
g
led
h
ar
m
o
n
y
:
f
i
r
s
t,
en
g
en
d
er
a
n
o
v
el
v
ec
to
r
;
s
ec
o
n
d
l
y
,
ad
j
u
s
t
th
e
v
ec
to
r
th
r
o
u
g
h
HS;
Step
d
:
m
o
d
if
y
h
ar
m
o
n
y
m
em
o
r
y
,
wh
ic
h
is
s
am
e
to
s
elec
tio
n
.
,
+
1
=
{
,
(
,
≤
(
,
)
)
,
,
ℎ
.
(
2
6
)
Step
e:
au
th
en
ticate
th
e
s
to
p
p
i
n
g
cr
iter
io
n
:
|f
(
b
est
)
−
f
(
w
o
r
s
t
)
| <
=
1
×
10
-
16
.
5.
H
YB
RID
I
Z
AT
I
O
N
O
F
M
I
N
E
B
L
AS
T
AL
G
O
R
I
T
H
M
W
I
T
H
H
AR
M
O
NY
S
E
ARCH
AL
G
O
RI
T
H
M
T
h
e
h
y
b
r
id
ize
d
Min
e
B
last
Alg
o
r
ith
m
with
Har
m
o
n
y
Sear
c
h
alg
o
r
ith
m
(
MH
)
im
p
r
o
v
es
th
e
s
ea
r
ch
ef
f
ec
tiv
ely
in
th
e
s
o
lu
tio
n
s
p
ac
e.
Min
e
b
last
alg
o
r
ith
m
im
p
r
o
v
es
th
e
ex
p
l
o
r
atio
n
a
n
d
h
ar
m
o
n
y
s
ea
r
ch
alg
o
r
ith
m
au
g
m
en
ts
th
e
ex
p
lo
itatio
n
.
At
f
ir
s
t
th
e
p
r
o
p
o
s
ed
alg
o
r
i
th
m
s
tar
ts
with
ex
p
lo
r
at
io
n
&
g
r
a
d
u
ally
it
m
o
v
es to
th
e
p
h
ase
o
f
e
x
p
lo
ita
tio
n
.
Par
am
eter
s
ar
e
in
itiated
I
n
itial b
an
d
wid
th
o
f
ea
c
h
s
h
r
a
p
n
el
p
iece
will b
e
d
ete
r
m
in
ed
At
f
ir
s
t D
y
n
am
ic
Har
m
o
n
y
M
em
o
r
y
will b
e
n
il a
n
d
in
later
p
h
ases
ar
b
itra
r
ily
it will b
e
en
g
e
n
d
er
e
d
Ob
jectiv
e
f
u
n
ctio
n
h
as b
ee
n
c
alcu
lated
f
o
r
t
h
e
f
ir
s
t sh
o
t p
o
in
t &
B
est h
as b
ee
n
f
o
u
n
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Tr
u
e
p
o
w
er lo
s
s
r
ed
u
ctio
n
b
y
a
u
g
men
ted
min
e
b
la
s
t a
lg
o
r
ith
m
(
K
a
n
a
g
a
s
a
b
a
i Len
in
)
87
While (t < Maximum Iterations)
For i= 1: N
If t <
μ
%; Exploration Phase is done by the MBA
Estimate the modernized position of landmines using:
y
n
+
1
f
=
Y
e
(
n
+
1
)
f
+
e
x
p
(
−
√
m
n
+
1
f
d
n
+
1
f
)
Y
n
f
,
n
=
0
,
1
,
2
,
3
,
.
.
Else
% (HS is embedded in this Exploitation Phase,)
jrandom= floor(D
∗
rand(0, 1));
End for
For j
∈
1, ...,D do
If random(0, 1) ≤ CR or j==jrand) then
uj= xj;r0 + F
∗
(xj;r1− xj;r2);
Else
uj= xj;i;
End if
End for
Else
Compute the location of explode landmine by the following
X
e
(
n
+
1
)
f
=
d
n
f
×
r
a
n
d
×
cos
(
θ
)
,
n
=
0
,
1
,
2
,
.
.
Estimate the Euclidean distance & compute the modernized position of shrapnel pieces
End if
End for
End if
Dete
r
m
in
e
o
b
jectiv
e
f
u
n
ctio
n
o
f
en
g
e
n
d
er
e
d
s
h
r
ap
n
el
p
iece
s
an
d
class
th
e
s
h
r
ap
n
el
p
iece
s
C
h
o
o
s
e
th
e
m
o
s
t e
x
ce
llen
t sh
r
ap
n
el
p
iece
// Harmony memory considering: arbitrarily select any variable
-
i pitch in HM
if (rand(0, 1) ≤ HMCR then
if (round(0, 1) ≤ PAR then
//Pitch adjusting: arbitrarily adjust uj wit
hin a small bandwidth,
±rand(0, 1)
∗
BAND
if(round(0, 1) ≤ 0.5 then
vj= uj+ random(0, 1)
∗
BAND
else
vj= uj− random(0, 1)
∗
BAND
end if
end if
else
//Random playing: arbitrarily select any pitch within upper uj and lower bounds lj
vj= lj+ rand(0, 1)
∗
(uj− lj)
End if
End for
if vjis better than the worst harmony in HM, xworst, then
Replace xworst with vj in HM, then sort HM
End if
Until (best) −
f(worst)| < ε
End for
End if
End if
End while
6.
