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20
26
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271
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w
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m
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P
SO)
[
1
]
,
g
en
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m
(
GA
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[
2
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,
an
d
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AC
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[
3
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h
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s
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m
p
lo
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I
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6930
T
E
L
KOM
NI
K
A
T
elec
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m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
2
7
1
-
2
8
1
272
o
p
tim
a
[
4
]
.
T
h
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f
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n
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a
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e
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tal
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f
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5
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ti
m
izer
(
C
C
O)
[
9
]
r
ef
lect
t
h
e
g
r
o
w
in
g
in
ter
est
i
n
m
ar
i
n
e
-
b
ased
b
eh
a
v
io
r
s
f
o
r
m
eta
h
eu
r
i
s
tic
d
ev
el
o
p
m
e
n
t.
Me
tah
e
u
r
is
t
ics
co
n
ti
n
u
e
to
ev
o
lv
e
w
it
h
n
e
w
er
p
ar
ad
ig
m
s
t
h
at
ad
d
r
ess
s
ca
lab
ilit
y
,
r
o
b
u
s
t
n
es
s
,
an
d
co
n
v
er
g
e
n
ce
p
r
o
p
er
ties
[
1
0
]
.
R
ec
en
t
ad
v
an
ce
s
h
ig
h
li
g
h
t
th
e
ad
ap
tab
ilit
y
o
f
s
w
ar
m
-
b
a
s
ed
m
e
th
o
d
s
ac
r
o
s
s
o
p
ti
m
izatio
n
d
o
m
ai
n
s
,
w
i
th
n
o
v
el
v
ar
ian
t
s
d
esig
n
ed
to
en
h
a
n
ce
co
n
v
er
g
e
n
ce
s
p
ee
d
an
d
m
ai
n
tai
n
d
iv
er
s
it
y
[
1
1
]
.
I
n
m
ar
in
e
ec
o
s
y
s
te
m
s
,
s
m
all
p
elag
ic
f
i
s
h
s
u
c
h
as
an
ch
o
v
ies
an
d
s
ar
d
in
es
f
ee
d
b
y
s
c
h
o
o
li
n
g
i
n
lar
g
e
g
r
o
u
p
s
w
h
ile
f
il
ter
in
g
p
lan
k
to
n
f
r
o
m
s
u
r
r
o
u
n
d
in
g
w
ater
a
s
d
em
o
n
s
tr
ated
i
n
F
ig
u
r
e.
1
.
Un
li
k
e
p
r
ed
ato
r
s
th
at
ch
ase
i
n
d
iv
id
u
al
p
r
ey
,
f
ilter
f
ee
d
er
s
co
n
tin
u
o
u
s
l
y
s
a
m
p
le
m
u
ltip
le
p
ar
ticles
an
d
s
elec
ti
v
el
y
r
etai
n
o
n
l
y
t
h
e
b
en
ef
icia
l
f
r
ac
tio
n
.
Mo
tiv
ate
d
b
y
th
is
u
n
iq
u
e
s
tr
ateg
y
,
w
e
p
r
o
p
o
s
e
a
n
e
w
alg
o
r
it
h
m
ca
lled
th
e
an
ch
o
v
y
-
in
s
p
ir
ed
f
ilter
alg
o
r
it
h
m
(
AF
A
)
,
w
h
ic
h
i
n
tr
o
d
u
ce
s
f
ilter
in
g
as
a
n
o
v
el
ex
p
lo
itatio
n
m
e
ch
an
i
s
m
to
i
m
p
r
o
v
e
co
n
v
er
g
e
n
ce
p
er
f
o
r
m
a
n
ce
w
h
i
le
p
r
eser
v
in
g
s
u
f
f
icie
n
t e
x
p
lo
r
atio
n
.
Fig
u
r
e
1
.
A
s
c
h
o
o
l o
f
an
c
h
o
v
i
es s
w
i
m
s
co
llecti
v
el
y
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
S
w
ar
m
i
n
telli
g
e
n
ce
(
SI)
alg
o
r
ith
m
s
f
o
r
m
an
i
m
p
o
r
tan
t
cla
s
s
o
f
m
eta
h
e
u
r
is
tic
s
in
s
p
ir
ed
b
y
co
llectiv
e
b
eh
av
io
r
s
o
b
s
er
v
ed
in
n
at
u
r
e
.
Am
o
n
g
th
e
m
,
P
SO,
G
A
,
ar
tif
icia
l
f
i
s
h
s
w
ar
m
a
lg
o
r
it
h
m
(
A
F
S
A
)
,
an
d
f
i
s
h
s
ch
o
o
l
s
ea
r
c
h
(
FS
S)
h
a
v
e
r
ec
e
iv
ed
s
i
g
n
i
f
ican
t
atte
n
tio
n
d
u
e
t
o
th
eir
ab
ilit
y
to
d
ea
l
w
it
h
h
i
g
h
-
d
i
m
e
n
s
io
n
al
a
n
d
n
o
n
li
n
ea
r
p
r
o
b
lem
s
.
