I
nte
rna
t
io
na
l J
o
urna
l o
f
Adv
a
nces in Applie
d Science
s
(
I
J
AAS)
Vo
l.
10
,
No
.
3
,
Sep
tem
b
er
2
0
2
1
,
p
p
.
20
5
~
21
1
I
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In
t
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b
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t
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m
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n
d
im
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k
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t
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e
p
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se
d
a
lg
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m
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n
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term
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iat
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s
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li
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t
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a
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d
t
h
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n
e
x
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re
s
th
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in
term
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iate
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tai
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b
a
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m
a
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ti
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lo
c
a
l
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a
rc
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imp
lem
e
n
ted
in
th
i
s
p
ro
p
o
se
d
a
lg
o
rit
h
m
.
Th
is
tec
h
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e
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h
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th
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IKS
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lg
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m
h
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s
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n
p
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p
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se
d
f
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ti
m
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l
re
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c
ti
v
e
p
o
we
r
p
r
o
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lem
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in
it
i
a
l
p
h
a
se
,
a
ra
n
d
o
m
p
o
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u
latio
n
o
f
p
r
o
b
a
b
le
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lu
ti
o
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s
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re
a
ted
a
n
d
re
-
a
b
so
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ti
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n
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se
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ti
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ti
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re
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th
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se
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rc
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t
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e
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h
e
d
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o
t
h
e
a
l
g
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rit
h
m
.
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e
a
lg
o
rit
h
m
h
a
s
b
e
e
n
b
u
il
t
to
a
d
v
a
n
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se
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rc
h
e
v
e
n
a
p
o
ten
ti
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l
so
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t
io
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m
o
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e
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wa
ste
(W
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a
n
d
it
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b
e
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t
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a
c
k
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tere
d
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(
F
B).
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lo
m
e
ru
lar
f
il
tratio
n
ra
te
(G
F
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tes
t
is
u
ti
l
ize
d
to
v
e
rify
th
e
fit
n
e
ss
o
f
k
id
n
e
y
s.
Be
tt
e
r
e
fficie
n
c
y
o
f
t
h
e
p
ro
p
o
se
d
M
ZA,
AB
,
a
n
d
IKS
a
lg
o
rit
h
m
c
o
n
firme
d
b
y
su
c
c
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ss
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ti
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n
d
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rd
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u
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118
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u
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n
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b
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m
s.
T
h
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l
ts
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o
w
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h
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t
a
c
ti
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e
p
o
we
r
l
o
ss
h
a
s
b
e
e
n
re
d
u
c
e
d
.
K
ey
w
o
r
d
s
:
Au
g
m
en
ted
b
at
I
m
p
r
o
v
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k
i
d
n
ey
s
ea
r
ch
Mo
u
n
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ze
b
r
a
Op
tim
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ea
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n
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CC B
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se
.
C
o
r
r
e
s
p
o
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ing
A
uth
o
r
:
L
en
in
Kan
ag
asab
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Dep
ar
tm
en
t o
f
E
lectr
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an
d
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lectr
o
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n
g
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g
Pra
s
ad
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Po
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d
h
ar
th
a
I
n
s
titu
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o
f
T
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h
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r
u
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awa
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a
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d
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r
a
Pra
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esh
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2
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7
,
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d
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m
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1.
I
NT
RO
D
UCT
I
O
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T
o
m
in
im
ize
th
e
tr
u
e
p
o
wer
l
o
s
s
is
k
ey
aim
in
th
is
r
ea
ctiv
e
p
o
wer
o
p
tim
izatio
n
p
r
o
b
le
m
.
Var
io
u
s
tech
n
iq
u
es
[
1
]
-
[
6
]
h
av
e
b
ee
n
a
p
p
lied
to
s
o
lv
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th
e
r
ea
ctiv
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p
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ile
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[
1
6
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a
p
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to
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ea
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p
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b
u
t
m
an
y
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o
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ith
m
s
s
tu
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also
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aile
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alan
ce
th
e
e
x
p
lo
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atio
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a
n
d
ex
p
lo
itatio
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d
u
r
in
g
th
e
s
ea
r
ch
o
f
g
l
o
b
al
s
o
l
u
tio
n
.
I
n
th
is
wo
r
k
m
o
u
n
tain
ze
b
r
a
alg
o
r
it
h
m
(
MZ
A)
,
au
g
m
en
te
d
b
at
alg
o
r
ith
m
(
AB
)
,
an
d
im
p
r
o
v
ed
k
id
n
ey
s
ea
r
ch
(
I
KS)
alg
o
r
ith
m
is
ap
p
lied
f
o
r
s
o
l
v
in
g
r
ea
ctiv
e
p
o
wer
o
p
tim
izatio
n
p
r
o
b
lem
.
MZ
A
em
u
l
ates
th
e
s
ea
r
ch
in
g
tech
n
iq
u
es
o
f
th
e
m
o
u
n
tain
z
eb
r
a
b
eh
av
io
u
r
.
Ma
in
ly
m
o
u
n
tain
ze
b
r
a
u
tili
ze
its
s
p
ec
ial
lo
g
ical,
c
o
o
p
er
ativ
e
an
d
s
elf
-
d
eter
m
in
i
n
g
ap
p
r
o
ac
h
in
its
s
ea
r
ch
to
f
in
d
th
e
g
r
ass
lan
d
.
T
h
is
tech
n
iq
u
e
en
h
an
ce
s
th
e
s
ea
r
ch
p
r
o
ce
d
u
r
e
in
r
ap
id
m
o
d
e.
T
h
e
n
th
is
p
ap
er
p
r
o
p
o
s
es
AB
alg
o
r
ith
m
to
s
o
lv
e
o
p
tim
al
r
ea
cti
v
e
p
o
wer
p
r
o
b
lem
.
B
at
alg
o
r
ith
m
is
m
im
ick
ed
f
r
o
m
th
e
ac
tio
n
s
o
f
th
e
b
at
an
d
it
f
ly
r
an
d
o
m
ly
to
lo
o
k
f
o
r
th
e
p
r
ey
.
W
av
elen
g
th
ca
n
b
e
ad
ju
s
ted
r
eg
u
lar
ly
an
d
ca
n
co
n
tr
o
l
th
e
r
ate
o
f
p
u
ls
e
e
m
is
s
io
n
r
∈
[
0
;
1
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d
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n
d
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p
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p
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Stan
d
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b
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alg
o
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ith
m
o
f
ten
f
alls
in
to
th
e
lo
ca
l
o
p
tim
a
wh
en
ap
p
lied
to
m
an
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o
p
tim
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p
r
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s
.
I
n
th
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p
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p
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s
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AB
alg
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ith
m
lo
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an
in
ter
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d
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tate
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as
b
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estab
lis
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ed
at
f
ir
s
t,
an
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th
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n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
: 2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
10
,
No
.
3
,
Sep
tem
b
er
2
0
2
1
:
20
5
–
21
1
206
ex
p
lo
r
e
th
e
in
ter
m
e
d
iate
s
tat
e
in
o
r
d
er
to
o
b
tain
th
e
g
lo
b
al
o
p
tim
a.
I
n
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
,
b
alan
c
e
b
etwe
en
lo
ca
l
an
d
g
l
o
b
al
s
ea
r
ch
h
as
b
ee
n
m
ain
tain
e
d
.
Mo
v
em
en
ts
o
f
th
e
b
ats
b
y
to
g
g
le
b
etwe
en
lo
ca
l
s
ea
r
ch
an
d
g
lo
b
al
s
ea
r
ch
is
co
n
tr
o
lled
b
y
th
e
p
u
ls
e
r
ate
r
an
d
r
ec
eip
t
o
r
r
ef
u
s
al
o
f
a
n
ew
-
f
a
n
g
led
e
n
g
en
d
e
r
ed
s
o
lu
tio
n
is
co
n
tr
o
lled
b
y
lo
u
d
n
ess
A.
