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2
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8
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–
136
130
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u
m
er
o
u
s
co
n
v
e
n
tio
n
al
tec
h
n
iq
u
e
s
o
f
f
er
ed
s
o
lu
tio
n
s
to
R
P
P
o
r
V
AR
s
o
u
r
c
e
s
p
lan
n
i
n
g
p
r
o
b
le
m
i
n
clu
d
ed
L
P
[
6
]
,
NL
P
,
MI
NL
P
an
d
No
n
L
in
ea
r
I
n
ter
io
r
P
o
in
t
Me
th
o
d
(
NI
P
M)
[
7
]
.
Ho
w
e
v
er
,
it
f
r
eq
u
en
t
l
y
r
e
s
u
lte
d
in
lo
ca
l
o
p
ti
m
a
r
at
h
er
t
h
an
g
iv
in
g
a
s
o
lu
tio
n
o
f
g
lo
b
al
o
p
ti
m
a.
I
t
al
s
o
ca
u
s
e
d
d
iv
er
g
e
n
ce
in
s
o
lu
tio
n
w
h
en
t
r
y
in
g
to
o
p
tim
ize
t
w
o
o
b
j
ec
ti
v
e
f
u
n
ct
io
n
s
s
i
m
u
lta
n
eo
u
s
l
y
.
C
o
n
s
eq
u
en
tl
y
,
n
e
w
ad
v
an
ce
d
o
p
ti
m
izatio
n
m
et
h
o
d
s
w
er
e
in
tr
o
d
u
ce
d
w
h
ich
e
x
h
ib
it
s
o
m
e
ar
tific
ial
in
telli
g
en
c
e
b
eh
av
io
r
s
s
u
ch
a
s
Si
m
u
lated
An
n
ea
li
n
g
(
S
A
)
,
E
v
o
lu
tio
n
ar
y
Alg
o
r
it
h
m
s
(
E
A
)
,
Gen
etic
A
l
g
o
r
ith
m
(
G
A
)
a
n
d
T
ab
u
Sear
c
h
(
T
S)
[
8
]
-
[
1
1
]
.
T
h
ese
tech
n
iq
u
es
o
f
f
er
ed
g
lo
b
al
o
p
tim
al
s
o
lu
t
io
n
s
,
h
o
w
ev
er
,
at
t
h
e
ex
p
e
n
s
e
o
f
c
o
m
p
u
tatio
n
al
ti
m
e
[
1
2
]
.
T
h
er
ef
o
r
e,
r
ec
en
t
r
esear
ch
es
ar
e
in
s
p
ir
ed
to
m
er
g
e
c
o
n
v
e
n
tio
n
al
m
et
h
o
d
s
an
d
ad
v
an
ce
d
o
p
tim
iza
t
io
n
tech
n
iq
u
es
f
o
r
b
etter
an
d
f
aste
r
o
p
tim
izat
io
n
ap
p
r
o
ac
h
es.
T
h
is
s
tu
d
y
i
n
tr
o
d
u
ce
d
a
n
ew
A
d
ap
tiv
e
T
u
m
b
li
n
g
B
ac
ter
ial
Fo
r
ag
in
g
Op
ti
m
iza
tio
n
(
A
T
B
FO)
alg
o
r
ith
m
w
h
ic
h
i
s
a
n
i
m
p
r
o
v
e
m
e
n
t
to
th
e
b
asic
B
ac
ter
ia
l
Fo
r
ag
i
n
g
Op
ti
m
izatio
n
(
B
FO)
alg
o
r
ith
m
.
T
h
e
p
r
o
p
o
s
e
d
tech
n
iq
u
e
w
a
s
i
m
p
le
m
en
ted
f
o
r
R
P
P
m
u
l
ti
-
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
.
Sev
er
al
i
d
en
tifie
d
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
w
er
e
g
e
n
er
alize
d
i
n
to
s
in
g
le
o
b
j
ec
tiv
e
f
u
n
ct
io
n
v
ia
th
e
w
ei
g
h
ted
s
u
m
m
et
h
o
d
th
en
k
n
o
w
n
as
th
e
m
u
lti
-
o
b
j
ec
tiv
es
f
u
n
ctio
n
.
