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I
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)
Vo
l.
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.
1
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Feb
r
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ar
y
201
8
,
p
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In
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th
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v
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d
th
e
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CS
(
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),
f
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it
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lrea
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d
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s.
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ey
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d
:
An
t
co
lo
n
y
o
p
ti
m
izatio
n
L
o
w
p
ass
s
tate
v
ar
iab
le
f
ilter
Me
tah
e
u
r
is
tic
Op
ti
m
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n
Co
p
y
rig
h
t
©
2
0
1
8
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Kr
itele
L
o
u
b
n
a,
Facu
lt
y
o
f
Sc
ien
ce
s
Dh
ar
el
M
h
r
az
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i
v
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s
it
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Sid
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m
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en
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b
d
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cc
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m
ail: lo
u
b
n
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k
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itele@
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c
o
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
o
p
tim
al
s
izi
n
g
o
f
a
n
alo
g
cir
cu
its
is
o
n
e
o
f
t
h
e
m
o
s
t
co
m
p
licated
ac
ti
v
itie
s
,
d
u
e
to
t
h
e
n
u
m
b
er
o
f
v
ar
iab
les
i
n
v
o
l
v
ed
,
to
th
e
n
u
m
b
er
o
f
r
eq
u
ir
ed
o
b
j
ec
tiv
es
to
b
e
o
p
tim
ized
an
d
to
t
h
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c
o
n
s
tr
ain
t
f
u
n
ctio
n
s
r
estrictio
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s
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h
e
ai
m
is
to
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u
t
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a
te
th
is
ta
s
k
i
n
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er
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cir
cu
i
ts
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an
d
s
izin
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ec
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tl
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th
e
u
s
ed
o
f
t
h
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m
eta
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e
u
r
is
tic
s
h
av
e
p
r
o
v
ed
a
ca
p
ac
it
y
to
tr
ea
t
th
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p
r
o
b
le
m
e
f
f
ic
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t
l
y
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s
u
ch
a
s
T
ab
u
Sear
ch
(
T
S)
[
1
]
,
Gen
etic
A
l
g
o
r
ith
m
s
(
GA
)
[
2
]
,
L
o
ca
l
s
ea
r
ch
(
L
S)
[
3
]
,
Sim
u
lated
An
n
ea
lin
g
(
S
A
)
[
4
]
,
An
t
C
o
lo
n
y
Op
ti
m
izatio
n
(
A
C
O)
[
5
-
7
]
an
d
P
ar
ticle
S
w
ar
m
Op
ti
m
izat
io
n
(
P
SO)
[
8
]
.
A
cti
v
e
a
n
alo
g
f
ilter
s
ar
e
co
n
s
t
itu
ted
o
f
a
m
p
l
if
y
i
n
g
ele
m
e
n
ts
,
r
esis
to
r
s
an
d
ca
p
ac
ito
r
s
;
th
er
ef
o
r
e,
th
e
f
ilter
d
es
ig
n
d
ep
en
d
s
s
tr
o
n
g
l
y
o
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p
as
s
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v
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co
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p
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n
en
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v
alu
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s
.
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w
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m
a
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f
ac
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r
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if
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lt a
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al
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n
o
f
p
ass
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v
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co
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p
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n
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s
.
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n
d
ee
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s
ea
r
ch
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all
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o
s
s
i
b
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atio
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s
i
n
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ed
v
alu
e
s
f
o
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ca
p
ac
ito
r
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d
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esis
to
r
s
is
a
n
ex
h
a
u
s
tiv
e
p
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o
ce
s
s
,
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ec
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s
e
d
is
cr
ete
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m
p
o
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s
ar
e
p
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u
ce
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ac
co
r
d
in
g
to
a
s
er
ies
o
f
v
alu
es
co
n
s
ta
n
ts
s
u
c
h
as th
e
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er
ie
s
: E
1
2
,
E
2
4
,
E
4
8
,
E
9
6
o
r
E
1
9
2
.
C
o
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s
eq
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en
tl
y
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an
i
n
telli
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t
s
ea
r
ch
m
et
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eq
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ir
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s
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p
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ta
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e
w
i
th
h
ig
h
ac
cu
r
ac
y
,
m
u
s
t
b
e
u
s
ed
.
T
h
e
A
C
O
tec
h
n
iq
u
e
h
as
b
ee
n
ap
p
lied
s
u
cc
es
s
f
u
ll
y
to
s
o
l
v
e
a
v
ar
iet
y
o
f
o
p
ti
m
izatio
n
p
r
o
b
lem
s
,
s
u
c
h
as
th
e
p
r
ed
ictio
n
o
f
th
e
co
n
s
u
m
p
tio
n
o
f
e
lectr
icit
y
[
9
]
,
th
e
tr
a
v
eli
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g
s
ale
s
m
a
n
p
r
o
b
le
m
(
T
S
P
)
[
1
0
]
,
th
e
v
eh
ic
le
r
o
u
ti
n
g
p
r
o
b
le
m
[
1
1
]
,
th
e
o
p
ti
m
izat
io
n
o
f
p
o
w
er
f
lo
w
[
1
2
]
,
th
e
lear
n
i
n
g
p
r
o
b
le
m
[
1
3
]
an
d
th
e
f
ield
o
f
an
alo
g
cir
c
u
its
d
esi
g
n
[
5
-
7
]
.