SI
M
UL
A
T
I
O
N
R
E
S
UL
T
S
At
f
ir
s
t
in
s
tan
d
ar
d
I
E
E
E
1
4
b
u
s
s
y
s
tem
th
e
v
alid
ity
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
ize
d
alg
o
r
ith
m
(
MH
)
h
as
b
ee
n
test
ed
&
co
m
p
ar
is
o
n
r
es
u
lts
ar
e
p
r
esen
ted
in
T
ab
le
1
.
Fig
u
r
e
1
.
Pro
v
id
e
th
e
d
etails
o
f
C
o
m
p
ar
is
o
n
o
f
r
ea
l p
o
wer
lo
s
s
.
T
ab
le
1
.
C
o
m
p
a
r
is
o
n
r
esu
lts
C
o
n
t
r
o
l
v
a
r
i
a
b
l
e
s
A
B
C
O
[
2
2
]
I
A
B
C
O
[
2
2
]
P
r
o
j
e
c
t
e
d
M
H
V1
1
.
0
6
1
.
0
5
1
.
0
4
V2
1
.
0
3
1
.
0
5
1
.
0
2
V3
0
.
9
8
1
.
0
3
1
.
0
3
V6
1
.
0
5
1
.
0
5
1
.
0
1
V8
1
.
0
0
1
.
0
4
0
.
9
0
Q9
0
.
1
3
9
0
.
1
3
2
0
.
1
1
0
T5
6
0
.
9
7
9
0
.
9
6
0
0
.
9
2
0
T4
7
0
.
9
5
0
0
.
9
5
0
0
.
9
0
0
T4
9
1
.
0
1
4
1
.
0
0
7
1
.
0
0
0
P
l
o
ss
(
M
W
)
5
.
9
2
8
9
2
5
.
5
0
0
3
1
4
.
8
2
4
2
6
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
9
,
No
.
2,
Au
g
u
s
t
20
20
:
83
–
9
1
88
Fig
u
r
e
1
.
C
o
m
p
a
r
is
o
n
o
f
r
ea
l
p
o
wer
lo
s
s
T
h
en
I
E
E
E
3
0
0
b
u
s
s
y
s
tem
[
1
8
]
is
u
s
ed
as
te
s
t
s
y
s
tem
to
v
a
lid
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
h
y
b
r
id
ize
d
alg
o
r
ith
m
(
MH
)
.
T
ab
le
2
s
h
o
ws
th
e
co
m
p
ar
is
o
n
o
f
r
ea
l
p
o
wer
lo
s
s
o
b
tain
ed
af
te
r
o
p
tim
izatio
n
.
Fig
u
r
e
2
g
i
v
es
th
e
co
m
p
a
r
is
o
n
o
f
r
ea
l
p
o
wer
v
alu
es.
R
ea
l
p
o
wer
lo
s
s
h
as
b
ee
n
co
n
s
id
er
a
b
ly
r
e
d
u
ce
d
wh
e
n
co
m
p
ar
ed
t
o
th
e
o
th
er
s
tan
d
ar
d
r
ep
o
r
ted
alg
o
r
it
h
m
s
.
T
h
en
h
y
b
r
id
ized
Mi
n
e
B
la
s
t
Alg
o
r
ith
m
with
Har
m
o
n
y
Sear
ch
alg
o
r
ith
m
(
MH
)
h
as
b
ee
n
test
ed
i
n
I
E
E
E
3
0
b
u
s
s
y
s
tem
[
2
5
]
wit
h
co
n
s
id
er
in
g
v
o
ltag
e
s
tab
ilit
y
in
d
ex
.
I
t
h
as
a
s
u
m
o
f
ac
tiv
e
an
d
r
ea
ctiv
e
p
o
wer
co
n
s
u
m
p
tio
n
o
f
2
.
8
3
4
an
d
1
.
2
6
2
p
e
r
u
n
it
o
n
1
0
0
MV
A
b
ase.
T
ab
le
3
g
iv
es
th
e
co
n
s
tr
ain
ts
o
f
co
n
tr
o
l
v
ar
iab
les;
T
ab
le
4
g
iv
es
t
h
e
s
y
s
tem
p
ar
am
eter
s
;
th
e
n
T
ab
le
5
g
iv
es
th
e
r
ea
l
p
o
wer
lo
s
s
co
m
p
ar
is
o
n
.