T
h
is
s
ec
tio
n
b
r
ief
l
y
r
ev
ie
w
s
th
e
s
e
ap
p
r
o
ac
h
es,
em
p
h
asizi
n
g
th
eir
u
n
d
er
l
y
i
n
g
m
ec
h
a
n
i
s
m
s
,
s
tr
e
n
g
t
h
s
,
a
n
d
lim
itatio
n
s
,
w
h
ile
id
en
ti
f
y
i
n
g
t
h
e
r
esear
ch
g
ap
th
at
m
o
t
iv
ate
s
th
e
d
ev
elo
p
m
e
n
t
o
f
th
e
A
F
A
.
2
.
1
.
P
a
rt
icle
s
wa
r
m
o
pti
m
iz
a
t
io
n
I
n
tr
o
d
u
ce
d
b
y
Ke
n
n
ed
y
a
n
d
E
b
er
h
ar
t
[
1
]
,
P
SO
m
o
d
els
th
e
s
o
cial
d
y
n
a
m
ics
o
f
b
ir
d
f
lo
ck
i
n
g
an
d
f
i
s
h
s
ch
o
o
lin
g
.
E
ac
h
p
a
r
ticle
r
ep
r
esen
t
s
a
s
o
l
u
tio
n
t
h
at
ad
j
u
s
t
s
its
tr
aj
ec
to
r
y
b
ased
o
n
it
s
p
e
r
s
o
n
al
b
est
a
n
d
th
e
g
lo
b
al
b
est.
T
h
e
alg
o
r
ith
m
is
co
m
p
u
tatio
n
all
y
e
f
f
icie
n
t
an
d
ea
s
y
to
i
m
p
le
m
en
t,
b
u
t
it
i
s
p
r
o
n
e
to
p
r
em
at
u
r
e
co
n
v
er
g
e
n
ce
i
n
m
u
lt
i
m
o
d
al
s
e
ar
ch
s
p
ac
es
[
1
2
]
.
2
.
2
.
G
enet
ic
a
lg
o
rit
hm
GA
,
p
r
o
p
o
s
ed
b
y
Ho
lla
n
d
[
2
]
,
ap
p
lies
p
r
in
cip
les
o
f
n
at
u
r
al
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
to
ev
o
lv
e
a
p
o
p
u
latio
n
o
f
ca
n
d
id
ate
s
o
lu
tio
n
s
.
I
t
i
s
v
er
s
atile
an
d
h
a
s
b
ee
n
ap
p
lied
i
n
n
u
m
er
o
u
s
o
p
ti
m
izat
io
n
co
n
tex
t
s
.
Ho
w
e
v
er
,
GA
o
f
te
n
d
em
a
n
d
s
ca
r
ef
u
l
p
ar
a
m
eter
tu
n
in
g
an
d
ca
n
b
ec
o
m
e
co
m
p
u
t
atio
n
all
y
e
x
p
en
s
iv
e,
p
ar
ticu
lar
l
y
f
o
r
lar
g
e
-
s
ca
le
p
r
o
b
le
m
s
[
1
3
]
.
2
.
3
.
A
rt
if
ici
a
l f
is
h sw
a
r
m
a
lg
o
rit
h
m
T
h
e
A
FS
A
,
o
r
ig
i
n
all
y
p
r
o
p
o
s
ed
b
y
Gao
an
d
W
en
[
1
4
]
,
i
m
itate
s
f
i
s
h
b
eh
a
v
io
r
s
s
u
ch
a
s
p
r
ey
i
n
g
,
s
w
ar
m
i
n
g
,
an
d
f
o
llo
w
i
n
g
,
w
h
i
ch
e
n
h
a
n
ce
s
ex
p
lo
r
atio
n
in
th
e
s
o
lu
tio
n
s
p
ac
e.
Nev
er
t
h
eles
s
,
AFS
A
m
a
y
s
u
f
f
er
f
r
o
m
s
lo
w
co
n
v
er
g
e
n
ce
an
d
t
h
e
r
is
k
o
f
s
ta
g
n
at
io
n
i
f
t
h
e
b
a
lan
ce
b
et
w
ee
n
ex
p
lo
r
atio
n
a
n
d
e
x
p
lo
itatio
n
i
s
n
o
t
ef
f
ec
tiv
e
l
y
m
a
n
ag
ed
[
1
5
]
,
[
1
6
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
A
n
ch
o
vy
-
i
n
s
p
ir
ed
filt
er a
lg
o
r
ith
m:
A
b
io
-
in
s
p
ir
ed
o
p
timiz
a
tio
n
a
p
p
r
o
a
ch
fo
r
…
(
A
z
r
u
l Ma
h
fu
r
d
z
)
273
2
.
4
.
F
is
h scho
o
l sea
rc
h
FS
S,
p
r
o
p
o
s
ed
b
y
Fi
lh
o
et
a
l.
[
1
7
]
,
d
r
aw
s
f
r
o
m
th
e
co
llectiv
e
b
e
h
av
io
r
o
f
f
i
s
h
s
c
h
o
o
ls
.
B
y
in
co
r
p
o
r
atin
g
f
ee
d
i
n
g
an
d
co
o
r
d
in
ated
s
w
i
m
m
i
n
g
,
FS
S
in
tr
o
d
u
ce
s
m
ec
h
an
i
s
m
s
f
o
r
b
alan
ci
n
g
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
.