I
n
th
i
s
w
o
r
k
,
w
e
p
r
o
p
o
s
e
to
ap
p
ly
t
h
r
ee
v
ar
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t
s
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t
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AC
O
tec
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iq
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s
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c
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as,
t
h
e
AS
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A
n
t
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)
,
t
h
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MM
A
S
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Ma
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in
An
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te
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a
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th
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AC
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f
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t
h
e
o
p
ti
m
al
s
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g
o
f
th
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L
o
w
-
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
2
2
7
–
2
3
5
228
P
ass
State
Var
iab
le
Fil
ter
co
n
s
id
er
in
g
t
w
o
o
b
j
ec
tiv
es
f
u
n
ctio
n
s
,
t
h
e
c
u
to
f
f
f
r
eq
u
en
c
y
an
d
th
e
s
elec
tiv
it
y
f
ac
to
r
.
T
h
e
r
em
ain
d
er
o
f
th
e
p
ap
er
is
s
tr
u
ct
u
r
ed
as
f
o
llo
w
s
:
T
h
e
s
ec
o
n
d
s
ec
tio
n
p
r
esen
ts
an
o
v
er
v
i
e
w
o
f
t
h
e
AC
O
tec
h
n
iq
u
e
a
n
d
h
ig
h
li
g
h
t
s
its
th
r
ee
m
o
s
t
i
m
p
o
r
tan
t
v
ar
i
an
ts
.
T
h
e
t
h
ir
d
s
ec
tio
n
d
ea
ls
w
it
h
t
h
e
ap
p
licatio
n
ex
a
m
p
le.
T
h
e
f
o
u
r
th
s
ec
tio
n
p
r
esen
ts
th
e
s
i
m
u
latio
n
a
n
d
t
h
e
AC
O
v
ar
ia
n
ts
co
m
p
ar
is
o
n
.
T
h
e
f
i
f
t
h
s
ec
ti
on
g
iv
e
s
s
o
m
e
co
m
p
ar
is
o
n
s
w
it
h
p
u
b
lis
h
ed
w
o
r
k
s
.
T
h
e
last
s
ec
t
io
n
s
u
m
m
ar
izes t
h
e
m
ai
n
r
esu
l
ts
o
f
t
h
e
w
o
r
k
.
2.
ANT CO
L
O
N
Y
O
P
T
I
M
I
Z
A
T
I
O
N:
ACO
T
E
CH
NI
Q
U
E
:
AN
O
VE
R
VI
E
W
AC
O
h
as
b
ee
n
i
n
s
p
ir
ed
b
y
th
e
f
o
r
ag
i
n
g
b
eh
a
v
io
r
o
f
r
ea
l
a
n
t
co
lo
n
ies.
Fi
g
u
r
e
1
s
h
o
w
s
a
n
il
lu
s
tr
atio
n
o
f
th
e
ab
ilit
y
o
f
an
t
s
to
f
i
n
d
th
e
s
h
o
r
tes
t
p
ath
b
et
w
ee
n
f
o
o
d
an
d
th
eir
n
est
[
14
]
,
[
1
5
]
.
I
t
is
illu
s
tr
ated
t
h
r
o
u
g
h
th
e
ex
a
m
p
le
o
f
t
h
e
ap
p
ea
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ce
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an
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b
s
tacle
o
n
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p
ath
.
E
v
er
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a
n
t
i
n
itia
ll
y
c
h
o
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s
e
s
p
ath
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to
m
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v
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b
s
ta
n
ce
,
ca
lled
p
h
er
o
m
o
n
e
i
n
t
h
e
p
at
h
.
T
h
e
q
u
a
n
tit
y
o
f
p
h
er
o
m
o
n
e
d
ep
o
s
ited
w
il
l g
u
id
e
o
th
er
an
t
s
to
th
e
f
o
o
d
s
o
u
r
ce
.
T
h
e
in
d
ir
ec
t c
o
m
m
u
n
icatio
n
b
et
w
ee
n
t
h
e
an
t
s
v
ia
t
h
e
p
h
er
o
m
o
n
e
tr
ai
l
allo
w
s
th
e
m
to
f
i
n
d
s
h
o
r
test
p
ath
s
f
r
o
m
t
h
eir
n
e
s
t to
th
e
f
o
o
d
s
o
u
r
ce
.
Fig
u
r
e
1.
Self
-
ad
ap
tiv
e
b
eh
a
v
io
r
o
f
a
r
ea
l a
n
t c
o
lo
n
y
,
(
a)
An
ts
g
o
in
s
ea
r
ch
o
f
f
o
o
d
;
(
b
)
A
n
ts
f
o
llo
w
a
p
ath
b
et
w
ee
n
n
e
s
t a
n
d
f
o
o
d
s
o
u
r
ce
.