C
o
m
p
ar
is
o
n
o
f
d
if
f
e
r
en
t
alg
o
r
ith
m
s
with
r
ef
er
en
ce
to
v
o
ltag
e
s
tab
ilit
y
im
p
r
o
v
em
en
t
h
as
b
ee
n
g
iv
en
in
T
ab
le
6
.
T
h
en
C
o
m
p
ar
is
o
n
o
f
v
alu
e
s
with
r
ef
er
en
ce
to
Vo
ltag
e
Dev
iatio
n
Min
im
izatio
n
h
as
b
ee
n
g
iv
e
n
T
ab
le
7
.
Fin
ally
,
C
o
m
p
ar
is
o
n
o
f
v
alu
es
with
r
ef
er
en
ce
t
o
Mu
lti
–
o
b
je
ctiv
e
f
o
r
m
u
latio
n
is
g
iv
en
i
n
T
ab
le
8
.
T
ab
le
2
.
C
o
m
p
a
r
is
o
n
o
f
r
ea
l p
o
wer
lo
s
s
P
a
r
a
me
t
e
r
M
e
t
h
o
d
EG
A
[
2
4
]
M
e
t
h
o
d
EE
A
[
2
4
]
M
e
t
h
o
d
C
S
A
[
2
3
]
P
r
o
j
e
c
t
e
d
M
H
P
LO
S
S
(
M
W
)
6
4
6
.
2
9
9
8
6
5
0
.
6
0
2
7
6
3
5
.
8
9
4
2
6
1
8
.
0
4
1
4
Fig
u
r
e
2
.
R
ea
l p
o
wer
lo
s
s
co
m
p
ar
is
o
n
T
ab
le
3
.
C
o
n
s
tr
ain
ts
o
f
c
o
n
tr
o
l
v
ar
iab
les
T
ab
le
4
.
Sy
s
tem
p
ar
a
m
eter
s
V
a
r
i
a
b
l
e
s
M
i
n
i
m
u
m
(
P
U
)
M
a
x
i
m
u
m
(
P
U
)
G
e
n
e
r
a
t
o
r
V
o
l
t
a
g
e
0
.
9
5
1
.
1
Tr
a
n
sf
o
r
mer T
a
p
0
.
9
1
.
1
V
A
R
S
o
u
r
c
e
0
5
(
M
V
A
R
)
D
e
scri
p
t
i
o
n
I
EEE
3
0
b
u
s
N
B
–
n
u
m
b
e
r
o
f
b
u
ses
30
NG
-
N
u
mb
e
r
o
f
g
e
n
e
r
a
t
o
r
s
6
NT
-
n
u
mb
e
r
o
f
t
r
a
n
sf
o
r
mers
4
NQ
-
n
u
m
b
e
r
o
f
s
h
u
n
t
9
NE
-
N
u
m
b
e
r
o
f
b
r
a
n
c
h
e
s
41
P
Lo
ss
(
b
a
s
e
c
a
se)
M
W
5
.
6
6
B
a
se
c
a
r
e
f
o
r
V
D
(
P
U
)
0
.
5
8
2
1
7
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Tr
u
e
p
o
w
er lo
s
s
r
ed
u
ctio
n
b
y
a
u
g
men
ted
min
e
b
la
s
t a
lg
o
r
ith
m
(
K
a
n
a
g
a
s
a
b
a
i Len
in
)
89
T
ab
le
5
.
C
o
m
p
a
r
is
o
n
o
f
r
ea
l p
o
wer
lo
s
s
with
d
if
f
er
en
t m
eta
h
eu
r
is
tic
alg
o
r
it
h
m
s
DE
[
2
6
]
G
S
A
[
2
7
]
A
P
O
P
S
O
[
2
8
]
MH
VG1
1
.
1
1
.
0
7
1
1
.
1
0
0
1
.
0
9
3
VG2
1
.
0
9
1
.
0
2
2
1
.
0
8
4
1
.
0
4
0
VG5
1
.
0
7
1
.
0
4
0
1
.
0
5
6
1
.
0
2
4
VG8
1
.
0
7
1
.
0
5
1
1
.
0
7
6
1
.
0
4
1
V
G
1
1
1
.
1
0
.
9
7
7
1
.
0
9
1
1
.
0
8
3
V
G
1
3
5
0
.
9
6
8
1
.
1
0
0
0
.
9
7
0
Q
C
1
0
5
1
.
6
5
3
5
.
0
0
0
4
.
9
6
2
Q
C
1
2
5
4
.
3
7
2
2
5
.
0
0
0
5
.
0
0
0
Q
C
1
5
5
0
.
1
1
9
9
4
.
8
7
9
4
.
7
8
3
Q
C
1
7
5
2
.
0
8
7
6
4
.
9
7
6
4
.