Desp
ite
its
p
o
ten
tial,
FS
S
ca
n
b
e
h
ig
h
l
y
s
en
s
iti
v
e
to
p
ar
am
eter
s
elec
tio
n
a
n
d
m
a
y
u
n
d
er
p
er
f
o
r
m
in
co
m
p
le
x
m
u
lti
m
o
d
al
lan
d
s
c
ap
es
[
1
8
]
,
[
1
9
]
.
2
.
5
.
Resea
rc
h
g
a
p:
f
ilte
r
-
f
ee
din
g
m
ec
ha
ni
s
m
A
lt
h
o
u
g
h
AFS
A
an
d
FS
S
ar
e
b
o
th
in
s
p
ir
ed
b
y
f
i
s
h
,
th
e
y
p
r
i
m
ar
il
y
m
o
d
el
m
o
v
e
m
e
n
t
an
d
f
o
r
ag
i
n
g
s
tr
ateg
ie
s
r
ath
er
th
an
s
elec
t
iv
e
f
ee
d
in
g
.
I
n
co
n
tr
ast,
an
c
h
o
v
ies
an
d
s
ar
d
in
es
e
m
p
lo
y
a
f
ilt
er
-
f
ee
d
i
n
g
p
r
o
ce
s
s
,
w
h
er
eb
y
ea
c
h
f
i
s
h
co
n
ti
n
u
o
u
s
l
y
s
a
m
p
les
p
la
n
k
to
n
a
n
d
s
elec
ti
v
el
y
r
etai
n
s
h
i
g
h
v
al
u
e
p
ar
ticles
w
h
il
e
d
is
ca
r
d
in
g
t
h
e
r
est
[
2
0
]
,
[
2
1
]
.
T
h
is
m
ec
h
a
n
is
m
r
ep
r
esen
t
s
an
ef
f
icie
n
t
n
a
tu
r
al
f
ilter
in
g
p
r
o
ce
s
s
th
at
m
ax
i
m
izes
en
er
g
y
g
a
in
w
h
ile
m
i
n
i
m
izi
n
g
co
s
t.
T
o
th
e
b
est
o
f
o
u
r
k
n
o
w
led
g
e,
n
o
e
x
is
ti
n
g
m
eta
h
eu
r
i
s
tic
e
x
p
licitl
y
in
co
r
p
o
r
ates
f
ilter
f
ee
d
i
n
g
as
an
o
p
ti
m
izatio
n
o
p
er
ato
r
.
Fi
s
h
-
i
n
s
p
ir
ed
alg
o
r
ith
m
s
s
u
ch
as
AFS
A
a
n
d
FS
S
ca
p
tu
r
e
asp
ec
ts
o
f
g
r
o
u
p
d
y
n
a
m
ics,
b
u
t
th
e
y
lack
t
h
e
s
e
lecti
v
e
f
i
lter
in
g
m
ec
h
a
n
is
m
.
T
h
is
g
ap
h
i
g
h
l
ig
h
t
s
th
e
p
o
ten
tial
o
f
a
n
e
w
p
ar
ad
ig
m
w
h
er
e
f
ilter
in
g
ac
t
s
as
a
n
o
v
el
ex
p
lo
itat
io
n
s
tr
ateg
y
s
u
c
h
s
a
m
p
li
n
g
w
id
el
y
(
ex
p
lo
r
atio
n
)
b
u
t
o
n
l
y
r
etain
i
n
g
p
r
o
m
is
i
n
g
ca
n
d
id
ates
(
ex
p
lo
itatio
n
)
.
T
h
e
p
r
o
p
o
s
ed
A
FA
ad
d
r
ess
es
th
is
g
ap
,
ex
ten
d
i
n
g
s
w
ar
m
in
te
lli
g
en
ce
w
it
h
a
b
io
lo
g
icall
y
g
r
o
u
n
d
ed
f
ilter
in
g
p
r
in
cip
le.
2
.
6
.
A
ncho
v
y
-
ins
pired f
ilte
r
a
lg
o
rit
h
m
Mo
tiv
ated
b
y
th
e
n
a
tu
r
al
f
ee
d
in
g
b
eh
a
v
io
r
o
f
an
ch
o
v
ies,
w
e
p
r
o
p
o
s
e
an
i
m
p
r
o
v
ed
b
io
-
in
s
p
ir
ed
o
p
tim
izatio
n
m
eth
o
d
.
An
c
h
o
v
ies
f
ee
d
b
y
f
ilter
in
g
p
lan
k
to
n
s
u
s
p
en
d
ed
in
th
e
w
ater
.
A
s
ch
o
o
l
o
f
an
ch
o
v
ie
s
s
w
i
m
s
co
llectiv
el
y
w
h
ile
ea
ch
f
is
h
co
n
ti
n
u
o
u
s
l
y
f
il
ter
s
w
a
ter
,
r
etain
in
g
o
n
l
y
ed
ib
le
p
ar
ticles
an
d
d
is
ca
r
d
in
g
th
e
r
est.