T
h
ey
; c
h
o
o
s
e,
w
i
th
eq
u
a
l p
r
o
b
ab
ilit
y
,
w
h
et
h
er
to
s
h
o
r
test
o
r
lo
n
g
es
t p
ath
;
(
c)
T
h
e
m
aj
o
r
ity
o
f
a
n
ts
h
a
v
e
ch
o
s
en
t
h
e
s
h
o
r
test
p
ath
.
2
.
1
.
Ant
S
y
s
t
e
m
T
h
e
f
ir
s
t
v
ar
ian
t
o
f
t
h
e
AC
O
is
«
An
t
S
y
s
te
m
»
(
A
S)
w
h
ic
h
is
u
s
ed
to
s
o
lv
e
co
m
b
in
ato
r
ial
o
p
tim
izatio
n
p
r
o
b
le
m
s
s
u
ch
a
s
th
e
tr
av
eli
n
g
s
a
les
m
an
p
r
o
b
le
m
(
T
SP
)
,
v
eh
icle
r
o
u
ti
n
g
p
r
o
b
lem
.
Fo
r
s
o
lv
i
n
g
s
u
c
h
p
r
o
b
lem
s
,
an
t
s
r
an
d
o
m
l
y
s
elec
t
t
h
e
v
er
te
x
to
b
e
v
is
i
t
ed
.
W
h
en
an
t
k
is
i
n
v
er
te
x
i
,
th
e
p
r
o
b
ab
ilit
y
o
f
g
o
in
g
to
v
er
te
x
j
is
g
iv
e
n
b
y
(
1
)
:
J
i
if
J
i
if
.
.
P
k
i
k
i
J
l
ij
ij
ij
ij
k
ij
k
i
0
(
1
)
W
h
er
e
J
i
k
is
th
e
s
et
o
f
n
e
ig
h
b
o
r
s
o
f
v
er
tex
i
o
f
th
e
k
t
h
an
t,
τ
ij
is
th
e
a
m
o
u
n
t
o
f
p
h
er
o
m
o
n
e
tr
ail
o
n
ed
g
e
(
i,
j
)
,
α
an
d
β
ar
e
w
eig
h
ti
n
g
s
t
h
at
co
n
tr
o
l
t
h
e
p
h
er
o
m
o
n
e
tr
ail
a
n
d
t
h
e
v
is
ib
ili
t
y
v
alu
e,
i.e
.
η
ij
,
w
h
ich
ex
p
r
ess
io
n
i
s
g
iv
e
n
b
y
(
2
)
:
d
ij
ij
(
2
)
T
h
e
d
ij
is
th
e
d
is
tan
ce
b
et
w
ee
n
v
er
tices
i
an
d
j
.
On
ce
all
an
ts
h
av
e
co
m
p
leted
a
to
u
r
,
th
e
p
h
er
o
m
o
n
e
tr
ail
s
ar
e
u
p
d
ated
.
T
h
e
u
p
d
ate
f
o
llo
w
s
th
is
r
u
le:
m
k
k
ij
ij
ij
1
1
(
3
)
W
h
er
e
is
th
e
ev
ap
o
r
atio
n
r
ate,
m
is
th
e
n
u
m
b
er
o
f
a
n
ts
,
a
n
d
Δ
τ
ij
k
(
t)
is
t
h
e
q
u
a
n
tit
y
o
f
p
h
er
o
m
o
n
e
laid
o
n
ed
g
e
(
i,
j
)
b
y
an
t
k
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
t Co
lo
n
y
Op
timiz
a
tio
n
fo
r
Op
tima
l
Lo
w
-
P
a
s
s
S
ta
te
V
a
r
ia
b
le
F
ilter
S
iz
in
g
(
K
r
itele
Lo
u
b
n
a
)
229
o
t
h
e
r
w
i
s
e
t
o
u
r
its
in
j)
(
i
,
e
d
g
e
u
s
e
d
k
ant
if
L
Q
k
k
ij
(
4
)
Q
is
a
co
n
s
ta
n
t a
n
d
L
K
i
s
th
e
le
n
g
t
h
o
f
t
h
e
to
u
r
co
n
s
tr
u
cted
b
y
an
t
k
.
2
.
2
.
M
a
x
-
m
i
n Ant
Sy
s
t
e
m
T
h
e
Ma
x
-
Mi
n
An
t
S
y
s
te
m
i
s
an
o
th
er
v
ar
ia
n
t
o
f
AC
O,
w
h
ich
w
as
d
ev
e
lo
p
ed
b
y
St
ü
tzl
e
&
Ho
o
s
[
1
5
]
,
[
1
6
]
to
im
p
r
o
v
e
co
n
v
er
g
en
ce
o
f
A
S.
Max
-
Min
an
t
s
y
s
te
m
h
a
s
al
w
a
y
s
b
ee
n
to
ac
h
ie
v
e
t
h
e
o
p
ti
m
al
p
at
h
s
ea
r
c
h
i
n
g
b
y
all
o
w
i
n
g
o
n
l
y
t
h
e
b
es
t
s
o
lu
tio
n
to
i
n
cr
ea
s
e
th
e
in
f
o
r
m
atio
n
an
d
u
s
e
a
s
i
m
p
le
m
ec
h
an
i
s
m
to
l
i
m
it
t
h
e
p
h
er
o
m
o
n
e,
w
h
ic
h
e
f
f
ec
ti
v
el
y
av
o
id
th
e
p
r
e
m
at
u
r
e
s
ta
g
n
at
i
o
n
.