Fo
r
th
at
r
ea
s
o
n
,
t
h
e
A
T
B
FO
f
o
r
m
u
lti
-
o
b
j
ec
tiv
es
f
u
n
ctio
n
i
s
n
a
m
ed
as
t
h
e
m
u
lti
-
o
b
j
ec
tiv
e
A
d
ap
tiv
e
T
u
m
b
li
n
g
B
ac
ter
ial
Fo
r
ag
in
g
alg
o
r
it
h
m
(
MO
A
T
B
FO)
.
Fin
all
y
,
th
e
p
er
f
o
r
m
an
ce
s
o
f
t
h
e
n
e
w
l
y
d
e
v
elo
p
ed
tech
n
iq
u
e
MO
A
T
B
FO
w
er
e
co
m
p
ar
ed
w
it
h
t
h
at
p
r
o
v
id
ed
b
y
t
h
e
m
u
l
ti
o
b
j
ec
tiv
e
Me
ta
-
E
P
m
et
h
o
d
.
T
h
e
s
m
alles
t to
tal
s
y
s
te
m
lo
s
s
es a
n
d
lar
g
er
m
ax
i
m
u
m
lo
ad
in
g
p
o
in
t t
h
at
t
h
e
s
y
s
te
m
ca
n
w
it
h
s
ta
n
d
ar
e
d
ec
lar
ed
as th
e
b
est s
o
lu
tio
n
s
.
2.
M
UL
T
I
O
B
J
E
CT
I
V
E
S
E
C
URED R
E
AC
T
I
V
E
P
O
WE
R
P
L
ANN
I
N
G
T
h
e
m
u
lt
i
-
o
b
j
ec
tiv
e
S
C
R
P
P
o
r
n
a
m
ed
as
MO
S
C
R
P
P
ai
m
ed
to
m
ax
i
m
ize
th
e
ML
P
a
n
d
m
i
n
i
m
ize
t
h
e
to
tal
s
y
s
te
m
lo
s
s
e
s
s
i
m
u
lta
n
eo
u
s
l
y
.
B
o
th
o
b
j
ec
tiv
e
f
u
n
c
tio
n
s
ar
e
co
m
b
i
n
ed
to
b
e
o
n
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
u
s
i
n
g
th
e
w
ei
g
h
ted
s
u
m
m
eth
o
d
an
d
ap
p
lied
to
th
e
n
e
w
MO
A
T
B
FO te
ch
n
iq
u
e.
2
.
1
.
M
a
x
i
m
izing
M
L
P
o
bje
ct
iv
e
f
un
ct
io
n
L
o
ad
m
ar
g
i
n
a
n
al
y
s
is
h
a
s
k
n
o
w
n
to
b
e
o
n
e
o
f
t
h
e
s
i
g
n
i
f
ica
n
t
p
ar
a
m
eter
s
f
o
r
v
o
lta
g
e
s
tab
ili
t
y
s
tu
d
ie
s
.
I
n
m
a
x
i
m
u
m
lo
ad
ab
ilit
y
li
m
i
t
ev
alu
at
io
n
,
th
e
lo
ad
w
as
i
n
c
r
ea
s
ed
u
n
til
t
h
e
o
cc
u
r
r
en
ce
v
o
ltag
e
co
llap
s
e,
th
at
w
h
e
n
th
e
s
y
s
te
m
b
e
g
i
n
s
to
lo
s
e
its
eq
u
ilib
r
iu
m
as
in
Fi
g
u
r
e
1
.
Gr
ap
h
icall
y
,
th
e
lo
ad
m
a
r
g
in
is
p
o
r
tr
a
y
ed
b
y
th
e
r
a
n
g
e
b
et
w
ee
n
λ
0
o
r
th
e
l
o
ad
in
g
f
o
r
b
ase
ca
s
e
a
n
d
λ
m
ax
,
o
r
id
en
ti
f
ied
as
th
e
m
ax
i
m
u
m
lo
ad
i
n
g
p
o
s
itio
n
[
1
3
]
.