T
h
is
ef
f
icie
n
t
f
ilter
i
n
g
m
ec
h
an
i
s
m
e
n
s
u
r
es
s
u
r
v
i
v
al
w
h
ile
b
alan
cin
g
f
o
c
u
s
ed
ex
p
l
o
itatio
n
(
ca
p
tu
r
in
g
p
lan
k
to
n
)
an
d
d
iv
er
s
e
e
x
p
lo
r
atio
n
(
s
ch
o
o
l
m
o
v
e
m
e
n
t to
n
e
w
f
ee
d
in
g
g
r
o
u
n
d
s
)
.
I
n
o
p
ti
m
izatio
n
,
th
i
s
b
io
lo
g
ica
l p
r
o
ce
s
s
is
m
o
d
eled
:
a.
C
an
d
id
ate
g
e
n
er
atio
n
(
f
i
lter
in
g
in
ta
k
e)
:
e
ac
h
an
c
h
o
v
y
s
a
m
p
les
m
u
l
tip
le
ca
n
d
id
ate
s
o
lu
ti
o
n
s
ar
o
u
n
d
it
s
p
o
s
itio
n
,
an
alo
g
o
u
s
to
f
ilter
in
g
w
ater
f
o
r
p
lan
k
to
n
.
b.
Fil
ter
in
g
o
p
er
ato
r
(
s
elec
tiv
e
f
ee
d
in
g
)
:
o
n
l
y
th
e
b
es
t
ca
n
d
i
d
ate
is
r
etain
ed
,
m
i
m
ick
in
g
t
h
e
r
eten
t
io
n
o
f
n
u
tr
i
tio
u
s
p
la
n
k
to
n
.
c.
Sch
o
o
lin
g
m
o
v
e
m
e
n
t
(
co
llect
iv
e
e
x
p
lo
r
atio
n
)
:
a
n
c
h
o
v
ie
s
a
d
j
u
s
t
th
eir
p
o
s
itio
n
s
co
llecti
v
el
y
to
w
ar
d
s
t
h
e
g
lo
b
al
b
est,
en
s
u
r
in
g
p
o
p
u
lati
o
n
-
le
v
el
s
ea
r
c
h
.
d.
D
y
n
a
m
ic
f
ilter
i
n
g
s
ize:
t
h
e
f
ilter
s
tep
s
h
r
in
k
s
o
v
er
ti
m
e,
p
r
o
m
o
ti
n
g
ex
p
lo
r
atio
n
in
ea
r
l
y
s
tag
e
s
an
d
ex
p
lo
itatio
n
i
n
later
s
ta
g
es.
e.
E
liti
s
m
a
n
d
lo
ca
l
r
ef
in
e
m
e
n
t
:
t
h
e
b
est
s
o
lu
tio
n
s
ar
e
p
r
eser
v
ed
ea
ch
iter
atio
n
,
w
h
ile
t
h
e
g
lo
b
al
b
est
u
n
d
er
g
o
e
s
Gau
s
s
ia
n
-
b
ased
lo
ca
l sear
ch
f
o
r
f
u
r
t
h
er
r
ef
i
n
e
m
e
n
t.
2
.
6
.
1
.
I
nitia
liza
t
io
n (
s
cho
o
l f
o
r
m
a
t
io
n
)
E
a
c
h
a
n
c
h
o
v
y
r
e
p
r
e
s
e
n
t
s
a
s
o
l
u
t
i
o
n
v
e
c
t
o
r
.
T
h
e
i
n
i
t
i
a
l
s
c
h
o
o
l
i
s
r
a
n
d
o
m
l
y
d
i
s
t
r
i
b
u
t
e
d
i
n
t
h
e
s
e
a
r
c
h
s
p
a
c
e
:
0
~
(
,
)
,
=
1
,
2
,
…
,
(
1
)
w
h
er
e
N
is
th
e
s
ch
o
o
l
s
ize
(
p
o
p
u
latio
n
)
,
an
d
[
,
]
d
ef
in
es
th
e
o
ce
an
b
o
u
n
d
ar
ies
(
s
ea
r
c
h
s
p
ac
e)
.
T
h
is
co
r
r
esp
o
n
d
s
to
a
s
ch
o
o
l o
f
an
ch
o
v
ie
s
in
i
tiall
y
s
ca
tter
ed
ac
r
o
s
s
th
e
s
ea
.
2
.
6
.
2
.
F
it
nes
s
e
v
a
lua
t
io
n (
pla
n
k
t
o
n qua
lity
)
T
h
e
n
u
tr
itio
n
a
l v
al
u
e
o
f
p
lan
k
t
o
n
is
r
ep
r
esen
ted
b
y
th
e
f
it
n
es
s
f
u
n
c
tio
n
:
(
)
=
(
)
(
2
)
w
h
er
e
b
etter
f
it
n
es
s
v
al
u
es c
o
r
r
esp
o
n
d
to
m
o
r
e
n
u
tr
itio
u
s
p
la
n
k
to
n
ca
p
tu
r
ed
b
y
an
a
n
ch
o
v
y
.
2
.
6
.
3
.
Dy
na
m
ic
f
ilte
r
s
ize
(
mo
uth
a
pert
ure
)
An
c
h
o
v
ies
r
eg
u
late
t
h
eir
m
o
u
th
ap
er
tu
r
e
d
y
n
a
m
icall
y
.