MM
AS
w
h
ich
b
ased
o
n
th
e
an
t
s
y
s
te
m
d
o
es
th
e
f
o
llo
w
i
n
g
ar
ea
s
o
f
i
m
p
r
o
v
e
m
en
t
:
a.
Du
r
in
g
t
h
e
o
p
er
atio
n
o
f
th
e
al
g
o
r
ith
m
,
o
n
l
y
a
s
in
g
le
a
n
t
w
as
allo
w
ed
to
in
cr
ea
s
e
th
e
p
h
er
o
m
o
n
e.
T
h
e
an
t
m
a
y
b
e
t
h
e
o
n
e
w
h
ic
h
f
o
u
n
d
th
e
b
est
s
o
l
u
tio
n
in
t
h
e
c
u
r
r
en
t
iter
atio
n
o
r
t
h
e
o
n
e
w
h
ic
h
f
o
u
n
d
th
e
b
est
s
o
lu
tio
n
f
r
o
m
t
h
e
b
eg
i
n
n
i
n
g
o
f
th
e
tr
ial.
b.
I
n
o
r
d
er
to
av
o
id
s
tag
n
atio
n
o
f
t
h
e
s
ea
r
ch
,
th
e
r
an
g
e
o
f
t
h
e
p
h
er
o
m
o
n
e
tr
ail
s
is
li
m
it
to
an
in
ter
v
al
[
τ
m
in
,
τ
m
ax
].
c.
T
h
e
p
h
er
o
m
o
n
e
i
s
in
itialized
t
o
τ
m
ax
i
n
ea
c
h
ed
g
e.
2
.
3
.
Ant
Co
lo
ny
S
y
s
t
e
m
:
T
h
e
A
C
S
al
g
o
r
ith
m
r
ep
r
esen
t
s
an
i
m
p
r
o
v
e
m
en
t
w
it
h
r
esp
ec
t
to
th
e
AS.
T
h
e
AC
S
i
n
co
r
p
o
r
ates
th
r
ee
m
ai
n
d
if
f
er
en
ce
s
w
it
h
r
esp
ec
t to
th
e
AS
alg
o
r
it
h
m
:
a.
AC
S
i
n
tr
o
d
u
ce
d
a
tr
a
n
s
it
io
n
r
u
le
d
ep
en
d
in
g
o
n
a
p
ar
a
m
eter
q
0
,
w
h
ic
h
p
r
o
v
id
es
a
d
ir
ec
t
w
a
y
to
b
ala
n
ce
b
et
w
ee
n
d
i
v
er
s
i
f
icatio
n
an
d
in
ten
s
i
f
icat
io
n
.
I
n
t
h
e
AC
S
alg
o
r
ith
m
,
an
an
t
p
o
s
itio
n
ed
o
n
n
o
d
e
i
ch
o
o
s
es
th
e
cit
y
j
to
m
o
v
e
to
b
y
ap
p
l
y
i
n
g
t
h
e
r
u
le
g
i
v
en
b
y
:
0
0
iJ
iu
J
u
q
q
if
q
q
if
t
j
k
i
*
m
a
x
a
r
g
(
5
)
W
h
er
e
q
is
a
r
an
d
o
m
n
u
m
b
er
u
n
i
f
o
r
m
l
y
d
is
tr
ib
u
ted
in
[
0
,
1
]
,
q
0
is
a
p
ar
am
eter
(
0
≤
q
0
≤1).
b.
T
h
e
g
lo
b
al
u
p
d
atin
g
r
u
le
is
a
p
p
lied
o
n
l
y
to
ed
g
es
w
h
ich
b
elo
n
g
to
t
h
e
b
est
an
t
to
u
r
.
T
h
e
p
h
er
o
m
o
n
e
lev
el
is
u
p
d
ated
as f
o
llo
w
s
:
ij
ij
ij
(
6
)
W
h
er
e
o
t
h
e
rw
i
se
t
o
u
r
g
l
o
b
a
l
b
e
st
j
i,
if
L
ij
(
7
)
c.
W
h
ile
an
t
s
co
n
s
tr
u
ct
a
s
o
l
u
tio
n
a
lo
ca
l p
h
er
o
m
o
n
e
u
p
d
atin
g
r
u
le
is
ap
p
lied
:
i
n
t
ij
ij
τ
ρ
τ
ρ
1
τ
(
8
)
3.
AP
P
L
I
CA
T
I
O
N
T
O
T
H
E
O
P
T
I
M
AL
DE
S
I
G
N
O
F
L
O
W
P
ASS
S
T
A
T
E
VAR
I
AB
L
E
F
I
L
T
E
R
T
h
e
th
r
ee
p
r
o
p
o
s
ed
v
ar
ian
t
s
o
f
AC
O
al
g
o
r
ith
m
w
er
e
u
s
ed
to
o
p
ti
m
ize
th
e
an
alo
g
cir
c
u
it,
n
a
m
el
y
State
Var
iab
le
Fil
ter
.