Du
r
in
g
t
h
e
a
s
s
es
s
m
en
t,
th
e
w
ea
k
est
o
r
cr
itical
b
u
s
a
m
o
n
g
t
h
e
n
et
w
o
r
k
a
n
d
m
ax
i
m
u
m
lo
ad
th
at
i
t
ca
n
s
u
s
tai
n
ca
n
also
b
e
d
eter
m
i
n
ed
.
V
o
l
t
a
g
e
L
o
a
d
M
a
r
g
i
n
L
o
a
d
λ
0
λ
m
a
x
Fig
u
r
e
1
.
L
o
ad
Ma
r
g
in
Ass
es
s
m
en
t
T
h
e
lo
ad
m
ar
g
in
is
d
eter
m
i
n
e
d
b
y
an
in
cr
e
m
en
t
o
f
lo
ad
at
0
.
0
5
o
r
5
%
r
e
p
ea
ted
ly
f
r
o
m
t
h
e
o
v
er
all
lo
ad
.
I
n
th
e
ap
p
r
o
ac
h
,
m
i
n
i
m
u
m
v
o
lta
g
e,
V
m
i
n
h
a
s
b
ee
n
s
et
at
0
.
8
5
V
as
th
e
cu
t
o
f
f
p
o
in
t
f
o
r
th
e
v
o
ltag
e
li
m
it
an
d
th
e
s
y
s
te
m
i
s
as
s
u
m
ed
to
o
p
er
ate
in
s
tr
es
s
s
itu
a
tio
n
wh
en
r
ea
ch
i
n
g
th
is
v
al
u
e
[
1
4
]
.
T
h
e
f
lo
w
ch
ar
t
a
s
i
n
Fig
u
r
e
2
is
p
r
esen
ted
th
e
ca
lc
u
latio
n
o
f
o
b
j
ec
tiv
e
f
u
n
ct
io
n
M
L
P
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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d
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&
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p
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N:
2502
-
4752
Mu
lti Ob
jective
A
d
a
p
tive
Tu
mb
lin
g
B
a
cteria
l F
o
r
a
g
in
g
in
V
A
R
S
o
lu
tio
n
s
fo
r
S
u
s
ta
in
a
b
le
.
.
.
.
(
E
.
E
.
Ha
s
s
a
n
)
131
S
t
a
r
t
I
n
c
r
e
a
s
e
l
o
a
d
R
u
n
l
o
a
d
f
l
o
w
V
m
i
n
<
0
.
85
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e
t
e
r
m
i
n
e
M
L
P
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n
d
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e
s
No
Fig
u
r
e
2
.
Flo
w
C
h
ar
t
to
g
et
M
L
P
2
.
2
.
M
ini
m
izing
t
o
t
a
l sy
s
t
e
m
lo
s
s
es o
bje
ct
iv
e
F
un
ct
io
n
T
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
f
o
r
to
tal
lo
s
s
m
i
n
i
m
izat
io
n
is
g
i
v
en
b
y
eq
u
atio
n
(
1
)
.
=
∑
,
(
,
)
=
∑
∈
=
(
,
)
(
2
∈
+
2
−
2
)
MW
(
1
)
≤
≤
∈
≤
≤
∈
{
,
}
w
h
er
e,
Q
i
a
n
d
Q
j
a
r
e
r
ea
ctiv
e
p
o
w
er
at
s
en
d
in
g
an
d
r
ec
eiv
i
n
g
b
u
s
es
r
esp
ec
tiv
el
y
,
is
g
en
er
ated
r
ea
ctiv
e
p
o
w
er
o
f
b
u
s
i,
ar
e
v
o
ltag
e
m
ag
n
it
u
d
e
at
s
e
n
d
in
g
a
n
d
r
ec
eiv
i
n
g
b
u
s
es
r
esp
ec
ti
v
el
y
.
,
is
to
tal
ac
tiv
e
p
o
w
er
lo
s
s
o
v
er
th
e
n
et
w
o
r
k
,
is
lo
ad
b
u
s
,
is
v
o
lta
g
e
co
n
tr
o
lled
b
u
s
an
d
is
r
ef
er
en
ce
(
s
lack
)
b
u
s
.