9
7
1
Q
C
2
0
4
.
4
1
0
.
3
5
7
3
.
8
2
1
3
.
7
0
5
Q
C
2
1
5
0
.
2
6
0
2
4
.
5
4
1
4
.
6
6
2
Q
C
23
2
.
8
0
0
4
0
.
0
0
0
0
2
.
3
5
4
2
.
4
0
0
Q
C
2
4
5
1
.
3
8
3
9
4
.
6
5
4
4
.
5
0
1
Q
C
2
9
2
.
5
9
7
9
0
.
0
0
0
0
2
.
1
7
5
2
.
1
6
0
T1
1
(6
-
9)
1
.
0
4
1
.
0
9
8
5
1
.
0
2
9
1
.
0
1
4
T1
2
(6
-
1
0
)
0
.
9
0
9
7
0
.
9
8
2
4
0
.
9
1
1
0
.
9
0
5
T1
5
(4
-
1
2
)
0
.
9
8
1
.
0
9
5
0
.
9
5
2
0
.
9
4
6
T3
6
(
2
8
-
27)
0
.
9
6
8
9
1
.
0
5
9
3
0
.
9
5
8
0
.
9
4
3
P
Lo
ss
(
M
W
)
4
.
5
5
5
4
.
5
1
4
3
4
.
3
9
8
4
.
2
1
4
V
D
(
P
U
)
1
.
9
5
8
9
0
.
8
7
5
2
2
1
.
0
4
7
1
.
0
3
1
L
-
i
n
d
e
x
(PU)
0
.
5
5
1
3
0
.
1
4
1
0
9
0
.
1
2
6
7
0
.
1
2
0
2
T
ab
le
6
.
C
o
m
p
a
r
is
o
n
o
f
d
if
f
e
r
en
t a
lg
o
r
ith
m
s
with
r
ef
er
en
ce
to
v
o
ltag
e
s
tab
ilit
y
i
m
p
r
o
v
e
m
en
t
DE
[
2
6
]
G
S
A
[
2
7
]
A
P
O
P
S
O
[
2
8
]
MH
VG1
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ab
le
7
.
C
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m
p
a
r
is
o
n
with
r
ef
e
r
en
ce
to
Vo
ltag
e
Dev
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n
Min
im
izatio
n
DE
[
2
6
]
G
S
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[
2
7
]
A
P
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P
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[
2
8
]
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ab
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.
C
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f
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with
r
ef
er
en
ce
to
Mu
lti
–
o
b
jectiv
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f
o
r
m
u
latio
n
A
P
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P
S
O
[
2
8
]
MH
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9
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
9
,
No
.
2,
Au
g
u
s
t
20
20
:
83
–
9
1
90
7.
CO
NCLU
SI
O
N
I
n
th
is
wo
r
k
Min
e
B
last
Al
g
o
r
ith
m
(
MBA)
h
as
b
ee
n
in
ter
m
in
g
led
with
Har
m
o
n
y
S
ea
r
ch
(
HS)
alg
o
r
ith
m
s
u
cc
ess
f
u
lly
s
o
lv
e
d
th
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
d
i
s
p
atch
p
r
o
b
lem
.
T
h
e
h
y
b
r
id
iz
ed
alg
o
r
ith
m
(
MH
)
im
p
r
o
v
es
th
e
s
ea
r
ch
e
f
f
ec
tiv
e
ly
in
th
e
s
o
lu
tio
n
s
p
ac
e
.
Min
e
b
last
alg
o
r
ith
m
im
p
r
o
v
es
t
h
e
ex
p
l
o
r
atio
n
an
d
h
ar
m
o
n
y
s
ea
r
ch
alg
o
r
ith
m
a
u
g
m
en
ts
th
e
ex
p
l
o
itatio
n
.
At
f
i
r
s
t
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
tar
ts
with
ex
p
lo
r
atio
n
an
d
g
r
ad
u
ally
it
m
o
v
es
to
t
h
e
p
h
ase
o
f
ex
p
l
o
itatio
n
.
At
f
i
r
s
t
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
tar
ts
with
ex
p
lo
r
atio
n
&
g
r
ad
u
ally
it
m
o
v
es
to
th
e
p
h
ase
o
f
ex
p
lo
itatio
n
.
Pro
p
o
s
ed
Hy
b
r
id
ized
alg
o
r
ith
m
(
MH
)
h
as
b
ee
n
test
ed
o
n
s
tan
d
ar
d
I
E
E
E
1
4
,
3
0
0
b
u
s
tes
t
s
y
s
tem
s
.
R
ea
l
p
o
wer
lo
s
s
h
as
b
ee
n
r
e
d
u
ce
d
co
n
s
id
er
ab
ly
b
y
th
e
p
r
o
p
o
s
ed
MH
alg
o
r
ith
m
.