An
alo
g
ac
tiv
e
Fil
ter
s
ar
e
i
m
p
o
r
tan
t
b
u
ild
in
g
b
lo
ck
s
i
n
s
ig
n
al
p
r
o
ce
s
s
i
n
g
cir
cu
its
.
T
h
e
y
ar
e
w
id
el
y
u
s
ed
i
n
th
e
s
ep
ar
atio
n
an
d
d
e
m
o
d
u
latio
n
o
f
s
ig
n
als,
f
r
eq
u
e
n
c
y
s
elec
tio
n
d
ec
o
d
in
g
,
a
n
d
esti
m
atio
n
o
f
a
s
ig
n
al
f
r
o
m
n
o
i
s
e
[
1
7
]
.
An
alo
g
ac
ti
v
e
f
ilter
s
ar
e
ch
ar
ac
ter
ized
b
y
f
o
u
r
b
asic
p
r
o
p
er
ties
:
th
e
f
ilter
t
y
p
e
(
lo
w
-
p
ass
,
h
ig
h
-
p
ass
,
b
an
d
p
ass
,
an
d
o
th
er
s
)
,
th
e
p
as
s
b
an
d
g
ain
(
g
e
n
er
al
l
y
all
t
h
e
f
ilter
s
h
av
e
u
n
i
t
y
g
a
in
in
t
h
e
p
ass
b
an
d
)
,
th
e
c
u
to
f
f
f
r
eq
u
en
c
y
(
t
h
e
p
o
in
t
w
h
er
e
t
h
e
o
u
tp
u
t
lev
el
h
as
f
alle
n
b
y
3
d
B
f
r
o
m
t
h
e
m
a
x
i
m
u
m
le
v
el
w
it
h
i
n
t
h
e
p
ass
b
an
d
)
,
an
d
th
e
q
u
ali
t
y
f
ac
to
r
Q
(
d
eter
m
in
e
s
t
h
e
s
h
ar
p
n
e
s
s
o
f
t
h
e
a
m
p
lit
u
d
e
r
esp
o
n
s
e
c
u
r
v
e)
[
1
8
]
.
A
s
tate
v
ar
iab
le
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
2
2
7
–
2
3
5
230
f
ilter
(
SVF)
r
ea
lizes
t
h
e
s
ta
te
-
s
p
ac
e
m
o
d
el
d
ir
ec
tl
y
.
T
h
e
in
s
t
an
tan
eo
u
s
o
u
tp
u
t
v
o
ltag
e
o
f
o
n
e
o
f
t
h
e
in
te
g
r
ato
r
s
co
r
r
esp
o
n
d
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ate
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
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&
C
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p
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I
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N:
2
0
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Fil
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f
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S,
t
h
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AS a
n
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th
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r
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s
h
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ab
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2
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in
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m
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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I
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&
C
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4.
CO
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AND
DIS
C
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4
.
1
.
Acc
ura
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a
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T
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C
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A
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[
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ta
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f
GA
,
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,
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SO a
n
d
AC
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T
ec
h
n
iq
u
es:
G
A
(
s
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[
1
8
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A
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(
s)
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(
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[
19
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„
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M
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(
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(
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Co
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Ra
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t
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r
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(
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o
b
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I
n
Fig
u
r
e
6
w
e
p
r
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t o
b
tain
ed
r
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lt
s
f
o
r
t
h
e
alg
o
r
it
h
m
s
:
A
S,
MM
AS a
n
d
AC
S.
T
h
e
g
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d
c
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n
v
er
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n
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r
ith
m
s
.
W
e
ca
n
,
also
,
n
o
ti
ce
th
at
t
h
e
r
o
b
u
s
tn
e
s
s
o
f
th
e
MM
AS
al
g
o
r
ith
m
is
b
etter
t
h
an
t
h
e
r
o
b
u
s
tn
e
s
s
o
f
th
e
A
S
an
d
A
C
S
al
g
o
r
ith
m
s
;
in
f
ac
t
th
e
co
n
v
er
g
e
n
ce
r
ates
t
o
th
e
s
a
m
e
o
p
ti
m
al
v
al
u
e
ar
e
2
6
%,
4
9
%
an
d
1
9
%
r
esp
ec
tiv
el
y
f
o
r
A
S,
MM
A
S a
n
d
AC
S.
T
h
e
T
o
tal
E
r
r
o
r
(
T
E
)
v
alu
es
v
er
s
u
s
iter
atio
n
n
u
m
b
er
ar
e
p
lo
ted
in
F
ig
u
r
e
7
an
d
F
ig
u
r
e
8
f
o
r
th
e
A
S,
th
e
MM
A
S
a
n
d
th
e
A
C
S
alg
o
r
ith
m
s
f
o
r
lin
ea
r
v
al
u
es
an
d
E
1
9
2
s
er
ies,
r
esp
ec
tiv
el
y
.