2
.
3
.
T
he
w
eig
hte
d su
m
m
et
ho
d
T
h
e
ap
p
r
o
ac
h
th
at
u
s
ed
to
f
o
r
m
u
late
t
w
o
o
r
m
o
r
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
a
n
d
r
ep
r
esen
ts
i
n
to
o
n
e
g
e
n
er
al
m
at
h
e
m
a
tical
f
o
r
m
u
la
as d
escr
ib
ed
in
eq
u
atio
n
(
2
)
.
=
∑
(
×
)
=
1
(
2
)
w
h
er
e
∑
=
1
=
1
an
d
=
m
ax
(
)
−
m
ax
(
)
−
m
i
n
(
)
k
i
s
n
u
m
b
er
s
o
f
o
b
j
ec
tiv
e
f
u
n
ctio
n
,
α
i
i
s
w
ei
g
h
t
in
g
f
ac
to
r
f
o
r
i
th
o
b
j
ec
tiv
e
f
u
n
c
tio
n
a
n
d
f
ni
is
n
o
r
m
alis
ed
v
a
lu
e
f
o
r
i
th
o
b
j
ec
tiv
e
f
u
n
ctio
n
.
3.
M
E
T
H
O
DO
L
O
G
Y
3
.
1
.
B
a
ct
er
ia
l F
o
ra
g
ing
O
ptim
iza
t
io
n Alg
o
rit
h
m
B
ac
ter
ial
Fo
r
ag
in
g
Op
ti
m
izati
o
n
(
B
FO)
alg
o
r
ith
m
i
s
m
o
tiv
a
ted
th
r
o
u
g
h
t
h
e
f
o
r
ag
i
n
g
ac
tiv
i
ties
o
f
t
h
e
E
s
ch
er
ic
h
ia
co
li
(
E
.
co
li)
b
ac
te
r
ia.
T
h
e
d
etails
o
n
th
e
b
io
lo
g
ical
asp
ec
ts
,
r
eg
ar
d
in
g
to
th
eir
h
u
n
ti
n
g
s
tr
ateg
ie
s
,
co
n
s
id
er
ed
th
eir
m
o
tile
b
eh
a
v
i
o
r
f
o
r
d
ec
is
io
n
-
m
a
k
i
n
g
m
ec
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d
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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n
J
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E
n
g
&
C
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m
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133
4.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
4
.
1
.
Resul
t
f
o
r
m
ulti
-
o
bje
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e
o
f
SCRP
P
I
n
itiall
y
,
th
e
i
n
cr
ea
s
e
in
t
h
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ML
P
b
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o
r
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an
d
af
te
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e
i
m
p
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m
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f
m
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b
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SC
R
P
P
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th
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cr
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b
u
s
d
u
r
in
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C
a
s
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1
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d
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u
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C
ase
2
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is
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th
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co
m
p
ar
is
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p
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w
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p
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lled
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A
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r
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y
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Fi
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4
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u
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tr
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th
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ap
h
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P
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m
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n
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t.
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h
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also
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o
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d
f
o
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d
if
f
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en
t
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tech
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w
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th
d
if
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n
t o
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f
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n
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.
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a
x
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Fig
u
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4
.
Gr
ap
h
to
Dep
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th
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in
t A
(
b
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e
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m
p
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m
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o
f
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C
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)
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d
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in
t B
(
af
ter
th
e
i
m
p
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n
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f
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C
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)
T
h
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n
d
etails
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s
t
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m
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lti
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T
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d
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s
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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–
136
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er
e
s
tu
d
ied
a
n
d
it
w
a
s
f
o
u
n
d
t
h
at
o
p
ti
m
izin
g
R
P
D,
C
P
an
d
T
T
C
S
s
i
m
u
lta
n
eo
u
s
l
y
o
b
tain
ed
th
e
b
est
r
es
u
lt
s
.