T
h
en
Hy
b
r
id
ize
d
Min
e
B
last
Alg
o
r
ith
m
with
Har
m
o
n
y
Sear
ch
al
g
o
r
ith
m
(
M
H)
ar
e
test
ed
in
I
E
E
E
3
0
,
b
u
s
s
y
s
tem
(
with
c
o
n
s
id
e
r
in
g
v
o
ltag
e
s
tab
ilit
y
in
d
e
x
)
-
r
ea
l
p
o
wer
lo
s
s
m
in
im
izatio
n
,
v
o
ltag
e
d
e
v
iatio
n
m
in
im
izatio
n
,
an
d
v
o
ltag
e
s
tab
ilit
y
in
d
ex
e
n
h
an
ce
m
e
n
t h
as
b
ee
n
attain
ed
.
RE
F
E
R
E
NC
E
S
[1
]
K.
Y.
Lee
,
e
t
a
l,
“
F
u
e
l
-
c
o
st
m
in
imis
a
ti
o
n
fo
r
b
o
t
h
re
a
l
a
n
d
re
a
c
ti
v
e
-
p
o
we
r
d
is
p
a
tch
e
s,”
Pro
c
e
e
d
in
g
s
Ge
n
e
ra
ti
o
n
,
T
ra
n
sm
issio
n
a
n
d
Distrib
u
ti
o
n
C
o
n
fer
e
n
c
e
,
v
o
l.
1
3
1
,
n
o
.
3
,
p
p
.
8
5
-
9
3
,
1
9
8
4
.
[2
]
N.
I.
De
e
b
,
e
t
a
l.
,
“
An
e
fficie
n
t
tec
h
n
iq
u
e
fo
r
re
a
c
ti
v
e
p
o
we
r
d
is
p
a
t
c
h
u
sin
g
a
re
v
ise
d
li
n
e
a
r
p
ro
g
ra
m
m
in
g
a
p
p
r
o
a
c
h
,
”
El
e
c
tric P
o
we
r S
y
ste
m
Res
e
a
rc
h
,
v
o
l.
15
,
n
o
.
2
,
p
p
.
1
2
1
–
1
3
4
,
1
9
8
8
.
[3
]
M
.
R.
Bjelo
g
rli
c
,
M
.
S
.
Ca
lo
v
ic,
B.
S
.
Ba
b
ic,
e
t.
a
l.
,
“
Ap
p
li
c
a
ti
o
n
o
f
Ne
wto
n
’s
o
p
t
ima
l
p
o
we
r
flo
w
i
n
v
o
lt
a
g
e
/rea
c
ti
v
e
p
o
we
r
c
o
n
tr
o
l”,
I
EE
E
T
ra
n
s P
o
we
r S
y
ste
m
,
v
o
l.
5
,
n
o
.
4
,
p
p
.
1
4
4
7
-
1
4
5
4
,
1
9
9
0
.
[4
]
S
.
G
ra
n
v
il
le,
“
Op
ti
m
a
l
re
a
c
ti
v
e
d
isp
a
tch
t
h
ro
u
g
h
i
n
terio
r
p
o
in
t
m
e
t
h
o
d
s
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r
S
y
ste
m
,
v
o
l
.
9
,
n
o
.
1
,
p
p
.
1
3
6
–
1
4
6
,
1
9
9
4
.
[5
]
N.
G
ru
d
in
i
n
,
“
Re
a
c
ti
v
e
p
o
we
r
o
p
ti
m
iza
ti
o
n
u
sin
g
su
c
c
e
ss
iv
e
q
u
a
d
ra
ti
c
p
r
o
g
ra
m
m
in
g
m
e
th
o
d
,
”
IE
EE
T
r
a
n
sa
c
ti
o
n
s
o
n
P
o
we
r S
y
ste
m
,
v
o
l.
13
,
n
o
.
4
,
p
p
.
1
2
1
9
–
1
2
2
5
,
1
9
9
8
.
[6
]
Wei
Ya
n
,
J.
Yu
,
D.
C.
Yu
a
n
d
K.
Bh
a
tt
a
ra
i
,
“
A
n
e
w
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
flo
w
m
o
d
e
l
in
re
c
tan
g
u
lar
fo
rm
a
n
d
it
s
so
lu
ti
o
n
b
y
p
re
d
icto
r
c
o
rre
c
to
r
p
rima
l
d
u
a
l
in
teri
o
r
p
o
in
t
m
e
th
o
d
”
,
IEE
E
T
r
a
n
s.
Pwr.
S
y
st
.
,
v
o
l.
21,
no.
1
,
pp.
61
-
6
7
,
2
0
0
6
.
[7
]
Ap
a
ra
ji
ta
M
u
k
h
e
rjee
,
Vi
v
e
k
a
n
a
n
d
a
M
u
k
h
e
rjee
,
“
S
o
lu
ti
o
n
o
f
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
b
y
c
h
a
o
ti
c
k
ri
ll
h
e
rd
a
lg
o
rit
h
m
"
,
IE
T
Ge
n
e
r.