T
h
en
i
n
th
is
w
o
r
k
,
I
KS
al
g
o
r
ith
m
is
p
r
o
p
o
s
ed
to
s
o
lv
e
t
h
e
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
.
I
t
im
itates
as
s
o
r
ted
p
r
o
g
r
ess
io
n
o
f
a
b
io
lo
g
ical
k
id
n
ey
.
Fo
u
r
m
ai
n
ac
tiv
it
ies:
f
iltra
tio
n
,
r
e
-
ab
s
o
r
p
tio
n
,
s
ec
r
etio
n
,
an
d
ex
cr
etio
n
p
lay
m
ajo
r
r
o
l
e
in
th
e
o
p
er
atio
n
o
f
k
id
n
ey
s
.
Kid
n
ey
s
f
u
n
ctio
n
p
lay
s
m
ajo
r
r
o
le
in
u
r
in
e
f
o
r
m
atio
n
an
d
b
lo
o
d
f
iltra
tio
n
in
th
e
h
u
m
an
b
o
d
y
.
Fu
n
d
am
e
n
tally
,
th
e
k
i
d
n
ey
s
s
u
p
e
r
v
is
e
t
h
e
q
u
a
n
tity
o
f
io
n
s
in
th
e
b
lo
o
d
a
n
d
also
d
ec
r
ea
s
e
th
e
p
r
esen
ce
o
f
s
u
r
p
lu
s
wate
r
an
d
waste.
I
n
in
itial
p
h
ase,
a
r
an
d
o
m
p
o
p
u
latio
n
o
f
p
r
o
b
ab
le
s
o
lu
tio
n
s
is
cr
ea
ted
an
d
r
e
-
a
b
s
o
r
p
tio
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,
s
ec
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etio
n
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d
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cr
etio
n
ar
e
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itated
in
th
e
s
ea
r
ch
p
r
o
ce
s
s
to
ch
ec
k
v
ar
io
u
s
co
n
d
itio
n
s
e
n
tr
en
ch
ed
to
th
e
alg
o
r
ith
m
.
T
h
e
alg
o
r
ith
m
is
b
u
ilt
to
im
p
r
o
v
e
th
e
s
ea
r
ch
ev
en
a
p
o
ten
tial
s
o
lu
tio
n
m
o
v
ed
to
waste
(
W
)
an
d
it
will
b
e
b
r
o
u
g
h
t
b
ac
k
to
th
e
f
ilter
ed
b
lo
o
d
(
FB
)
.
Glo
m
e
r
u
lar
f
iltra
tio
n
r
ate
(
GFR
)
test
i
s
u
tili
ze
d
to
v
er
if
y
th
e
f
itn
ess
o
f
k
id
n
ey
s
.
Pro
p
o
s
ed
MZ
A,
AB
,
an
d
I
KS
alg
o
r
ith
m
ef
f
icien
cy
is
v
er
if
ied
b
y
test
in
g
it in
s
tan
d
ar
d
I
E
E
E
1
4
-
b
u
s
,
1
1
8
-
b
u
s
,
an
d
3
0
0
-
b
u
s
test
s
y
s
t
em
s
.
2.
O
B
J
E
CT
I
V
E
F
UNC
T
I
O
N
Ob
jectiv
e
o
f
t
h
e
p
r
o
b
le
m
is
t
o
r
e
d
u
ce
t
h
e
t
r
u
e
p
o
w
e
r
l
o
s
s
as
(
1
)
.
F
=
P
L
=
∑
g
k
k
∈
Nbr
(
V
i
2
+
V
j
2
−
2
V
i
V
j
c
os
θ
ij
)
(
1
)
Vo
ltag
e
d
e
v
ia
ti
o
n
is
g
i
v
e
n
as
(
2
)
.
F
=
P
L
+
ω
v
×
Vol
ta
ge
De
via
tion
(
2
)
Vo
ltag
e
d
e
v
ia
ti
o
n
is
g
i
v
e
n
b
y
(
3
)
.
Vol
ta
ge
De
via
tion
=
∑
|
V
i
−
1
|
N
p
q
i
=
1
(
3
)
I
n
eq
u
ality
co
n
s
t
r
a
in
t
is
g
i
v
e
n
b
y
(
4
)
.
P
G
=
P
D
+
P
L
(
4
)
I
n
i
n
e
q
u
a
lit
y
c
o
n
s
tr
ai
n
ts
a
r
e
g
i
v
e
n
b
y
(
5
)
-
(
9
)
.
P
g
s
l
a
ck
m
i
n
≤
P
g
s
l
ack
≤
P
g
s
l
ack
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
O
UNT
AIN
Z
E
B
RA
A
L
G
O
RIT
H
M
Nat
u
r
al
b
e
h
av
io
u
r
o
f
m
o
u
n
tai
n
z
e
b
r
a
d
u
r
i
n
g
t
h
e
s
c
av
en
g
i
n
g
en
d
ea
v
o
r
s
h
as
b
ee
n
p
r
et
e
n
d
e
d
i
n
MZA
.
Mo
u
n
ta
in
z
eb
r
a
s
e
ar
ch
i
n
g
r
o
u
p
t
o
f
i
n
d
t
h
e
f
o
o
d
.
Alik
e
to
p
lain
ze
b
r
as,
m
o
u
n
tain
ze
b
r
as
d
o
n
o
t
b
e
in
cu
m
u
lativ
e
in
to
b
ig
h
er
d
s
.
O
n
ly
th
ey
co
m
p
o
s
e
as
s
m
all
f
am
ily
g
r
o
u
p
s
co
n
s
is
tin
g
o
f
a
s
o
lo
s
tallio
n
ar
o
u
n
d
o
n
e
to
f
iv
e
m
ar
es,
jo
in
tly
with
th
eir
cu
r
r
en
t o
f
f
s
p
r
in
g
’
s
.
Sin
g
le
m
ales r
esid
e
in
d
iv
id
ed
g
r
o
u
p
s
,
an
d
ad
u
lt si
n
g
le
tr
y
to
co
n
f
in
e
y
o
u
n
g
m
ar
es to
s
et
u
p
a
h
a
r
em
.
I
n
th
is
th
ey
a
r
e
d
i
v
er
g
en
t
b
y
th
e
lea
d
in
g
s
tallio
n
o
f
th
e
g
r
o
u
p
.
At
a
s
in
g
l
e
ti
m
e
,
m
a
r
es
g
i
v
e
b
ir
t
h
t
o
o
n
e
f
o
al
.
F
o
r
a
b
o
u
t
a
y
ea
r
,
t
h
e
f
o
al
f
ee
d
s
m
ai
n
l
y
o
n
i
ts
m
o
th
er
'
s
m
ilk
af
ter
wh
ich
it
is
wea
n
ed
o
n
to
h
ar
d
s
ea
r
ch
.
B
etwe
en
th
e
ag
es
o
f
1
3
an
d
3
7
m
o
n
th
s
m
o
u
n
tain
ze
b
r
a
f
o
als
co
m
m
o
n
l
y
m
o
v
e
a
way
f
r
o
m
th
eir
m
ater
n
al
h
e
r
d
s
f
o
r
s
o
m
e
tim
e.
C
o
n
v
er
s
ely
Har
tm
a
n
n
'
s
m
o
u
n
tain
ze
b
r
a
m
ar
es
f
o
r
ce
o
u
t
th
eir
f
o
als
w
h
en
th
e
y
a
r
e
a
g
ed
ar
o
u
n
d
1
4
to
1
6
m
o
n
th
s
.