T
h
u
s
,
MO
A
T
B
FO
w
a
s
u
tili
ze
d
i
n
MO
SC
R
P
P
in
o
r
d
er
to
o
p
tim
ize
t
h
e
R
P
D,
C
P
a
n
d
T
T
C
S
s
i
m
u
lta
n
eo
u
s
l
y
s
o
t
h
a
t
th
e
o
p
ti
m
al
r
es
u
lt
s
wo
u
ld
b
e
p
r
o
v
id
ed
.
T
h
e
p
e
r
f
o
r
m
an
ce
o
f
M
O
A
T
B
FO
w
as
c
o
m
p
ar
ed
w
it
h
th
at
o
f
MO
B
F
O
an
d
MO
Me
ta
-
E
P
.
T
h
r
o
u
g
h
o
u
t
th
e
a
n
al
y
s
i
s
,
t
h
e
MO
A
T
B
FO
s
h
o
w
s
t
h
e
b
est
ac
h
ie
v
e
m
e
n
t
i
n
ter
m
s
o
f
ML
P
i
m
p
r
o
v
e
m
e
n
t,
m
i
n
i
m
u
m
v
o
ltag
e
i
m
p
r
o
v
e
m
e
n
t a
s
w
ell
i
n
to
tal
lo
s
s
es
m
in
i
m
izatio
n
.
ACK
N
O
WL
E
D
G
M
E
NT
S
T
h
an
k
y
o
u
to
Mi
n
is
tr
y
o
f
Hi
g
h
er
E
d
u
ca
t
io
n
Ma
la
y
s
ia
(
M
OHE
)
an
d
Un
i
v
er
s
it
i
T
ek
n
i
k
a
l
Ma
la
y
s
ia
Me
lak
a
(
UT
eM
)
w
it
h
f
i
n
a
n
cia
l
s
u
p
p
o
r
t
th
r
o
u
g
h
th
e
g
r
a
n
t
R
A
G
S/1
/2
0
1
5
/
T
K0
/FKE
/0
3
/B
0
0
9
4
.
T
h
e
s
u
p
p
o
r
t
is
g
r
atef
u
ll
y
ac
k
n
o
w
led
g
ed
.
RE
F
E
R
E
NC
E
S
[1
]
Z.
Zak
a
ria,
e
t
a
l
.
,
“
Eco
n
o
m
ic
L
o
a
d
Disp
a
tch
v
ia
a
n
I
m
p
ro
v
e
d
Ba
c
teria
l
F
o
ra
g
in
g
Op
ti
m
iza
ti
o
n
,”
2
0
1
4
IEE
E
8
t
h
In
ter
n
a
t
io
n
a
l
Po
we
r E
n
g
i
n
e
e
rin
g
a
n
d
Op
ti
miz
a
ti
o
n
C
o
n
fer
e
n
c
e
(
PE
OCO
2
0
1
4
).
L
a
n
g
k
a
wi
,
p
p
.
3
8
0
-
3
8
5
,
2
0
1
4
.
[2
]
Y.
K.
W
u
,
e
t
a
l.
,
“
L
it
e
ra
tu
re
Re
v
iew
o
f
P
o
w
e
r
S
y
ste
m
Blac
k
o
u
ts
,”
En
e
rg
y
Pro
c
e
d
i
a
,
v
o
l
.
1
4
1
,
p
p
.
4
2
8
-
4
3
1
,
2
0
1
7
.
[3
]
D.
Ga
n
,
e
t
a
l.
,
“
Larg
e
-
sc
a
le
v
a
r
o
p
ti
m
iza
ti
o
n
a
n
d
p
lan
n
in
g
b
y
tab
u
se
a
rc
h
,”
El
e
c
tric
Po
we
r
S
y
ste
ms
Res
e
a
rc
h
,
v
o
l.
39
,
p
p
.
1
9
5
-
2
0
4
,
1
9
9
6
.
[4
]
B.
Bh
a
tt
a
c
h
a
ry
y
a
a
n
d
R.
Ba
b
u
,
“
T
e
a
c
h
in
g
L
e
a
rn
in
g
Ba
se
d
Op
ti
m
iza
ti
o
n
a
lg
o
ri
t
h
m
f
o
r
re
a
c
ti
v
e
p
o
w
e
r
p
lan
n
in
g
,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
Po
we
r
&
En
e
rg
y
S
y
ste
ms
,
v
o
l.