T
r
a
n
sm
.
Distrib
,
,
v
o
l.
9
,
no
.
1
5
,
p
p
.
2
3
5
1
–
2
3
6
2
,
2
0
1
5
.
[8
]
M
a
h
a
letc
h
u
m
i
A/P
M
o
rg
a
n
,
No
r
Ru
l
Ha
sm
a
Ab
d
u
ll
a
h
,
M
o
h
d
He
rwa
n
S
u
laim
a
n
,
M
a
h
f
u
z
a
h
M
u
sta
f
a
a
n
d
R
o
sd
i
y
a
n
a
S
a
m
a
d
,
“
Co
m
p
u
tati
o
n
a
l
i
n
telli
g
e
n
c
e
tec
h
n
iq
u
e
fo
r
sta
ti
c
VA
R
c
o
m
p
e
n
sa
to
r
(S
VC)
in
sta
ll
a
ti
o
n
c
o
n
sid
e
rin
g
m
u
lt
i
-
c
o
n
ti
n
g
e
n
c
ies
(N
-
m
)”
,
AR
PN
J
o
u
rn
a
l
o
f
E
n
g
i
n
e
e
rin
g
a
n
d
Ap
p
li
e
d
S
c
ien
c
e
s
,
v
o
l
.
1
0
,
no
.
2
2
,
2
0
1
5
.
[9
]
M
o
h
d
He
rwa
n
S
u
laim
a
n
,
Zu
ria
n
i
M
u
sta
ffa
,
Ha
m
d
a
n
Da
n
iy
a
l
,
M
o
h
d
R
u
slli
m
M
o
h
a
m
e
d
a
n
d
Om
a
r
A
li
m
a
n
,
“
S
o
l
v
in
g
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
P
lan
n
i
n
g
P
ro
b
lem
Util
izin
g
Na
tu
re
In
sp
ired
Co
m
p
u
ti
n
g
Tec
h
n
iq
u
e
s”
,
AR
PN
J
o
u
rn
a
l
o
f
En
g
i
n
e
e
rin
g
a
n
d
A
p
p
li
e
d
S
c
ien
c
e
s
,
vol
.
1
0
,
no
.
2
1
,
p
p
.
9
7
7
9
-
9
7
8
5
.
2
0
1
5
.
[1
0
]
M
o
h
d
He
rwa
n
S
u
laim
a
n
,
Wo
n
g
Lo
In
g
,
Z
u
rian
i
M
u
sta
ffa
a
n
d
M
o
h
d
R
u
slli
m
M
o
h
a
m
e
d
,
“
G
re
y
Wo
lf
Op
ti
m
ize
r
fo
r
S
o
lv
in
g
Eco
n
o
m
ic
Disp
a
tch
P
ro
b
lem
with
Va
lv
e
-
L
o
a
d
in
g
Eff
e
c
t
s”
,
AR
PN
J
o
u
r
n
a
l
o
f
E
n
g
in
e
e
rin
g
a
n
d
Ap
p
li
e
d
S
c
ien
c
e
s,
vol
.
1
0
,
no
.
2
1
,
p
p
.
9
7
9
6
-
9
8
0
1
,
2
0
1
5
.
[1
1
]
P
a
n
d
iara
jan
,
K.
&
Ba
b
u
lal,
C.
K.,
“
F
u
z
z
y
h
a
rm
o
n
y
se
a
rc
h
a
lg
o
r
it
h
m
b
a
se
d
o
p
ti
m
a
l
p
o
we
r
fl
o
w
fo
r
p
o
we
r
sy
ste
m
se
c
u
rit
y
e
n
h
a
n
c
e
m
e
n
t”.
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
El
e
c
tric P
o
we
r E
n
e
rg
y
S
y
st.,
v
o
l.
7
8
,
p
p
.
7
2
-
7
9
.
2
0
1
6
.
[1
2
]
S
u
laim
a
n
,
M
.
H.,
M
u
sta
ffa
,
Z.
,
M
o
h
a
m
e
d
,
M
.
R
.
,
Alima
n
,
O
.
,
“
An
a
p
p
li
c
a
ti
o
n
o
f
m
u
lt
i
-
v
e
rse
o
p
ti
m
i
z
e
r
fo
r
o
p
t
ima
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
p
r
o
b
lem
s
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
S
imu
l
a
ti
o
n
:
S
y
ste
ms
,
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
1
7
,
v
o
l.
4
1
,
pp.
1
-
5
2
0
1
7
.