Fo
r
a
w
h
ile,
y
o
u
n
g
m
ales
m
ay
wan
d
er
alo
n
e
b
ef
o
r
e
jo
i
n
in
g
a
b
ac
h
elo
r
g
r
o
u
p
,
wh
ile
f
em
al
es
ar
e
eith
er
tak
e
n
in
to
a
n
o
th
er
b
r
ee
d
i
n
g
h
er
d
o
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2
2
5
2
-
8
8
1
4
Tr
u
e
p
o
w
er lo
s
s
r
ed
u
ctio
n
b
y
mo
u
n
ta
in
z
eb
r
a
,
a
u
g
me
n
ted
b
a
t a
n
d
i
mp
r
o
ve
d
kid
n
ey
…
(
Le
n
in
K
a
n
a
g
a
s
a
b
a
i)
207
ar
e
jo
in
ed
b
y
a
s
in
g
le
m
a
le
to
s
tr
u
ctu
r
e
a
n
o
v
el
b
r
ee
d
in
g
h
er
d
.
I
n
d
e
p
e
n
d
e
n
t
n
at
u
r
e
o
f
m
o
u
n
tai
n
z
eb
r
a
is
id
e
n
t
if
ie
d
a
n
d
i
n
t
eg
r
a
te
d
in
to
t
h
e
al
g
o
r
it
h
m
.
I
t
h
as
b
ee
n
r
e
p
r
e
s
en
t
e
d
b
y
(
1
0
)
.
c
k
+
1
=
c
k
+
r
g1
a
1
(
lo
m
ax
−
d
k
)
+
r
g2
a
2
(
lf
m
ax
−
d
k
)
(1
0)
W
h
e
r
e
c
k
,
d
k
r
ep
r
es
e
n
ts
th
e
ex
p
l
o
r
ati
o
n
a
n
d
e
x
p
l
o
it
ati
o
n
;
r
g1
,
r
g2
ar
e
le
ar
n
i
n
g
f
a
ct
o
r
s
;
a
1
,
a
2
a
r
e
r
a
n
d
o
m
n
u
m
b
e
r
s
.
T
o
p
r
o
m
i
n
en
t
(
b
est
)
m
o
u
n
t
ai
n
z
eb
r
a
t
h
e
r
e
wil
l
b
e
a
n
i
n
t
er
f
ac
e
wit
h
n
u
m
e
r
o
u
s
m
o
u
n
tai
n
ze
b
r
a
s
(
1
0
)
,
an
d
c
o
m
p
a
r
is
o
n
will
b
e
t
h
e
r
e
wit
h
ea
c
h
m
o
u
n
tai
n
z
eb
r
a
.
M
o
v
e
m
e
n
t
t
o
o
t
h
er
l
o
c
ati
o
n
s
is
ex
p
r
ess
e
d
b
y
(
1
1
)
.
d
k
+
1
=
λ
(
c
k
+
d
k
)
(
1
1
)
E
ac
h
m
o
u
n
t
ai
n
z
eb
r
a
’
s
f
it
n
es
s
w
ill
b
e
u
p
d
at
e
d
a
n
d
b
o
t
h
th
e
lf
m
ax
(
in
d
i
v
i
d
u
al
M
o
u
n
t
ai
n
Z
e
b
r
a
’
s
lo
c
ati
o
n
)
an
d
lo
m
ax
(
th
e
M
o
u
n
t
ai
n
Z
e
b
r
a’
s
h
e
r
d
’
s
b
est
l
o
c
ati
o
n
)
will
b
e
d
ete
r
m
i
n
e
d
.
W
h
e
n
p
r
e
s
en
t
f
it
n
ess
is
s
u
p
e
r
i
o
r
t
h
e
n
(
lf
m
ax
)
lo
ca
t
io
n
v
ec
to
r
o
f
p
a
r
ti
c
u
la
r
m
o
u
n
tai
n
ze
b
r
a
is
p
r
o
t
ec
t
ed
.
M
o
v
em
e
n
t
o
f
m
o
u
n
tai
n
z
eb
r
a
s
to
s
ta
y
o
r
e
x
p
l
o
it
wi
th
in
t
h
e
e
x
p
l
o
r
ati
o
n
s
p
ac
e
an
d
c
an
b
e
c
o
n
t
r
o
lle
d
b
y
(
1
0
)
an
d
(
1
1
)
.
O
n
co
n
s
i
d
e
r
a
ti
o
n
o
f
t
h
e
two
c
o
m
p
eti
n
g
f
o
r
ce
s
(
lo
m
ax
,
lf
m
ax
)
m
o
u
n
t
ain
z
e
b
r
a
s
m
o
v
e
t
o
s
ea
r
c
h
o
t
h
er
a
r
e
as t
h
r
o
u
g
h
(
1
1
)
.
Dim
en
s
io
n
a
l
ele
m
e
n
t
d
k
is
s
u
b
tr
a
cte
d
f
r
o
m
t
h
e
m
a
x
i
m
u
m
v
ec
t
o
r
a
n
d
m
u
lt
ip
lie
d
b
y
a
n
a
r
b
it
r
a
r
y
n
u
m
b
e
r
(
a
1
,
a
2
)
(
b
e
twe
e
n
0
.
0
a
n
d
0
.
6
)
wi
th
le
ar
n
i
n
g
p
a
r
a
m
et
er
(
r
g1
,
r
g2
)
.
M
o
v
em
en
t
is
u
p
d
ated
f
o
r
g
lo
b
al
an
d
lo
ca
l sear
ch
b
y
(
1
2
)
,
(
1
3
)
.
,
+
1
=
,
+
1
+
(
1
,
)
ʘ
(
,
−
,
)
>
[
ℎ
]
(
1
2
)
,
+
1
=
,
+
1
+
(
1
,
)
ʘ
(
,
−
,
)
≤
[
ℎ
]
(
1
3
)
T
h
en
th
e
p
o
s
itio
n
is
m
o
d
if
ie
d
b
y
(
1
4
)
.
,
+
1
=
,
+
1
+
,
(
1
4
)
W
o
r
s
t f
itn
ess
v
alu
e
s
ep
ar
ato
r
will b
e
im
p
lem
en
ted
b
y
(
1
5
)
.
,
=
+
(
−
+
1
)
×
(
1
5
)
MZA
a
l
g
o
r
it
h
m
f
o
r
s
o
lv
in
g
r
e
ac
ti
v
e
p
o
we
r
p
r
o
b
le
m
Ste
p
a
:
R
ea
c
ti
v
e
p
o
w
e
r
p
r
o
b
l
e
m
o
b
je
cti
v
e
f
u
n
cti
o
n
h
as
b
e
en
in
i
tiat
e
d
in
t
h
e
p
r
o
c
e
ss.
Ste
p
b
:
A
t
r
a
n
d
o
m
m
o
u
n
t
ai
n
z
eb
r
a
s
a
r
e
i
n
iti
ate
d
i
n
th
e
s
o
l
u
t
i
o
n
s
p
a
ce
Ste
p
c
:
F
it
n
ess
v
al
u
es
h
as
b
ee
n
u
p
d
a
te
d
b
y
(
1
0
)
Ste
p
d
:
L
o
c
ati
o
n
o
f
m
o
u
n
t
ai
n
z
eb
r
a
is
m
o
d
i
f
ie
d
b
y
(
1
1
)
Ste
p
e
:
I
s
lo
m
ax
u
p
d
a
ti
n
g
?
I
f
y
es
,
g
o
t
o
s
u
b
s
e
q
u
e
n
t
s
te
p
o
r
els
e
g
o
to
b
Ste
p
f
:
I
f
e
n
d
c
r
i
te
r
i
o
n
is
n
o
t
m
et,
t
h
e
n
g
o
b
ac
k
t
o
s
t
e
p
c
Ste
p
g
:
O
p
ti
m
iz
e
d
o
u
t
p
u
t
4.
AUG
M
E
NT
E
D
B
AT
A
L
G
O
RIT
H
M
B
at
alg
o
r
ith
m
is
m
im
ick
ed
f
r
o
m
th
e
ac
tio
n
s
o
f
th
e
b
at
a
n
d
it
f
ly
r
an
d
o
m
ly
to
lo
o
k
f
o
r
th
e
p
r
ey
.