Fro
m
t
h
ese
f
ig
u
r
es,
it
ca
n
b
e
s
ee
n
th
at
th
e
n
u
m
b
er
o
f
iter
atio
n
s
r
eq
u
ir
ed
to
ac
h
iev
e
th
e
q
u
alit
y
r
eq
u
ir
e
m
e
n
ts
ar
e
s
l
ig
h
tl
y
d
i
f
f
er
e
n
t
f
o
r
ea
ch
al
g
o
r
ith
m
.
I
n
f
ac
t
f
o
r
t
h
e
AS,
th
e
o
p
ti
m
al
v
al
u
e
o
f
th
e
T
E
is
r
ea
ch
ed
o
n
th
e
42
nd
iter
ati
o
n
,
f
o
r
th
e
MM
AS
it is
r
ea
ch
ed
o
n
th
e
2
5
3
rd
iter
atio
n
an
d
f
o
r
th
e
AC
S it i
s
r
ea
ch
ed
o
n
th
e
1
4
th
iter
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
2
2
7
–
2
3
5
234
W
e
n
o
tice
th
at
t
h
e
A
C
O
m
e
th
o
d
s
ar
e
f
aster
i
n
ter
m
o
f
t
h
e
n
u
m
b
er
o
f
i
ter
atio
n
s
to
a
ch
iev
e
t
h
e
o
p
tim
a
l
v
al
u
es
o
f
th
e
T
E
,
in
p
ar
ticu
lar
th
e
AC
S,
co
m
p
ar
ed
to
th
e
G
A
alg
o
r
it
h
m
a
n
d
AB
C
alg
o
r
ith
m
,
w
it
h
4
4
4
1
iter
atio
n
s
an
d
1
7
5
iter
atio
n
s
,
to
r
ea
ch
th
e
o
p
ti
m
a
l d
esig
n
r
esp
ec
tiv
el
y
[
1
8
]
.
Fig
u
r
e
6
.
B
o
x
p
lo
t f
o
r
th
e
co
n
v
er
g
e
n
ce
r
ate
f
o
r
T
E
Fig
u
r
e
7
.
T
E
v
alu
es
v
er
s
u
s
iter
atio
n
n
u
m
b
er
f
o
r
th
e
A
S,
AC
S,
an
d
MM
AS a
lg
o
r
it
h
m
s
(
lin
ea
r
v
al
u
es)
Fig
u
r
e
8
.
T
E
v
alu
es
v
er
s
u
s
iter
atio
n
n
u
m
b
er
f
o
r
th
e
A
S,
AC
S
,
an
d
MM
AS a
lg
o
r
it
h
m
s
(
E
1
9
2
s
er
ies)
5.
CO
NCLU
SI
O
N
W
e
p
r
esen
ted
in
th
is
p
ap
er
an
ap
p
licatio
n
o
f
th
e
t
h
r
ee
im
p
o
r
ta
n
t
v
ar
ia
n
ts
o
f
th
e
An
t
C
o
lo
n
y
Op
ti
m
izatio
n
tec
h
n
iq
u
e
f
o
r
o
p
ti
m
al
s
izi
n
g
o
f
a
s
tate
v
ar
iab
le
f
il
ter
.
SP
I
C
E
s
i
m
u
latio
n
co
n
f
i
r
m
s
th
e
v
al
id
it
y
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
s
.
T
h
e
AS
p
er
f
o
r
m
s
th
e
s
m
aller
T
o
tal
E
r
r
o
r
,
th
e
A
S
an
d
A
C
S
g
i
v
e
th
e
r
ap
id
co
n
v
er
g
en
ce
to
th
e
o
p
ti
m
al
v
al
u
es
an
d
th
e
MM
AS
p
r
o
v
id
e
a
b
etter
co
n
v
er
g
e
n
ce
r
ate.
T
h
e
co
m
p
ar
is
o
n
,
w
it
h
alr
ea
d
y
p
u
b
lis
h
ed
w
o
r
k
s
,
s
h
o
w
ed
th
a
t
th
e
AC
O
tec
h
n
iq
u
es
p
r
ese
n
t
al
ter
n
ati
v
e
a
n
d
co
m
p
eti
tiv
e
m
et
h
o
d
s
f
o
r
t
h
e
an
alo
g
f
ilter
d
esi
g
n
a
u
to
m
a
tio
n
an
d
o
p
ti
m
izat
io
n
.
RE
F
E
R
E
NC
E
S
[1
]
G
lo
v
e
r
F
.
“
Tab
u
s
e
a
rc
h
-
p
a
rt
II”
.
ORS
A
J
o
u
rn
a
l
o
n
c
o
m
p
u
ti
n
g
.
1
9
9
0
;
2
(
1
):
4
–
3
2
.
[2
]
G
ri
m
b
leb
y
J
B.
“
Au
t
o
ma
t
ic
a
n
a
lo
g
u
e
c
irc
u
i
t
sy
n
t
h
e
sis
u
si
n
g
g
e
n
e
ti
c
a
l
g
o
rit
h
ms
”
.
IE
EE
P
ro
c
e
e
d
in
g
s
-
Circu
it
s,
De
v
ice
s an
d
S
y
ste
m
s.