81
,
p
p
.
2
4
8
-
2
5
3
,
2
0
1
6
.
[5
]
B.
T
a
m
i
m
i,
e
t
a
l.
,
“
Eff
e
c
t
o
f
R
e
a
c
ti
v
e
P
o
w
e
r
L
i
m
it
M
o
d
e
li
n
g
o
n
M
a
x
im
u
m
S
y
ste
m
L
o
a
d
in
g
a
n
d
A
c
ti
v
e
a
n
d
Re
a
c
ti
v
e
P
o
w
e
r
M
a
rk
e
ts
,”
P
o
we
r
S
y
ste
ms
,
IEE
E
T
ra
n
sa
c
ti
o
n
s
,
v
o
l.
25
,
p
p
.
1
1
0
6
-
1
1
1
6
,
2
0
1
0
.
[6
]
Z.
W
e
n
ju
a
n
,
e
t
a
l
.
,
“
Re
v
iew
o
f
Re
a
c
ti
v
e
P
o
w
e
r
P
la
n
n
i
n
g
:
Ob
jec
ti
v
e
s,
Co
n
stra
in
ts,
a
n
d
A
lg
o
rit
h
m
s
,”
Po
we
r
S
y
ste
ms
,
IEE
E
T
ra
n
sa
c
ti
o
n
s
,
v
o
l.
22
,
p
p
.
2
1
7
7
-
2
1
8
6
,
2
0
0
7
.
[7
]
H.
S
o
n
g
,
e
t
a
l.
,
“
Re
a
c
ti
v
e
o
p
ti
m
a
l
p
o
w
e
r
f
lo
w
in
c
o
rp
o
ra
ti
n
g
m
a
r
g
in
e
n
h
a
n
c
e
m
e
n
t
c
o
n
stra
in
ts w
it
h
n
o
n
li
n
e
a
r
i
n
terio
r
p
o
i
n
t
m
e
th
o
d
,”
Ge
n
e
ra
ti
o
n
,
T
r
a
n
s
miss
io
n
a
n
d
Distrib
u
ti
o
n
,
IEE
E
P
ro
c
e
e
d
in
g
,
v
o
l.
1
5
2
,
p
p
.
9
6
1
-
9
6
8
,
2
0
0
5
.
[8
]
D.
Ga
n
,
e
t
a
l.
,
“
Larg
e
-
sc
a
le
v
a
r
o
p
ti
m
iza
ti
o
n
a
n
d
p
lan
n
in
g
b
y
ta
b
u
se
a
rc
h
,”
El
e
c
tric
Po
we
r
S
y
ste
ms
Res
e
a
rc
h
,
v
o
l.
39
,
p
p
.
1
9
5
-
2
0
4
,
1
9
9
6
.
[9
]
Z.
W
e
n
ju
a
n
a
n
d
L
.
M
.
T
o
lb
e
rt
,
“
S
u
rv
e
y
o
f
re
a
c
ti
v
e
p
o
w
e
r
p
lan
n
in
g
m
e
th
o
d
s
,”
IEE
E
Po
we
r
E
n
g
i
n
e
e
rin
g
S
o
c
iety
Ge
n
e
ra
l
M
e
e
ti
n
g
,
v
o
l
.
2
,
p
p
.
1
4
3
0
-
1
4
4
0
,
2
0
0
5
.
[1
0
]
M
.
Eg
h
b
a
l,
e
t
a
l.
,
“
A
p
p
li
c
a
ti
o
n
o
f
m
e
tah
e
u
risti
c
m
e
th
o
d
s
t
o
re
a
c
ti
v
e
p
o
w
e
r
p
lan
n
i
n
g
:
a
c
o
m
p
a
ra
ti
v
e
stu
d
y
f
o
r
GA
,
P
S
O an
d
E
P
S
O
,”
S
y
ste
ms
,
M
a
n
a
n
d
Cy
b
e
rn
e
ti
c
s,
2
0
0
7
.
IS
IC.
IEE
E
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
,
p
p
.