W
av
elen
g
th
ca
n
b
e
ad
j
u
s
ted
r
eg
u
lar
ly
an
d
ca
n
co
n
tr
o
l
t
h
e
r
ate
o
f
p
u
ls
e
e
m
is
s
io
n
r
∈
[
0
;
1
]
,
d
e
p
en
d
o
n
t
h
e
p
r
o
p
in
q
u
ity
o
f
th
e
tar
g
et
[
1
7
]
.
L
o
u
d
n
ess
is
as
s
u
m
ed
to
b
e
v
ar
y
in
g
f
r
o
m
a
lar
g
e
(
p
o
s
itiv
e)
A
0
to
m
in
im
u
m
co
n
s
tan
t v
alu
e
A
m
i
n
.
Fre
s
h
s
o
lu
tio
n
s
ar
e
p
r
o
d
u
ce
d
b
y
(
1
6
)
-
(
1
8
)
.
f
i
(
t
)
=
f
m
i
n
+
(
f
m
ax
−
f
m
i
n
)
∪
(
0
,
1
)
(
1
6
)
v
i
(
t
+
1
)
=
v
i
t
+
(
x
i
t
−
b
e
s
t
)
Q
i
(
t
)
,
(
1
7
)
x
i
(
t
+
1
)
=
x
i
(
t
)
+
v
i
(
t
)
(
1
8
)
Fo
r
lo
ca
l
s
ea
r
ch
a
ca
p
r
icio
u
s
walk
with
d
ir
ec
t
ex
p
lo
itatio
n
is
u
s
ed
to
m
o
d
er
n
ize
th
e
p
r
es
en
t
m
o
s
t
ex
ce
llen
t
s
o
lu
tio
n
b
y
(
1
9
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
: 2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
10
,
No
.
3
,
Sep
tem
b
er
2
0
2
1
:
20
5
–
21
1
208
x
(
t
)
=
b
e
s
t
+
ϵ
A
i
(
t
)
(
2U
(
0
,
1
)
−
1
)
(
1
9
)
ϵ
-
s
ca
lin
g
f
ac
to
r
,
A
i
(
t
)
-
lo
u
d
n
ess
.
D
ep
en
d
in
g
o
n
th
e
p
u
ls
e
r
ate
r
i
an
d
n
ew
-
f
an
g
led
s
o
lu
tio
n
s
ar
e
ac
ce
p
ted
with
s
o
m
e
p
r
o
x
im
ity
lo
ca
l
s
ea
r
ch
will
b
e
co
m
m
en
ce
d
.
W
h
en
b
a
t
f
in
d
s
a
p
r
ey
r
ate
o
f
p
u
ls
e
e
m
is
s
io
n
r
i
au
g
m
e
n
t
s
an
d
lo
u
d
n
ess
Ai
d
im
in
is
h
ed
,
wh
ich
m
ath
em
atica
lly
wr
itten
b
y
(
2
0
)
.
A
i
(
t
+
1
)
=
α
A
i
(
t
)
,
r
i
(
t
)
=
r
i
(
0
)
[
1
−
e
xp
(
−
γϵ
)
]
(
2
0
)
Stan
d
ar
d
B
at
alg
o
r
ith
m
o
f
ten
f
alls
in
to
th
e
lo
ca
l
o
p
tim
a
wh
en
ap
p
lied
to
m
an
y
o
p
tim
izatio
n
p
r
o
b
lem
s
[
1
7
]
.
I
n
th
e
p
r
o
p
o
s
e
d
AB
alg
o
r
ith
m
,
an
in
ter
m
ed
i
ate
s
tate
is
e
s
tab
lis
h
ed
at
f
ir
s
t
,
an
d
th
en
ex
p
lo
r
e
th
e
in
ter
m
ed
iate
s
tate
in
o
r
d
er
to
o
b
tain
th
e
g
lo
b
al
o
p
tim
a.
I
ter
ativ
e
lo
ca
l sear
ch
im
p
lem
en
t
ed
in
th
is
p
r
o
p
o
s
ed
alg
o
r
ith
m
as f
o
llo
ws
:
Step
a.
∗
d
ef
in
es
th
e
b
est s
o
lu
tio
n
an
d
is
p
er
tu
r
b
ed
to
attain
a
n
in
ter
m
ed
iate
s
tate
∗
∗
g
iv
en
b
y
(
2
1
)
.
∗
∗
=
∗
×
(
)
(
2
1
)
Step
b
.
I
n
ter
m
ed
iate
s
tate
∗
∗
h
a
s
b
ee
n
r
e
-
s
ea
r
ch
ed
in
o
r
d
er
t
o
g
et
t
h
e
lo
ca
l
o
p
tim
al
s
o
l
u
tio
n
∗
′
,
th
e
lo
ca
l
o
p
tim
a
(
∗
′
)
.
Step
c.
W
h
en
(
∗
′
)
<
(
∗
)
,
th
en
∗
′
=
∗
(
∗
)
=
(
∗
′
)
o
th
er
wis
e
(
−
(
(
∗
′
)
−
(
∗
)
)
)
>
(
)
,
th
en
∗
′
=
∗
(
∗
)
=
(
∗
′
)
.
Step
d
.
Ou
tp
u
t th
e
m
o
s
t e
x
ce
ll
en
t so
lu
tio
n
.
Sto
ch
asti
c
in
er
tia
weig
h
t (
SIW)
is
ap
p
lied
in
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
.
=
+
(
−
)
×
+
×
(
)
(
2
2
)
T
h
e
v
elo
ci
ty
o
f
th
e
p
o
p
u
latio
n
u
p
d
ated
b
y
(
2
3
)
.
=
−
1
+
(
−
∗
)
(
2
3
)
I
n
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
b
alan
ce
b
etwe
en
lo
ca
l
a
n
d
g
lo
b
a
l
s
ea
r
ch
h
as
b
ee
n
m
ain
tain
ed
.
Mo
v
em
en
ts
o
f
th
e
b
ats b
y
to
g
g
le
b
etwe
en
lo
ca
l s
ea
r
ch
an
d
g
lo
b
al
s
ea
r
ch
is
co
n
tr
o
lled
b
y
t
h
e
p
u
ls
e
r
ate
r
an
d
r
ec
eip
t o
r
r
e
f
u
s
al
o
f
a
n
ew
-
f
an
g
le
d
en
g
en
d
er
ed
s
o
l
u
tio
n
is
co
n
tr
o
lled
b
y
lo
u
d
n
ess
A
.
=
(
0
−
∞
1
−
)
(
−
)
+
∞
(
2
4
)
=
(
0
−
∞
1
−
)
(
−
)
+
∞
(
2
5
)
T
h
e
p
s
eu
d
o
co
d
e
is
:
Begin
Initialize the bat population
Position and velocity of the bat population initialized
Update
Q
i
(
t
)
=
Q
m
in
+
(
Q
m
a
x
−
Q
m
in
)
∪
(
0
,
1
)
;
po
si
ti
on
an
d
ve
lo
ci
ty
of
ba
ts
ar
e
mo
de
rn
iz
ed
by
x
i
(
t
+
1
)
=
x
i
(
t
)
+
v
i
(
t
)
;
=
−
1
+
(
−
∗
)
Is
r
a
n
do
m
>
r
i
?
; if yes got to next step or else to step “g”
Fr
om
mo
st
ex
ce
ll
en
t
so
lu
ti
on
ch
os
e
a
so
lu
ti
on
an
d
lo
ca
l
so
lu
ti
on
en
ge
nd
e
re
d
by
x
(
t
)
=
b
e
st
+
ϵ
A
i
(
t
)
(
2U
(
0
,
1
)
−
1
)
<
(
)
=
(
∗
)
; if yes go to next step or else got to step “i”
=
(
0
−
∞
1
−
)
(
−
)
+
∞
;
=
(
0
−
∞
1
−
)
(
−
)
+
∞
Update the most excellent solution and fitness value
Is
<
?