2
0
0
0
;
1
4
7
(
6
):
3
1
9
–
3
2
3
.
[3
]
A
a
rts
E
a
n
d
L
e
n
stra
K.
“
L
o
c
a
l
se
a
rc
h
in
c
o
m
b
in
a
to
rial
o
p
ti
m
iza
ti
o
n
”
.
Prin
c
e
to
n
:
Pri
n
c
e
to
n
Un
ive
rs
it
y
Pre
ss
.
2
0
0
3
.
[4
]
F
a
k
h
f
a
k
h
M
,
Bo
u
g
h
a
ri
o
u
M
,
S
a
ll
e
m
A
a
n
d
L
o
u
lo
u
M
.
“
De
sig
n
o
f
L
o
w
No
ise
Am
p
li
f
iers
th
ro
u
g
h
F
l
o
w
-
G
ra
p
h
s
a
n
d
th
e
ir
Op
t
im
iza
ti
o
n
b
y
th
e
S
im
u
late
d
A
n
n
e
a
li
n
g
T
e
c
h
n
iq
u
e
”
.
Bo
o
k
:
Ad
v
a
n
c
e
s
in
M
o
n
o
l
it
h
ic
M
icr
o
wa
v
e
In
teg
ra
ted
Circ
u
it
s fo
r W
ire
les
s S
y
ste
ms
:
M
o
d
e
li
n
g
a
n
d
De
sig
n
T
e
c
h
n
o
l
o
g
ies
,
IGI g
l
o
b
a
l
.
2
0
1
2
;
p
p
8
9
-
1
0
3
,
[5
]
Be
n
h
a
la
B,
A
h
a
it
o
u
f
A
,
Ko
tt
i
M
,
F
a
k
h
f
a
k
h
M
,
Be
n
lah
b
ib
B
,
M
e
c
h
e
q
ra
n
e
A
,
L
o
u
lo
u
M
,
A
b
d
i
F
a
n
d
A
b
a
rk
a
n
e
E.
“
A
p
p
li
c
a
ti
o
n
o
f
th
e
A
CO
T
e
c
h
n
iq
u
e
to
th
e
Op
ti
m
iza
ti
o
n
o
f
An
a
lo
g
Circu
it
P
e
rf
o
rm
a
n
c
e
s”
.
C
h
a
p
ter
9
,
Bo
o
k
:
0
1
2
x
1
0
-4
AS
M
M
A
S
A
C
S
T
o
t
a
l
E
r
r
o
r
(
T
E
)
0
200
400
600
800
1000
-5
,
0
x
1
0
-4
0
,
0
5
,
0
x
1
0
-4
1
,
0
x
1
0
-3
1
,
5
x
1
0
-3
2
,
0
x
1
0
-3
2
,
5
x
1
0
-3
3
,
0
x
1
0
-3
3
,
5
x
1
0
-3
4
,
0
x
1
0
-3
4
,
5
x
1
0
-3
5
,
0
x
1
0
-3
TE(To
t
al
Err
o
r
)
I
T
E
R
AT
I
ON
S
AS
AC
S
M
M
AS
0
200
400
600
800
1000
1
,
0
x
1
0
-3
1
,
5
x
1
0
-3
2
,
0
x
1
0
-3
2
,
5
x
1
0
-3
3
,
0
x
1
0
-3
3
,
5
x
1
0
-3
4
,
0
x
1
0
-3
4
,
5
x
1
0
-3
5
,
0
x
1
0
-3
5
,
5
x
1
0
-3
6
,
0
x
1
0
-3
TE(To
t
al
Err
o
r
)
I
TERATI
ON
S
AS
AC
S
M
M
AS
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
t Co
lo
n
y
Op
timiz
a
tio
n
fo
r
Op
tima
l
Lo
w
-
P
a
s
s
S
ta
te
V
a
r
ia
b
le
F
ilter
S
iz
in
g
(
K
r
itele
Lo
u
b
n
a
)
235
An
a
l
o
g
C
irc
u
it
s:
A
p
p
li
c
a
ti
o
n
s,
D
e
sig
n
a
n
d
Per
fo
rm
a
n
c
e
,
E
d
.
,
Dr
.
T
lelo
-
Cu
a
u
t
le,
NOVA
S
c
ie
n
c
e
Pu
b
li
sh
e
rs
.
2
0
1
1
;
p
p
.
2
3
5
–
2
5
5
.
[6
]
Be
n
h
a
la
B,
A
h
a
i
to
u
f
A
,
M
e
c
h
a
q
ra
n
e
A
,
Be
n
lah
b
ib
A
,
A
b
d
i
F
,
A
b
a
rk
a
n
E
a
n
d
F
a
k
h
f
a
k
h
M
.
“
S
izin
g
o
f
c
u
rre
n
t
c
o
n
v
e
y
o
rs
b
y
m
e
a
n
s
o
f
a
n
a
n
t
c
o
lo
n
y
o
p
ti
m
iza
ti
o
n
tec
h
n
i
q
u
e
”
.
T
h
e
IEE
E
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
M
u
lt
ime
d
ia
Co
mp
u
t
in
g
a
n
d
S
y
ste
ms
(
ICM
CS
'
1
1
)
.