A
t
t
h
e
s
tar
t,
t
h
e
f
ilter
is
w
id
e
(
e
x
p
lo
r
atio
n
)
,
b
u
t g
r
ad
u
all
y
n
ar
r
o
w
s
(
e
x
p
lo
itatio
n
)
o
v
er
ti
m
e:
(
)
=
−
(
−
)
(
3
)
w
h
er
e
is
t
h
e
iter
atio
n
i
n
d
ex
a
n
d
is
th
e
m
a
x
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
2
7
1
-
2
8
1
274
2
.
6
.
4
.
Ca
nd
ida
t
e
g
ener
a
t
io
n
(
pla
n
k
t
o
n inta
k
e
)
E
ac
h
an
c
h
o
v
y
g
e
n
er
ates
ca
n
d
id
ate
s
o
lu
tio
n
s
w
it
h
i
n
its
f
ilter
r
ad
iu
s
as d
e
m
o
n
s
tr
ated
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
An
ch
o
v
ies
f
ilter
i
n
g
p
lan
k
to
n
as a
n
a
n
alo
g
y
to
ca
n
d
id
ate
g
en
er
atio
n
a
n
d
s
elec
ti
v
e
f
ilter
i
n
g
i
n
A
F
A
,
=
(
)
+
(
)
.
,
,
,
~
(
−
1
,
1
)
(
4
)
T
h
e
b
est ca
n
d
id
ate
s
o
lu
tio
n
(
t
h
e
m
o
s
t n
u
tr
itio
u
s
p
lan
k
to
n
p
a
r
ticle)
is
s
elec
ted
.
(
)
=
a
r
g
(
,
(
)
)
(
5
)
2
.
6
.
5
.
P
o
s
it
io
n
up
da
t
e
(
s
cho
o
lin
g
a
nd
ra
nd
o
m
drif
t
)
An
c
h
o
v
ies
s
y
n
c
h
r
o
n
ize
w
it
h
t
h
e
g
lo
b
al
b
est
w
h
ile
m
ain
tain
i
n
g
r
a
n
d
o
m
d
r
if
t
.
(
+
1
)
=
(
)
+
.
(
(
)
−
(
)
)
+
.
_
(
6
)
w
h
er
e
g
b
est(
t
)
is
th
e
b
est
an
ch
o
v
y
i
n
th
e
s
ch
o
o
l,
ri
∼
U
(
−1
,
1
)
,
co
n
tr
o
ls
ex
p
lo
itatio
n
,
an
d
β
co
n
tr
o
ls
ex
p
lo
r
atio
n
.
2
.
6
.
6
.
E
litis
m
(
preserv
i
ng
s
t
ro
ng
est
a
ncho
v
ies
)
E
liti
s
m
is
ap
p
lied
to
p
r
eser
v
e
th
e
s
tr
o
n
g
est
a
n
ch
o
v
ies
r
e
p
r
esen
tin
g
h
ig
h
-
q
u
ali
t
y
s
o
l
u
ti
o
n
s
.
T
h
is
s
tr
ateg
y
e
n
s
u
r
es
th
a
t
th
e
b
est
s
o
lu
tio
n
s
ar
e
n
o
t
lo
s
t
d
u
r
in
g
th
e
iter
ativ
e
u
p
d
ate
p
r
o
ce
s
s
.
C
o
n
s
eq
u
e
n
tl
y
,
elitis
m
i
m
p
r
o
v
es
co
n
v
er
g
e
n
ce
co
n
s
i
s
ten
c
y
a
n
d
o
v
er
all
o
p
tim
iza
tio
n
p
er
f
o
r
m
an
ce
.
T
o
r
etain
h
ig
h
-
q
u
alit
y
s
o
lu
tio
n
s
,
elitis
m
is
ap
p
lied
:
(
+
1
)
=
{
∈
(
)
∪
(
+
1
)
|
(
)
≤
}
(
7
)
2
.
6
.
7
.
L
o
ca
l
s
ea
rc
h o
n g
lo
ba
l
bes
t
(
a
ncho
v
y
m
uta
t
io
n
)
An
c
h
o
v
ies
r
e
f
i
n
e
th
eir
f
ee
d
in
g
b
y
ad
j
u
s
ti
n
g
t
h
eir
f
ilter
m
e
s
h
.
T
h
e
g
lo
b
al
b
est
u
n
d
er
g
o
e
s
Gau
s
s
ia
n
m
u
tatio
n
:
′
(
)
=
(
)
+
∈
(
)
.
(
0
,
)
(
8
)
w
it
h
a
d
ec
a
y
i
n
g
p
er
tu
r
b
atio
n
f
ac
to
r
:
∈
(
)
=
∈
(
1
−
)
(
9
)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
A
n
ch
o
vy
-
i
n
s
p
ir
ed
filt
er a
lg
o
r
ith
m:
A
b
io
-
in
s
p
ir
ed
o
p
timiz
a
tio
n
a
p
p
r
o
a
ch
fo
r
…
(
A
z
r
u
l Ma
h
fu
r
d
z
)
275
Fin
all
y
,
t
h
e
g
lo
b
al
b
est is
u
p
d
ated
b
y
s
elec
tin
g
t
h
e
s
o
lu
tio
n
with
t
h
e
lo
w
e
s
t
f
it
n
ess
v
al
u
e
a
m
o
n
g
t
h
e
p
r
ev
io
u
s
g
lo
b
al
b
est an
d
th
e
m
u
tated
ca
n
d
id
ate:
(
+
1
)
=
a
r
g
{
(
(
)
)
,
(
′
(
)
)
}
(
1
0
)
3.