; if yes got to step “d” or else go to next step
Obtain the perturbed solution
′
by
∗
∗
=
∗
×
(
)
Obtain the best
perturbed solution
′′
and fitness
(
′′
)
Is
(
′′
)
<
(
)
; if yes go to next step or else
−
(
(
(
′′
)
<
(
)
)
)
>
=
′′
,
(
′′
)
=
(
)
<
?
;
if
ye
s
go
to
st
ep
“d
”
or
el
se
re
tu
rn
wi
th
g
lo
ba
l
be
st
so
lu
ti
on
an
d
fi
tn
es
s
value
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2
2
5
2
-
8
8
1
4
Tr
u
e
p
o
w
er lo
s
s
r
ed
u
ctio
n
b
y
mo
u
n
ta
in
z
eb
r
a
,
a
u
g
me
n
ted
b
a
t a
n
d
i
mp
r
o
ve
d
kid
n
ey
…
(
Le
n
in
K
a
n
a
g
a
s
a
b
a
i)
209
5.
I
M
P
RO
V
E
D
K
I
DN
E
Y
S
E
A
RCH
AL
G
O
RI
T
H
M
Kid
n
ey
s
ea
r
ch
alg
o
r
ith
m
im
it
ates
ass
o
r
ted
p
r
o
g
r
ess
io
n
o
f
a
b
io
lo
g
ical
k
id
n
e
y
.
Fo
u
r
m
ain
ac
tiv
it
ies:
f
iltra
tio
n
,
re
-
ab
s
o
r
p
tio
n
,
s
ec
r
et
io
n
,
an
d
e
x
cr
etio
n
p
lay
m
ajo
r
r
o
le
in
th
e
o
p
er
atio
n
o
f
k
id
n
e
y
s
.
I
n
in
itial
p
h
ase,
a
r
an
d
o
m
p
o
p
u
latio
n
o
f
p
r
o
b
a
b
le
s
o
lu
tio
n
s
is
cr
ea
ted
an
d
re
-
ab
s
o
r
p
tio
n
,
s
ec
r
etio
n
an
d
e
x
c
r
etio
n
ar
e
im
itated
in
th
e
s
ea
r
ch
p
r
o
ce
s
s
to
ch
e
ck
v
ar
io
u
s
co
n
d
itio
n
s
en
tr
en
ch
ed
to
th
e
alg
o
r
ith
m
.
T
h
e
a
lg
o
r
ith
m
is
b
u
ilt
to
im
p
r
o
v
e
th
e
s
ea
r
ch
ev
e
n
a
p
o
t
en
tial
s
o
lu
tio
n
m
o
v
e
d
to
W
an
d
it
will
b
e
b
r
o
u
g
h
t
b
ac
k
to
t
h
e
FB
.
GF
R
te
s
t
i
s
u
tili
ze
d
to
v
er
if
y
th
e
f
itn
ess
o
f
k
id
n
e
y
s
[
1
8
]
.
T
h
e
test
ap
p
r
o
x
im
ately
g
iv
es
th
e
ca
p
ac
ity
o
f
b
lo
o
d
th
at
p
ass
th
r
o
u
g
h
t
h
e
g
l
o
m
er
u
li
ev
er
y
m
in
u
te.
Dep
e
n
d
o
n
th
e
GFR
test
co
n
s
eq
u
e
n
ce
w
h
ich
is
l
ess
th
an
1
5
o
r
f
alls
b
etwe
en
1
5
a
n
d
6
0
o
r
is
m
o
r
e
th
an
6
0
a
p
a
r
ticu
lar
d
ee
d
w
ill
b
e
ex
ec
u
ted
.
T
h
is
p
r
o
ce
d
u
r
e
im
p
lem
en
ted
to
im
p
r
o
v
e
th
e
r
ate
o
f
ex
p
lo
r
atio
n
an
d
f
in
d
in
g
th
e
o
p
tim
al
s
o
l
u
tio
n
.
T
h
e
GFR
test
in
g
p
r
o
ce
d
u
r
e
is
ad
d
e
d
at
th
e
en
d
o
f
ev
e
r
y
iter
atio
n
.
W
h
en
GFR
lev
el
is
le
s
s
th
an
1
5
,
th
e
p
r
o
ce
d
u
r
e
is
r
ep
etitiv
e
with
th
e
p
o
p
u
latio
n
in
Fil
ter
ed
B
lo
o
d
.
W
h
en
GFR
le
v
el
is
b
etwe
en
1
5
an
d
6
0
,
a
p
r
o
g
r
ess
o
f
p
r
ac
tical
s
o
lu
tio
n
s
in
Fil
ter
ed
b
lo
o
d
is
im
p
lem
en
ted
as
a
tr
ea
tm
e
n
t
f
o
r
s
h
o
r
ten
e
d
k
id
n
ey
f
u
n
ctio
n
.
T
h
is
p
r
o
g
r
ess
io
n
in
cr
ea
s
es
th
e
ex
p
lo
r
atio
n
ab
ilit
y
an
d
is
p
lan
n
e
d
to
h
elp
th
e
al
g
o
r
ith
m
i
n
d
is
co
v
er
y
o
f
en
h
a
n
ce
d
s
o
lu
tio
n
.
I
f
t
h
e
GFR
lev
el
is
lar
g
er
th
an
6
0
,
th
en
k
id
n
e
y
f
u
n
ctio
n
is
co
m
m
o
n
,
in
wh
ic
h
ca
s
e
n
o
ex
tr
a
p
r
o
g
r
ess
io
n
is
ad
d
ed
to
alg
o
r
ith
m
.
Mo
v
em
en
t
eq
u
atio
n
is
(
2
6
)
.
+
1
=
+
(
−
)
(
2
6
)
Fil
ter
in
g
o
f
th
e
s
o
lu
tio
n
s
is
d
o
n
e
with
a
f
iltra
tio
n
r
ate
an
d
C
alcu
latio
n
o
f
th
e
f
iltra
tio
n
r
ate
(
)
is
d
o
n
e
u
s
in
g
th
e
f
o
llo
win
g
(
2
7
)
.
=
×
∑
(
)
=
1
(
2
7
)
is
a
co
n
s
tan
t
v
alu
e
b
etwe
en
0
an
d
1
an
d
is
attu
n
ed
i
n
ad
v
an
ce
,
s
r
ep
r
esen
ts
th
e
s
ize
o
f
th
e
p
o
p
u
latio
n
,
an
d
(
)
r
ep
r
esen
ts
an
o
b
jectiv
e
f
u
n
cti
o
n
o
f
s
o
lu
tio
n
y
at
ith
iter
atio
n
.
I
n
ev
er
y
iter
atio
n
,
p
r
ev
io
u
s
to
in
teg
r
atio
n
th
e
FB
an
d
W
will
b
e
p
o
p
u
latio
n
f
o
r
th
e
s
u
b
s
eq
u
en
t
iter
atio
n
.
T
h
e
al
g
o
r
ith
m
co
m
p
u
te
s
th
e
GFR
lev
el
b
ased
o
n
th
e
f
r
in
FB
.
G
l
ome
r
ul
a
r
fil
tr
a
tion
r
a
te
=
120
−
(
∗
100
)
(
2
8
)
T
h
e
p
s
eu
d
o
co
d
e
is
:
Define the Population
Calculate approximate solution in the population
Most excellent solution
, is found
By
(27) find the Filtration rate
-
,
Define waste (W)
Define filtered blood (FB)
Number of iteration will be found
Do while (iteration
<
)
For
; compute new
by using (26)
Check the value of
using
If
allocated to W then place on re
-
absorption and produce
by using (26)
If re
-
absorption is not fulfilled then
will not be part of F
B
Eradicate
from W (excretion)
Place randomly Z into W to exchange
End if
is reabsorbed
Else
If it is superior than the
is secreted
Calculate the GFR level solutions in FB by using (28)
15
<
<
60
;
ℎ
End if
<
15
;
ℎ
ℎ
ℎ
ℎ
End if
End
if
End for
Rank the
from FB and modernize the
Merge W and FB
By (27) amend filtration rate
End while
Return
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
: 2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
10
,
No
.