[1
3
]
M
a
h
a
letc
h
u
m
i
A/P
M
o
rg
a
n
,
No
r
Ru
l
Ha
sm
a
Ab
d
u
ll
a
h
,
M
o
h
d
He
rwa
n
S
u
laim
a
n
,
M
a
h
f
u
z
a
h
M
u
sta
f
a
a
n
d
R
o
sd
i
y
a
n
a
S
a
m
a
d
,
“
M
u
lt
i
-
O
b
jec
ti
v
e
E
v
o
l
u
ti
o
n
a
r
y
P
r
o
g
ra
m
m
in
g
(M
OEP
)
Us
in
g
M
u
tati
o
n
Ba
se
d
o
n
A
d
a
p
ti
v
e
M
u
tati
o
n
Op
e
ra
to
r
(AMO)
Ap
p
li
e
d
fo
r
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Dis
p
a
tch
,
”
AR
PN
J
o
u
rn
a
l
o
f
E
n
g
i
n
e
e
rin
g
a
n
d
Ap
p
li
e
d
S
c
ien
c
e
s
,
vol
.
1
1
,
no
.
1
4
,
2
0
1
6
.
[1
4
]
Re
b
e
c
c
a
Ng
S
h
in
M
e
i,
M
o
h
d
He
r
wa
n
S
u
laim
a
n
,
Zu
ria
n
i
M
u
sta
ffa
,
“
An
t
Li
o
n
Op
ti
m
ize
r
fo
r
Op
t
ima
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
S
o
lu
ti
o
n
,
”
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
S
y
ste
ms
,
S
p
e
c
ial
Iss
u
e
A
M
P
E2
0
1
5
,
p
p
.
6
8
-
74
,
2
0
1
6
.
[1
5
]
M
a
h
a
letc
h
u
m
i
M
o
rg
a
n
,
No
r
Ru
l
Ha
sm
a
Ab
d
u
ll
a
h
,
M
o
h
d
He
rwa
n
S
u
laim
a
n
,
M
a
h
f
u
z
a
h
M
u
sta
fa
,
Ro
sd
iy
a
n
a
S
a
m
a
d
,
“
Be
n
c
h
m
a
rk
S
tu
d
ies
o
n
O
p
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
(ORPD)
Ba
se
d
M
u
lt
i
-
o
b
jec
ti
v
e
Ev
o
lu
ti
o
n
a
r
y
P
ro
g
ra
m
m
in
g
(M
OEP
)
Us
i
n
g
M
u
tatio
n
Ba
se
d
o
n
Ad
a
p
t
iv
e
M
u
t
a
ti
o
n
Ad
a
p
ter
(AMO)
a
n
d
P
o
l
y
n
o
m
ial
M
u
tatio
n
Op
e
ra
to
r
(P
M
O)
,
”
J
o
u
rn
a
l
o
f
E
lec
trica
l
S
y
ste
ms
,
1
2
-
1
,
2
0
1
6
.
[1
6
]
Re
b
e
c
c
a
Ng
S
h
in
M
e
i,
M
o
h
d
H
e
rwa
n
S
u
laim
a
n
,
Zu
ria
n
i
M
u
sta
ffa
,
Ha
m
d
a
n
Da
n
iy
a
l,
“
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
we
r
Disp
a
tch
S
o
l
u
ti
o
n
b
y
Lo
ss
M
i
n
i
m
iza
ti
o
n
u
si
n
g
M
o
t
h
-
F
lam
e
Op
t
imiz
a
ti
o
n
Tec
h
n
iq
u
e
,
”
Ap
p
li
e
d
S
o
ft
Co
m
p
u
t
in
g
,
vol
5
9
,
p
p
.
2
1
0
-
2
2
2
,
2
0
1
7
.
[1
7
]
Ch
a
n
d
ra
g
u
p
ta
M
a
u
r
y
a
n
K
u
p
p
a
m
u
th
u
S
i
v
a
li
n
g
a
m
1
,
S
u
b
ra
m
a
n
ian
Ra
m
a
c
h
a
n
d
ra
n
,
P
u
rrn
ima
a
S
h
iv
a
S
a
k
th
i
Ra
jam
a
n
i,
“
Re
a
c
ti
v
e
p
o
we
r
o
p
ti
m
iza
ti
o
n
in
a
p
o
we
r
s
y
ste
m
n
e
two
r
k
t
h
ro
u
g
h
m
e
tah
e
u
risti
c
a
l
g
o
rit
h
m
s
,
”
T
u
r
k
ish
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
&
Co
m
p
u
t
e
r S
c
ien
c
e
,
v
o
l
.
2
5
,
p
p
.
4
6
1
5
-
4
6
2
3
,
2
0
1
7
.
[1
8
]
IEE
E,
“
Th
e
I
EE
E
-
tes
t
sy
ste
m
s”
,
(
1
9
9
3
)
.
[1
9
]
A.
S
a
d
o
ll
a
h
,
A.
Ba
h
re
in
i
n
e
jad
,
H.