AL
G
O
RI
T
H
M
T
E
ST
I
NG
A
ND
E
VA
L
UA
T
I
O
N
Six
b
e
n
ch
m
ar
k
f
u
n
c
tio
n
s
w
e
r
e
e
m
p
lo
y
ed
f
o
r
th
e
s
i
m
u
lati
o
n
s
,
as
d
ef
in
ed
i
n
[
2
1
]
–
[
2
6
]
.
T
ab
le
1
p
r
o
v
id
es
th
e
co
r
r
esp
o
n
d
in
g
s
ea
r
ch
d
o
m
ai
n
,
i
n
itializat
io
n
r
an
g
e,
a
n
d
g
lo
b
al
o
p
ti
m
u
m
m
ea
n
w
h
i
le
T
ab
le
2
s
h
o
w
ed
p
ar
a
m
eter
s
et
tin
g
f
o
r
AF
A
,
P
SO
an
d
G
A
.
All
f
u
n
cti
o
n
s
w
er
e
tes
ted
in
a
3
0
-
d
i
m
e
n
s
io
n
al
s
p
ac
e
u
n
d
er
m
i
n
i
m
izatio
n
.
Am
o
n
g
t
h
e
m
,
Sp
h
er
e,
R
o
s
en
b
r
o
ck
a
n
d
Sch
w
e
f
el
1
.
2
ar
e
u
n
i
m
o
d
al,
w
h
er
ea
s
R
a
s
tr
i
g
in
,
Gr
ie
w
a
n
k
,
an
d
A
ck
le
y
ar
e
m
u
lti
m
o
d
al
w
it
h
n
u
m
er
o
u
s
lo
ca
l
m
i
n
i
m
a.
ℎ
(
)
=
∑
2
=
1
(
1
1
)
(
)
=
∑
[
100
(
+
1
−
1
=
1
−
2
)
+
(
−
1
)
²
(
1
2
)
ℎ
1
.
2
(
)
=
∑
(
∑
=
1
=
1
)
²
(
13)
(
)
=
10
+
∑
[
2
−
10
c
os
(
2
)
]
=
1
(
1
4
)
(
)
=
1
+
1
4000
∑
2
=
1
−
∏
=
1
(
√
)
(
1
5
)
(
)
=
−
20
e
xp
(
−
0
.
2
√
1
∑
2
=
1
)
(
1
6
)
e
xp
(
1
∑
c
os
(
2
=
1
)
+
20
+
T
ab
le
1
.
Fu
n
ctio
n
s
u
s
ed
: sear
ch
s
p
ac
e,
in
it
ializatio
n
r
a
n
g
e,
a
n
d
o
p
ti
m
a
F
u
n
c
t
i
o
n
P
a
r
a
me
t
e
r
s
S
e
a
r
c
h
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1
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Feb
r
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ar
y
20
26
:
2
7
1
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276
s
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T
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
2
7
1
-
2
8
1
278
Fin
all
y
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SO
r
e
co
r
d
ed
co
m
p
etitiv
e
r
es
u
lts
,
w
h
ile
G
A
p
r
o
d
u
ce
d
s
u
b
s
tan
t
iall
y
p
o
o
r
er
o
u
tco
m
e
s
(
T
ab
le
8
)
.
A
s
ill
u
s
tr
ated
in
Fi
g
u
r
e
8
,
A
F
A
m
a
in
tai
n
ed
co
n
s
is
te
n
t
co
n
v
er
g
e
n
ce
w
i
th
r
elati
v
el
y
lo
w
v
ar
ia
n
ce
ac
r
o
s
s
r
u
n
s
.
O
v
er
a
ll,
th
e
s
i
m
u
latio
n
r
esu
lts
ac
r
o
s
s
al
l
s
ix
b
en
c
h
m
ar
k
f
u
n
ctio
n
s
clea
r
l
y
i
n
d
icate
th
a
t
AF
A
co
n
s
is
te
n
tl
y
o
u
tp
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f
o
r
m
s
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SO
a
n
d
G
A
i
n
ter
m
s
o
f
co
n
v
er
g
e
n
ce
s
p
ee
d
,
ac
cu
r
ac
y
,
an
d
s
tab
il
it
y
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T
ab
le
7
.
Sim
u
latio
n
r
esu
lts
f
o
r
Gr
ie
w
a
n
k
f
u
n
c
tio
n
A
l
g
o
r
i
t
h
m
B
e
st
f
i
t
n
e
ss me
a
n
S
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
(
st
d
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i
n
i
m
u
m
f
i
t
n
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a
x
i
m
u
m
f
i
t
n
e
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A
F
A
0
.
1
0
2
6
5
0
.