3
,
Sep
tem
b
er
2
0
2
1
:
20
5
–
21
1
210
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
MZ
A,
AB
an
d
I
KS
alg
o
r
ith
m
h
as b
ee
n
test
ed
.
T
h
e
c
o
m
p
a
r
is
o
n
o
f
r
esu
lts
is
p
r
esen
ted
in
T
a
b
le
1
.
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
[
1
9
]
I
A
B
C
O
[
1
9
]
M
ZA
AB
I
K
S
V1
1
.
0
6
1
.
0
5
1
.
0
0
1
.
0
5
1
.
0
1
V2
1
.
0
3
1
.
0
5
1
.
0
1
1
.
0
4
1
.
0
0
V3
0
.
9
8
1
.
0
3
1
.
0
0
1
.
0
1
1
.
0
5
V6
1
.
0
5
1
.
0
5
1
.
0
4
1
.
0
2
1
.
0
6
V8
1
.
0
0
1
.
0
4
0
.
9
2
0
.
9
0
0
.
9
1
Q9
0
.
1
3
9
0
.
1
3
2
0
.
1
1
0
0
.
1
0
9
0
.
1
0
6
T5
6
0
.
9
7
9
0
.
9
6
0
0
.
9
2
2
0
.
9
2
0
0
.
9
1
8
T4
7
0
.
9
5
0
0
.
9
5
0
0
.
9
1
0
0
.
9
0
8
0
.
9
1
3
T4
9
1
.
0
1
4
1
.
0
0
7
1
.
0
0
2
1
.
0
0
0
1
.
0
0
1
P
l
o
ss
(
M
W
)
5
.
9
2
8
9
2
5
.
5
0
0
3
1
4
.
9
9
2
0
4
4
.
5
0
0
0
9
4
.
4
9
9
7
1
Vali
d
it
y
o
f
p
r
o
p
o
s
e
d
MZ
A,
AB
an
d
I
KS
alg
o
r
ith
m
h
as
b
ee
n
v
e
r
i
f
i
ed
in
s
tan
d
ar
d
I
E
E
E
1
1
8
-
b
u
s
test
s
y
s
tem
.
L
im
itatio
n
s
on
r
ea
cti
v
e
p
o
wer
s
o
u
r
ce
ar
e
lis
ted
in
T
ab
le
2
,
an
d
T
ab
le
3
s
h
o
ws
g
o
o
d
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
.
T
ab
le
2
.
R
ea
ctiv
e
p
o
wer
s
o
u
r
c
es lim
its
B
U
S
N
O
5
34
37
44
45
46
48
QC
M
A
X
0
.
0
0
1
4
.
0
0
0
.
0
0
1
0
.
0
0
1
0
.
0
0
1
0
.
0
0
1
5
.
0
0
QC
M
I
N
-
4
0
.
0
0
0
.
0
0
-
2
5
.
0
0
0
.
0
0
0
.
0
0
0
.
0
0
0
.
0
0
B
U
S
N
O
74
79
82
83
1
0
5
1
0
7
1
1
0
QC
M
A
X
1
2
.
0
0
2
0
.
0
0
2
0
.
0
0
1
0
.
0
0
2
0
.
0
0
6
.
0
0
6
.
0
0
QC
M
I
N
0
.
0
0
0
.
0
0
0
.
0
0
0
.
0
0
0
.
0
0
0
.
0
0
0
.
0
0
T
h
en
I
E
E
E
3
0
0
-
b
u
s
s
y
s
tem
[
2
1
]
is
u
s
ed
as
test
s
y
s
tem
to
v
alid
ate
th
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
MZ
A,
AB
an
d
I
KS
alg
o
r
ith
m
.
T
ab
le
4
s
h
o
ws
th
e
co
m
p
a
r
is
o
n
o
f
r
ea
l
p
o
wer
lo
s
s
o
b
tain
ed
af
ter
o
p
tim
izatio
n
.
R
ea
l
p
o
wer
lo
s
s
h
as
b
ee
n
co
n
s
id
er
ab
ly
r
e
d
u
ce
d
wh
en
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
ith
m
s
.
C
o
m
p
ar
ed
to
E
E
A
m
eth
o
d
,
p
r
o
p
o
s
ed
I
KS a
lg
o
r
ith
m
r
e
d
u
ce
s
4
.
77
% r
ea
l
p
o
wer
lo
s
s
.
T
ab
le
3
.
R
ea
l p
o
wer
l
o
s
s
co
m
p
ar
is
o
n
r
esu
lts
A
c
t
i
v
e
p
o
w
e
r
l
o
ss (p
.
u
)
M
e
t
h
o
d
-
B
B
O
[2
0
]
M
e
t
h
o
d
-
I
LSBB
O
/
S
t
r
a
t
e
g
y
I
[2
0
]
M
e
t
h
o
d
-
I
LSBB
O
/
S
t
r
a
t
e
g
y
I
I
[2
0
]
M
ZA
AB
I
K
S
M
i
n
i
m
u
m
v
a
l
u
e
1
2
8
.
7
7
0
0
1
2
6
.
9
8
0
0
1
2
4
.
7
8
0
0
1
1
8
.
7
9
2
0
1
1
6
.
9
9
0
0
1
1
6
.
0
4
0
5
M
a
x
i
m
u
m
v
a
l
u
e
1
3
2
.
6
4
0
1
3
7
.
3
4
0
0
1
3
2
.
3
9
0
0
1
2
3
.
8
4
3
0
1
2
3
.
4
1
2
7
1
2
2
.
7
1
9
6
A
v
e
r
a
g
e
v
a
l
u
e
1
3
0
.
2
1
0
0
1
3
0
.
3
7
0
0
1
2
9
.
2
2
0
0
1
2
0
.
1
2
4
9
1
1
9
.
7
9
0
1
1
1
9
.
0
0
1
2
T
ab
le
4
.
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
EEA
M
e
t
h
o
d
[
2
2
]
EG
A
M
e
t
h
o
d
[
2
2
]
C
S
A
M
e
t
h
o
d
[
2
3
]
MZ
A
AB
I
K
S
P
LO
S
S
(
M
W
)
6
5
0
.
6
0
2
7
6
4
6
.
2
9
9
8
6
3
5
.
8
9
4
2
6
2
0
.
1
9
8
2
6
2
0
.
1
0
4
1
6
1
9
.
5
4
2
7
7.
CO
NCLU
SI
O
N
I
n
th
is
p
ap
er
,
o
p
tim
al
r
ea
ctiv
e
p
o
wer
p
r
o
b
lem
h
as b
ee
n
s
u
cc
ess
f
u
lly
s
o
lv
ed
b
y
MZA
,
au
g
m
en
ted
AB
an
d
I
KS
alg
o
r
ith
m
.
Pr
o
p
o
s
ed
MZ
A
u
tili
ze
s
it
s
s
p
ec
ial
lo
g
ic
al,
co
o
p
er
ativ
e
a
n
d
s
elf
-
d
eter
m
in
in
g
ap
p
r
o
ac
h
i
n
th
e
s
ea
r
ch
o
f
a
b
est p
ath
t
o
ar
r
iv
e
at
g
r
ass
lan
d
.
T
h
is
co
n
f
e
r
s
i
t
to
r
ea
c
h
o
p
tim
u
m
r
esu
lts
f
aster
th
an
s
o
m
e
o
th
er
s
ea
r
ch
in
g
ag
e
n
ts
.