3
7
5
5
-
3
7
6
0
,
2
0
0
7
.
[1
1
]
W
.
Yu
ro
n
g
,
e
t
a
l.
,
“
Re
a
c
ti
v
e
P
o
w
e
r
P
lan
n
i
n
g
Ba
se
d
o
n
F
u
z
z
y
Cl
u
ste
rin
g
,
G
ra
y
Co
d
e
,
a
n
d
S
im
u
la
ted
A
n
n
e
a
li
n
g
,”
Po
we
r S
y
ste
ms
,
IEE
E
T
ra
n
s
a
c
ti
o
n
s
,
v
o
l.
26
,
p
p
.
2
2
4
6
-
2
2
5
5
,
2
0
1
1
.
[1
2
]
S
.
M
ish
ra
,
“
H
y
b
rid
lea
st
-
sq
u
a
re
a
d
a
p
ti
v
e
b
a
c
t
e
rial
f
o
ra
g
in
g
s
trate
g
y
f
o
r
h
a
r
m
o
n
ic
e
sti
m
a
ti
o
n
,”
Ge
n
e
ra
ti
o
n
,
T
ra
n
sm
issio
n
a
n
d
Distrib
u
ti
o
n
,
I
EE
E
Pro
c
e
e
d
i
n
g
s
,
v
o
l.
1
5
2
,
p
p
.
3
7
9
-
3
8
9
,
2
0
0
5
.
[1
3
]
N
.
Am
in
u
d
in
,
e
t
a
l.
,
“
Op
ti
m
a
l
P
o
w
e
r
F
lo
w
f
o
r
L
o
a
d
M
a
rg
in
Im
p
ro
v
e
m
e
n
t
u
sin
g
Ev
o
lu
t
io
n
a
ry
P
r
o
g
ra
m
m
in
g
,”
Res
e
a
rc
h
a
n
d
De
v
e
lo
p
me
n
t,
2
0
0
7
.
S
CORe
D 2
0
0
7
.
5
th
S
t
u
d
e
n
t
Co
n
f
e
re
n
c
e
,
p
p
.
1
-
6
,
2
0
0
7
.
[1
4
]
Z.
M
.
Ya
sin
,
e
t
a
l.
,
“
M
u
lt
i
o
b
jec
ti
v
e
q
u
a
n
tu
m
-
in
sp
ired
e
v
o
lu
ti
o
n
a
ry
p
ro
g
ra
m
m
in
g
f
o
r
o
p
ti
m
a
l
lo
c
a
ti
o
n
a
n
d
siz
in
g
o
f
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
,”
S
u
st
a
i
n
a
b
le
Util
iza
ti
o
n
a
n
d
De
v
e
lo
p
me
n
t
in
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
(
S
T
UD
ENT
),
2
0
1
2
IE
EE
C
o
n
fer
e
n
c
e
,
p
p
.
2
3
3
-
2
3
8
,
2
0
1
2
.
[1
5
]
E.
E
Ha
ss
a
n
,
e
t
a
l.
,
“
Im
p
ro
v
e
d
A
d
a
p
ti
v
e
T
u
m
b
li
n
g
Ba
c
teria
l
F
o
ra
g
in
g
Op
ti
m
iza
ti
o
n
(A
T
BF
O
)
f
o
r
e
m
issio
n
c
o
n
stra
in
e
d
e
c
o
n
o
m
ic d
isp
a
tch
p
r
o
b
lem
,”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
W
o
rl
d
Co
n
g
re
ss
o
n
E
n
g
i
n
e
e
rin
g
,
v
o
l.
2
,
p
p
.
1
-
4
,
2
0
1
2
.
[1
6
]
E.
E.
Ha
ss
a
n
,
e
t
a
l
.
,
“
A
d
a
p
ti
v
e
T
u
m
b
li
n
g
Ba
c
teria
l
F
o
ra
g
in
g
f
o
r
S
u
sta
i
n
a
b
le
Eco
n
o
m
ic
Lo
a
d
Disp
a
tch
,”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
1
2
t
h
W
S
EA
S
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
,
p
p
.
2
4
1
-
231
,
2
0
1
3
.
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