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6
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4
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9
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3
Fig
u
r
e
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.
C
o
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v
er
g
en
ce
c
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r
v
e
co
m
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ar
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o
n
f
o
r
Gr
ie
w
a
n
k
f
u
n
ctio
n
T
ab
le
8
.
Sim
u
latio
n
r
esu
lts
f
o
r
A
c
k
le
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f
u
n
c
tio
n
A
l
g
o
r
i
t
h
m
B
e
st
f
i
t
n
e
ss me
a
n
S
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
(
st
d
)
M
i
n
i
m
u
m
f
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M
a
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m
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A
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8
8
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0
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6
7
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9
2
5
.
3
5
0
8
PSO
2
.
1
6
3
7
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.
0
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0
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6
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5
.
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3
4
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6
Fig
u
r
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.
C
o
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en
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c
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r
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m
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ar
is
o
n
f
o
r
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c
k
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u
n
ct
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o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
T
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L
KOM
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K
A
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elec
o
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m
u
n
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p
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l
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o
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ith
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b
io
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in
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(
A
z
r
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279
5.
DIS
CU
SS
I
O
N
T
h
e
co
m
p
ar
ativ
e
an
al
y
s
i
s
o
f
AF
A
,
P
SO,
an
d
GA
ac
r
o
s
s
s
ix
b
en
ch
m
ar
k
f
u
n
ctio
n
s
h
i
g
h
l
ig
h
ts
th
e
d
is
tin
ct
ad
v
an
tag
e
s
o
f
t
h
e
p
r
o
p
o
s
ed
A
F
A
ap
p
r
o
ac
h
.
Fo
r
u
n
i
m
o
d
al
f
u
n
c
tio
n
s
s
u
ch
a
s
Sp
h
e
r
e,
R
o
s
en
b
r
o
ck
,
an
d
Sch
w
e
f
el
1
.
2
,
A
F
A
co
n
s
i
s
te
n
tl
y
ac
h
ie
v
ed
lo
w
er
b
est
a
n
d
m
ea
n
f
i
tn
e
s
s
v
alu
e
s
w
it
h
s
m
aller
s
ta
n
d
ar
d
d
ev
iatio
n
s
.
T
h
is
i
n
d
icate
s
t
h
at
AF
A
i
s
h
ig
h
l
y
e
f
f
ec
tiv
e
in
r
ef
i
n
in
g
s
o
lu
tio
n
s
to
w
ar
d
s
th
e
g
lo
b
al
o
p
tim
u
m
a
n
d
d
em
o
n
s
tr
ate
s
s
tr
o
n
g
ex
p
lo
ita
tio
n
ca
p
ab
ilit
y
.
I
n
co
n
tr
ast,
P
SO
ex
h
ib
ited
s
lo
w
er
co
n
v
e
r
g
en
ce
an
d
lar
g
er
v
ar
ian
ce
,
w
h
i
le
GA
s
tr
u
g
g
led
s
ig
n
i
f
ica
n
tl
y
i
n
th
ese
p
r
o
b
le
m
s
d
u
e
to
p
r
em
at
u
r
e
co
n
v
er
g
e
n
ce
an
d
w
ea
k
er
lo
ca
l
s
ea
r
ch
ab
ilit
y
.
Fo
r
m
u
lti
m
o
d
al
f
u
n
ctio
n
s
s
u
c
h
as
R
a
s
tr
ig
i
n
,
Gr
ie
w
a
n
k
,
a
n
d
A
c
k
le
y
,
AF
A
ag
a
in
s
h
o
w
e
d
s
u
p
er
io
r
r
o
b
u
s
tn
es
s
.
T
h
e
ab
ilit
y
o
f
A
F
A
to
b
alan
ce
e
x
p
lo
r
a
tio
n
an
d
ex
p
lo
itatio
n
allo
w
e
d
it
to
escap
e
lo
ca
l
m
i
n
i
m
a
an
d
co
n
v
er
g
e
clo
s
er
to
th
e
g
lo
b
al
o
p
tim
u
m
.
T
h
e
co
n
v
er
g
e
n
ce
cu
r
v
es
(
Fi
g
u
r
e
s
6
to
8
)
co
n
f
ir
m
t
h
at
AF
A
m
ai
n
tai
n
ed
s
tab
le
p
r
o
g
r
ess
ac
r
o
s
s
iter
atio
n
s
,
w
h
er
ea
s
P
SO
an
d
G
A
o
f
te
n
b
ec
a
m
e
tr
ap
p
ed
in
s
u
b
o
p
ti
m
al
r
eg
io
n
s
o
r
d
is
p
la
y
ed
h
i
g
h
f
l
u
ct
u
atio
n
s
.
No
tab
l
y
,
in
t
h
e
A
c
k
le
y
f
u
n
c
tio
n
,
P
SO
p
r
o
d
u
ce
d
r
es
u
lts
r
elati
v
el
y
c
lo
s
e
to
A
F
A
,
b
u
t G
A
r
e
m
ai
n
ed
s
i
g
n
i
f
ican
tl
y
i
n
f
er
io
r
in
p
er
f
o
r
m
a
n
ce
.