I
n
t
h
e
p
r
o
p
o
s
ed
AB
alg
o
r
ith
m
,
b
alan
ce
b
etwe
en
lo
ca
l
a
n
d
g
lo
b
a
l
s
ea
r
ch
h
as
b
ee
n
m
ain
tain
ed
.
Mo
v
em
en
ts
o
f
th
e
b
ats
b
y
to
g
g
le
b
etwe
en
lo
c
al
s
ea
r
ch
an
d
g
lo
b
al
s
ea
r
c
h
i
s
co
n
tr
o
lled
b
y
th
e
p
u
ls
e
r
ate
r
an
d
r
ec
eip
t
o
r
r
e
f
u
s
al
o
f
a
n
ew
-
f
an
g
led
e
n
g
en
d
er
ed
s
o
lu
tio
n
is
co
n
tr
o
lled
b
y
lo
u
d
n
ess
A.
I
KS
alg
o
r
ith
m
h
as
b
ee
n
b
u
ilt
to
a
d
v
an
ce
th
e
s
ea
r
ch
,
ev
en
a
p
o
te
n
tial
s
o
lu
tio
n
m
o
v
ed
to
W
an
d
it
will
b
e
b
r
o
u
g
h
t
b
ac
k
to
th
e
FB
.
GFR
test
i
s
u
ti
lized
to
v
er
if
y
t
h
e
f
itn
ess
o
f
k
i
d
n
ey
s
.
T
h
e
test
ap
p
r
o
x
im
ately
g
iv
es
th
e
ca
p
ac
ity
o
f
b
lo
o
d
th
at
p
ass
th
r
o
u
g
h
t
h
e
g
lo
m
e
r
u
li
ev
er
y
m
in
u
te.
Pro
p
o
s
ed
MZ
A,
AB
an
d
I
K
S
alg
o
r
ith
m
s
h
a
ve
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2
2
5
2
-
8
8
1
4
Tr
u
e
p
o
w
er lo
s
s
r
ed
u
ctio
n
b
y
mo
u
n
ta
in
z
eb
r
a
,
a
u
g
me
n
ted
b
a
t a
n
d
i
mp
r
o
ve
d
kid
n
ey
…
(
Le
n
in
K
a
n
a
g
a
s
a
b
a
i)
211
p
er
f
o
r
m
ed
well
w
h
en
e
v
alu
ate
d
in
s
tan
d
ar
d
I
E
E
E
1
4
-
b
u
s
,
1
1
8
-
b
u
s
,
a
n
d
3
0
0
-
b
u
s
test
s
y
s
tem
s
.
T
r
u
e
p
o
wer
lo
s
s
r
ed
u
ce
d
c
o
n
s
id
er
ab
l
y
wh
en
c
o
m
p
ar
ed
to
o
th
er
s
tan
d
ar
d
al
g
o
r
ith
m
s
.
RE
F
E
R
E
NC
E
S
[1
]
K.
Y.
Lee
,
Y.
M
.
P
a
rk
,
a
n
d
J.
L.
Ortiz,
“
F
u
e
l
-
c
o
st
m
in
imis
a
ti
o
n
f
o
r
b
o
t
h
re
a
l
a
n
d
re
a
c
ti
v
e
-
p
o
we
r
d
isp
a
tch
e
s,
”
Pro
c
e
e
d
in
g
s Ge
n
e
ra
ti
o
n
,
T
r
a
n
sm
i
ss
io
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
a
n
d
S
.
M
.
S
h
a
h
id
e
h
p
o
u
r
,
“
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
tch
u
si
n
g
a
re
v
ise
d
li
n
e
a
r
p
ro
g
ra
m
m
in
g
a
p
p
ro
a
c
h
,
”
E
lec
tric P
o
we
r S
y
ste
m R
e
se
a
rc
h
,
v
o
l.
1
5
,
n
o
.
2
,
p
p
.
1
2
1
–
1
3
4
,
1
9
8
8
.
[3
]
M
.
R.
Bjel
o
g
rli
c
,
M
.
S
.
Ca
lo
v
ic,
P
.
Ristan
o
v
ic
a
n
d
B.
S
.
Ba
b
ic,
“
Ap
p
li
c
a
ti
o
n
o
f
Ne
wto
n
’s
o
p
t
ima
l
p
o
we
r
fl
o
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
i
sp
a
tch
th
r
o
u
g
h
i
n
terio
r
p
o
in
t
m
e
th
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
in
,
“
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
ro
g
ra
m
m
in
g
m
e
th
o
d
,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
P
o
we
r S
y
ste
m
,
v
o
l.
1
3
,
n
o
.
4
,
p
p
.
1
2
1
9
–
1
2
2
5
,
1
9
9
8
.
[6
]
W.
Ya
n
,
J.
Y
u
,
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
i
ts
so
lu
ti
o
n
b
y
p
re
d
icto
r
c
o
r
re
c
to
r
p
r
ima
l
d
u
a
l
in
teri
o
r
p
o
in
t
m
e
th
o
d
,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
p
o
we
r
sy
ste
ms
,
v
o
l.
2
1
,
n
o
.
1
,
p
p
.
6
1
-
6
7
,
2
0
0
6
.
[7
]
A.
M
u
k
h
e
rjee
a
n
d
V.
M
u
k
h
e
rjee
,
“
S
o
lu
ti
o
n
o
f
o
p
t
ima
l
re
a
c
ti
v
e
p
o
w
e
r
d
isp
a
tch
b
y
c
h
a
o
ti
c
k
ri
ll
h
e
r
d
a
lg
o
rit
h
m
,
”
IE
T
Ge
n
e
ra
ti
o
n
,
T
ra
n
sm
issio
n
a
n
d
Di
strib
u
ti
o
n
,
v
o
l.
9
,
n
o
.
1
5
,
p
p
.
2
3
5
1
–
2
3
6
2
,
2
0
1
5
.
[8
]
M
.
A/P
M
o
r
g
a
n
,
N.
R
.
H.
Ab
d
u
ll
a
h
,
M
.
H.
S
u
laim
a
n
,
M
.
M
u
st
a
fa
a
n
d
R.
S
a
m
a
d
,
“
Co
m
p
u
tati
o
n
a
l
in
tell
ig
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
si
d
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
A
p
p
l
ied
S
c
ien
c
e
s
,
v
o
l.
1
0
,
n
o
.
2
2
,
p
p
.
1
7
0
5
9
-
1
7
0
6
4
,
2
0
1
5
.
[9
]
M
.
H.
S
u
laim
a
n
,
Z.
M
u
sta
ffa
,
H.
Da
n
iy
a
l,
M
.
R.
M
o
h
a
m
e
d
a
n
d
O
.
Alima
n
,
“
S
o
l
v
in
g
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
p
lan
n
in
g
p
r
o
b
lem
u
ti
li
z
i
n
g
n
a
t
u
r
e
in
sp
ired
c
o
m
p
u
ti
n
g
tec
h
n
i
q
u
e
s
,
”
AR
PN
J
o
u
rn
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
,
v
o
l
.
1
0
,
n
o
.
2
1
,
p
p
.
9
7
7
9
-
9
7
8
5
,
2
0
1
5
.
[1
0
]
M
.
H.
S
u
laim
a
n
,
W
.
L.
I
n
g
,
Z.
M
u
sta
ffa
a
n
d
M
.
R.
M
o
h
a
m
e
d
,
“
G
r
e
y
wo
lf
o
p
ti
m
ize
r
fo
r
so
l
v
in
g
e
c
o
n
o
m
ic
d
is
p
a
tch
p
ro
b
lem
with
v
a
lv
e
-
l
o
a
d
i
n
g
e
ffe
c
ts,”
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
,
n
o
.
2
1
,
p
p
.
9
7
9
6
-
9
8
0
1
,
2
0
1
5
.
[1
1
]
K.
P
a
n
d
iara
jan
,
a
n
d
C.