Esk
a
n
d
a
r,
M
.
Ha
m
d
i,
“
M
in
e
b
las
t
a
lg
o
rit
h
m
fo
r
o
p
ti
m
iza
ti
o
n
o
f
tru
ss
stru
c
tu
re
s
with
d
isc
re
te v
a
riab
les
,
”
Co
mp
u
te
rs
&
S
tru
c
tu
re
s
,
v
o
l.
1
0
2
–
1
0
3
,
p
p
.
4
9
–
6
3
,
2
0
1
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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6
Tr
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y
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ith
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(
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91
[2
0
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Ali
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a
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h
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d
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m
d
ia,
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a
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it
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m
,
“
A
n
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p
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latio
n
-
b
a
se
d
a
lg
o
rit
h
m
fo
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g
i
n
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g
o
p
ti
m
iza
ti
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n
p
ro
b
lem
s,
”
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p
li
e
d
S
o
ft
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o
mp
u
ti
n
g
,
v
o
l.
1
3
,
p
p
.
2
5
9
2
–
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6
1
2
,
2
0
1
3
.
[2
1
]
Z.
W.
G
e
e
m
,
J.
H.
Kim
,
a
n
d
G
.
V.
Lo
g
a
n
a
t
h
a
n
,
“
A
n
e
w
h
e
u
risti
c
o
p
t
imiz
a
ti
o
n
a
lg
o
r
it
h
m
:
H
a
rm
o
n
y
se
a
rc
h
,
”
J
.
S
imu
l
.
,
v
o
l
.
7
6
,
n
o
.
2
,
p
p
.
6
0
–
6
8
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b
.
2
0
0
1
.
[2
2
]
Ch
a
n
d
ra
g
u
p
ta
M
a
u
r
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u
p
p
a
m
u
th
u
S
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n
g
a
m
1,
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u
b
ra
m
a
n
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Ra
m
a
c
h
a
n
d
ra
n
,
P
u
rrn
ima
a
S
h
iv
a
S
a
k
th
i
Ra
jam
a
n
i,
“
Re
a
c
ti
v
e
p
o
we
r
o
p
ti
m
iza
ti
o
n
in
a
p
o
we
r
s
y
ste
m
n
e
two
r
k
t
h
ro
u
g
h
m
e
tah
e
u
risti
c
a
l
g
o
rit
h
m
s
,
”
T
u
r
k
ish
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
&
Co
m
p
u
t
e
r S
c
ien
c
e
,
v
o
l
.
25
,
p
p
.
4
6
1
5
–
4
6
2
3
,
2
0
1
7
.
[2
3
]
S
.
S
u
re
n
d
e
r
Re
d
d
y
,
“
Op
ti
m
a
l
Re
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c
ti
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e
P
o
we
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S
c
h
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d
u
li
n
g
Us
in
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Cu
c
k
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g
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h
m
,
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ter
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e
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trica
l
a
n
d
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o
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ter
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(IJ
ECE
)
,
v
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l
.
7
,
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o
.
5
,
p
p
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3
4
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-
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6
.
2
0
1
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[2
4
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.
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d
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y
,
“
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a
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m
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tal
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ria
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les
,
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e
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trica
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Po
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n
d
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ms
,
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l.
5
4
,
p
p
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1
9
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0
,
2
0
1
4
.
[2
5
]
Ill
in
o
is
Ce
n
ter
fo
r
a
S
m
a
rter
El
e
c
tri
c
G
rid
(ICS
EG
).
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il
a
b
le
o
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li
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e
:
h
t
tp
s://
ics
e
g
.
it
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.
[2
6
]
El
El
a
,
A.A.;
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id
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M
.
A.
;
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,
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.
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,
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Po
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r S
y
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Res
,
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p
p
.
4
5
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–
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6
4
,
2
0
1
1
.
[2
7
]
Du
m
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n
,
S
.
;
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ö
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z
,
Y.;
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ü
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U.;
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n
,
N.
,
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ti
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ra
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it
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rc
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m
,”
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Ge
n
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r.
T
ra
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sm
.
Distrib
,
v
o
l.
6,
p
p
.
5
6
3
–
5
7
6
,
2
0
1
2
.
[2
8
]
Aljo
h
a
n
i,
T.
M
.
;
E
b
ra
h
im,
A.F
.
;
M
o
h
a
m
m
e
d
,
O
,
“
S
in
g
le an
d
M
u
lt
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ti
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Op
ti
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Re
a
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ti
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e
P
o
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Disp
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tch
Ba
se
d
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n
H
y
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ri
d
Artifi
c
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P
h
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sic
s
–
P
a
rt
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S
wa
rm
Op
ti
m
iza
ti
o
n
,”
E
n
e
rg
ies
,
v
o
l
.
1
2
,
n
o
.
1
2
,
p
p
.
2
3
3
3
,
2
0
1
9
.
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