T
h
ese
f
i
n
d
in
g
s
d
e
m
o
n
s
tr
ate
t
h
at
th
e
u
n
iq
u
e
f
ilter
-
b
ased
m
ec
h
an
i
s
m
i
n
A
F
A
p
r
o
v
id
es
b
o
th
d
iv
er
s
it
y
p
r
eser
v
atio
n
a
n
d
d
ir
ec
tio
n
al
g
u
id
a
n
ce
,
w
h
ich
i
m
p
r
o
v
e
s
g
l
o
b
al
s
ea
r
ch
ca
p
ac
it
y
.
T
h
e
ad
ap
tiv
e
m
o
v
e
m
en
t
o
f
ca
n
d
id
ate
s
o
lu
t
io
n
s
i
n
AF
A
p
r
ev
en
t
s
s
tag
n
atio
n
,
e
n
s
u
r
i
n
g
a
h
ig
h
er
p
r
o
b
ab
ilit
y
o
f
r
ea
c
h
in
g
th
e
g
lo
b
al
o
p
ti
m
u
m
co
m
p
ar
ed
to
co
n
v
e
n
tio
n
a
l P
SO
an
d
G
A
.
6.
CO
NCLU
SI
O
N
T
h
e
s
i
m
u
la
tio
n
r
es
u
lts
o
n
s
i
x
b
en
ch
m
ar
k
f
u
n
ctio
n
s
d
e
m
o
n
s
t
r
ate
th
at
th
e
p
r
o
p
o
s
ed
A
FA
s
i
g
n
i
f
ica
n
tl
y
o
u
tp
er
f
o
r
m
s
co
n
v
e
n
tio
n
al
P
S
O
an
d
G
A
i
n
ter
m
s
o
f
co
n
v
er
g
e
n
ce
s
p
ee
d
,
ac
cu
r
ac
y
,
a
n
d
s
tab
ilit
y
.
A
F
A
co
n
s
is
ten
tl
y
p
r
o
d
u
ce
d
lo
w
er
b
est
an
d
m
ea
n
f
itn
e
s
s
v
al
u
es
w
it
h
r
ed
u
ce
d
v
ar
ia
n
ce
ac
r
o
s
s
u
n
i
m
o
d
al
a
n
d
m
u
lti
m
o
d
al
f
u
n
ctio
n
s
.
I
n
u
n
i
m
o
d
al
p
r
o
b
le
m
s
,
AF
A
e
x
h
ib
ited
s
tr
o
n
g
e
x
p
lo
itatio
n
a
b
ilit
y
b
y
r
ap
id
l
y
co
n
v
er
g
i
n
g
to
th
e
g
lo
b
al
o
p
tim
u
m
.
Fo
r
m
u
lti
m
o
d
al
p
r
o
b
le
m
s
,
AF
A
m
ai
n
tai
n
ed
r
o
b
u
s
tn
e
s
s
b
y
ef
f
ec
ti
v
e
l
y
av
o
id
i
n
g
lo
ca
l
m
in
i
m
a
an
d
ac
h
iev
in
g
b
etter
g
lo
b
al
s
ea
r
ch
p
er
f
o
r
m
an
ce
.
O
v
er
all,
t
h
ese
r
esu
lt
s
co
n
f
ir
m
t
h
at
AF
A
p
r
o
v
id
es
a
co
m
p
etiti
v
e
an
d
r
eliab
le
o
p
ti
m
izatio
n
to
o
l,
ca
p
ab
le
o
f
ad
d
r
ess
in
g
co
m
p
l
ex
o
p
ti
m
iza
tio
n
la
n
d
s
ca
p
es
m
o
r
e
ef
f
icie
n
tl
y
t
h
a
n
tr
ad
itio
n
al
m
eta
h
eu
r
i
s
tic
alg
o
r
ith
m
s
.
Fu
tu
r
e
w
o
r
k
w
ill
f
o
cu
s
o
n
ex
te
n
d
i
n
g
t
h
e
ap
p
licatio
n
o
f
AF
A
to
r
ea
l
-
w
o
r
ld
o
p
ti
m
izatio
n
p
r
o
b
lem
s
an
d
ex
p
lo
r
in
g
h
y
b
r
id
v
ar
ia
n
t
s
th
at
m
a
y
f
u
r
th
er
en
h
a
n
ce
its
p
er
f
o
r
m
an
ce
.
F
UNDIN
G
I
NF
O
RM
AT
I
O
N
No
f
u
n
d
in
g
is
i
n
v
o
lv
ed
i
n
th
i
s
w
o
r
k
.
A
UT
H
O
R
CO
NT
RIB
UT
I
O
NS ST
A
T
E
M
E
NT
T
h
is
j
o
u
r
n
al
u
s
e
s
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT
)
to
r
ec
o
g
n
ize
in
d
i
v
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
t
h
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
lla
b
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
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M
So
Va
Fo
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Vi
Su
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Fu
A
zr
u
l
Ma
h
f
u
r
d
z
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Mu
h
a
m
m
ad
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h
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w
a
w
i
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Su
n
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d
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Mu
h
a
m
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ad
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d
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C
:
C
o
n
c
e
p
t
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a
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z
a
t
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n
M
:
M
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t
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f
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n
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