K.
Ba
b
u
la
l,
“
F
u
z
z
y
h
a
rm
o
n
y
se
a
rc
h
a
lg
o
rit
h
m
b
a
se
d
o
p
ti
m
a
l
p
o
we
r
fl
o
w
f
o
r
p
o
we
r
s
y
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
o
f
E
lec
trica
l
Po
we
r
a
n
d
E
n
e
rg
y
S
y
ste
ms
,
v
o
l.
7
8
,
p
p
.
7
2
-
7
9
.
2
0
1
6
.
[1
2
]
M
.
H.
S
u
laim
a
n
,
Z.
M
u
sta
ffa
,
M
.
R.
M
o
h
a
m
e
d
a
n
d
O.
Alima
n
,
“
An
a
p
p
li
c
a
ti
o
n
o
f
m
u
lt
i
-
v
e
rse
o
p
t
imiz
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
im
u
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
,
n
o
.
4
1
,
p
p
.
5
.
1
-
5
.
5
.
2
0
1
7
.
[1
3
]
M
.
A/P
M
o
r
g
a
n
,
N.
R.
H
.
Ab
d
u
ll
a
h
,
M
.
H.
S
u
laim
a
n
,
M
.
M
u
sta
fa
a
n
d
R.
S
a
m
a
d
,
“
M
u
lt
i
-
o
b
jec
ti
v
e
e
v
o
l
u
ti
o
n
a
ry
p
ro
g
ra
m
m
in
g
(M
OE
P
)
u
sin
g
m
u
tatio
n
b
a
se
d
o
n
a
d
a
p
ti
v
e
m
u
tati
o
n
o
p
e
ra
to
r
(AMO)
a
p
p
li
e
d
f
o
r
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
is
p
a
tch
,
”
AR
PN
J
o
u
rn
a
l
o
f
En
g
in
e
e
rin
g
a
n
d
Ap
p
li
e
d
S
c
ien
c
e
s
,
v
o
l.
1
1
,
n
o
.
1
4
,
p
p
.
8
8
8
4
-
8
8
8
8
,
2
0
1
6
.
[1
4
]
R.
Ng
S
h
in
M
e
i,
M
.
H.
S
u
laim
a
n
,
Z.
M
u
sta
ffa
,
“
An
t
li
o
n
o
p
ti
m
ize
r
fo
r
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
is
p
a
tch
so
l
u
ti
o
n
,
”
J
o
u
rn
a
l
o
f
El
e
c
tr
ica
l
S
y
ste
ms
,
"
S
p
e
c
ia
l
Iss
u
e
A
M
PE
2
0
1
5
"
,
p
p
.
6
8
-
7
4
,
2
0
1
6
.
[1
5
]
M
.
M
o
rg
a
n
,
N.
R.
H.
A
b
d
u
ll
a
h
,
M
.
H.
S
u
laim
a
n
,
M
.
M
u
sta
fa
,
R.
S
a
m
a
d
,
“
Be
n
c
h
m
a
rk
st
u
d
ies
o
n
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
(ORPD)
b
a
se
d
m
u
lt
i
-
o
b
jec
ti
v
e
e
v
o
lu
ti
o
n
a
ry
p
ro
g
ra
m
m
in
g
(M
OE
P
)
u
sin
g
m
u
tat
io
n
b
a
se
d
o
n
a
d
a
p
ti
v
e
m
u
tatio
n
a
d
a
p
ter
(AMO
)
a
n
d
p
o
l
y
n
o
m
ial
m
u
tatio
n
o
p
e
ra
to
r
(
P
M
O)
,
”
J
o
u
r
n
a
l
o
f
E
lec
trica
l
S
y
ste
ms
,
v
o
l.
1
2
,
n
o
.
1
,
p
p
.
1
2
1
-
1
3
2
,
2
0
1
6
.
[1
6
]
R.
Ng
S
h
in
M
e
i,
M
.
H.
S
u
laim
a
n
,
Z.
M
u
sta
ffa
,
H.
Da
n
i
y
a
l,
“
Op
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
d
isp
a
tch
so
lu
ti
o
n
b
y
l
o
ss
mi
n
imiz
a
ti
o
n
u
sin
g
m
o
t
h
-
flam
e
o
p
ti
m
iza
ti
o
n
tec
h
n
i
q
u
e
,
”
A
p
p
li
e
d
S
o
ft
C
o
mp
u
ti
n
g
,
v
o
l
.
5
9
,
p
p
.
2
1
0
-
2
2
2
,
2
0
1
7
.
[1
7
]
X.S
.
Ya
n
g
,
“
Ba
t
a
lg
o
rit
h
m
f
o
r
m
u
lt
i
-
o
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
,
”
In
te
rn
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Bi
o
-
In
s
p
ire
d
Co
mp
u
ta
ti
o
n
,
v
o
l.
3
,
n
o
.
5
,
2
6
7
–
2
7
4
,
2
0
1
1
.
[1
8
]
N.
S.
Ja
d
d
i
,
J.
Alv
a
n
k
a
rian
,
a
n
d
S
.
A
b
d
u
ll
a
h
,
“
Ki
d
n
e
y
-
i
n
s
p
ired
a
lg
o
rit
h
m
fo
r
o
p
ti
m
iza
ti
o
n
p
ro
b
lem
s
,
”
Co
mm
u
n
ica
ti
o
n
s i
n
No
n
li
n
e
a
r S
c
i
e
n
c
e
a
n
d
Nu
me
ric
a
l
S
imu
l
a
ti
o
n
,
v
o
l.
42
,
p
p
.
3
5
8
-
3
6
9
,
2
0
1
7
.
[1
9
]
C.
M
.
K.
S
i
v
a
li
n
g
a
m
,
S
.
Ra
m
a
c
h
a
n
d
ra
n
a
n
d
P
.
S
.
S
.
Ra
jam
a
n
i,
“
Re
a
c
ti
v
e
p
o
we
r
o
p
ti
m
iza
ti
o
n
i
n
a
p
o
we
r
sy
ste
m
n
e
two
rk
t
h
ro
u
g
h
m
e
tah
e
u
risti
c
a
lg
o
rit
h
m
s,”
T
u
rk
ish
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
t
e
r
S
c
ien
c
e
,
v
o
l.
2
5
,
n
o
.
6
,
p
p
.
4
6
1
5
–
4
6
2
3
,
2
0
1
7
.
Do
i:
1
0
.
3
9
0
6
/el
k
-
1
7
0
3
-
1
5
9
.
[2
0
]
Jia
n
g
tao
Ca
o
,
F
u
li
Wan
g
a
n
d
P
in
g
Li
,
“
An
imp
ro
v
e
d
b
io
g
e
o
g
r
a
p
h
y
-
b
a
se
d
o
p
ti
m
iza
ti
o
n
a
l
g
o
rit
h
m
fo
r
o
p
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
flo
w
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Co
n
tro
l
a
n
d
A
u
t
o
ma
ti
o
n
,
v
o
l
.
7
,
no
.
3
,
p
p
.
1
6
1
-
1
7
6
,
2
0
1
4
.
\
[2
1
]
IEE
E,
“
T
h
e
IEE
E
-
tes
t
s
y
ste
m
s
,
”
1
9
9
3
,
[On
l
in
e
].
A
v
a
il
a
b
l
e:
h
tt
p
s://
e
lec
tri
c
g
rid
s.e
n
g
r.
tam
u
.
e
d
u
/ele
c
tri
c
-
g
rid
-
tes
t
-
c
a
se
s/ie
e
e
-
300
-
b
u
s
-
sy
ste
m
/
.
[2
2
]
S
.
S
.
Re
d
d
y
,
“
Op
ti
m
a
l
re
a
c
ti
v
e
p
o
we
r
sc
h
e
d
u
li
n
g
u
sin
g
c
u
c
k
o
o
se
a
rc
h
a
lg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
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