2
0
1
1
;
p
p
.
8
9
9
–
9
0
4
;
O
u
a
rz
a
z
a
te,
M
o
ro
c
c
o
.
[7
]
Be
n
h
a
la
B,
A
h
a
it
o
u
f
A
,
M
e
c
h
a
q
ra
n
e
A
a
n
d
Be
n
lah
b
ib
B.
“
M
u
lt
io
b
jec
ti
v
e
o
p
ti
miza
ti
o
n
o
f
se
c
o
n
d
g
e
n
e
ra
ti
o
n
c
u
rr
e
n
t
c
o
n
v
e
y
o
rs
b
y
th
e
ACO
te
c
h
n
iq
u
e
”
.
T
h
e
In
tern
a
ti
o
n
a
l
Co
n
f
e
r
e
n
c
e
o
n
M
u
lt
im
e
d
ia
Co
m
p
u
ti
n
g
a
n
d
S
y
ste
m
s
(ICM
CS
'
1
2
).
2
0
1
2
;
p
p
.
1
1
4
7
–
1
1
5
1
;
T
a
n
g
ier,
M
o
r
o
c
c
o
.
[8
]
F
a
k
h
f
a
k
h
M
,
Co
o
re
n
Y,
S
a
ll
e
m
A
,
L
o
u
lo
u
M
,
a
n
d
S
iarry
P
.
“
A
n
a
lo
g
Circu
it
De
sig
n
Op
ti
m
iza
ti
o
n
th
r
o
u
g
h
th
e
P
a
rti
c
le
S
w
a
rm
Op
ti
m
iza
ti
o
n
T
e
c
h
n
iq
u
e
”
.
J
o
u
r
n
a
l
o
f
An
a
lo
g
I
n
t
e
g
ra
ted
Circ
u
i
ts
&
S
ig
n
a
l
Pr
o
c
e
ss
in
g
,
S
p
ri
n
g
e
r
.
2
0
1
0
;
6
3
(
1
):
7
1
–
8
2
.
[9
]
Ha
ij
ian
g
Wan
g
,
S
h
a
n
li
n
Ya
n
g
.
“
El
e
c
tri
c
it
y
Co
n
su
m
p
ti
o
n
P
r
e
d
ictio
n
Ba
se
d
o
n
S
V
R
w
it
h
A
n
t
Co
lo
n
y
Op
ti
m
iza
ti
o
n
”
.
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
ti
o
n
C
o
mp
u
ti
n
g
E
lec
tro
n
ics
a
n
d
C
o
n
tr
o
l
)
.
2
0
1
3
;
1
1
(1
1
):
[1
0
]
6
9
2
8
-
6
9
3
4
.
[1
1
]
Jin
h
u
i
Y,
X
iao
h
u
S
,
M
a
u
r
izio
M
,
Ya
n
c
h
u
n
L
.
“
A
n
a
n
t
c
o
lo
n
y
o
p
ti
m
i
z
a
ti
o
n
m
e
th
o
d
f
o
r
g
e
n
e
ra
li
z
e
d
T
S
P
p
ro
b
lem
”
.
Pro
g
re
ss
in
N
a
tu
r
a
l
S
c
ien
c
e
.
2
0
0
8
;
1
8
(1
1
):
1
4
1
7
-
1
4
2
2
.
[1
2
]
Ch
e
n
g
m
in
g
Qi
.
“
V
e
h
icle
Ro
u
ti
n
g
Op
ti
m
i
z
a
ti
o
n
in
L
o
g
isti
c
s
Dist
rib
u
ti
o
n
Us
i
n
g
H
y
b
rid
A
n
t
Co
lo
n
y
A
l
g
o
rit
h
m
”
.
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
t
io
n
C
o
mp
u
ti
n
g
E
lec
tro
n
ics
a
n
d
C
o
n
tro
l
)
.
2
0
1
3
;
1
1
(9
):
5
3
0
8
-
5
3
1
5
.
[1
3
]
L
e
n
in
Ka
n
a
g
a
s
a
b
a
i,
Ra
v
in
d
ra
n
a
th
Re
d
d
y
B
a
n
d
S
u
ry
a
Ka
l
a
v
a
th
i
M
.
“
Op
ti
m
a
l
P
o
w
e
r
F
lo
w
u
sin
g
A
n
t
Co
lo
n
y
S
e
a
rc
h
A
lg
o
rit
h
m
to
Ev
a
lu
a
te
L
o
a
d
Cu
rtailm
e
n
t
In
c
o
r
p
o
ra
ti
n
g
V
o
lt
a
g
e
S
tab
il
it
y
M
a
rg
in
Crit
e
rio
n
”
.
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
t
e
r E
n
g
i
n
e
e
rin
g
(
IJ
ECE
)
.
2
0
1
3
;
3
(
5
):
6
0
3
-
6
1
1
.
[1
4
]
L
M
De
C
a
m
p
o
s,
JM
F
e
rn
a
n
d
e
z
-
L
u
n
a
,
JA
G
á
m
e
z
,
JM.
P
u
e
rta.
“
A
n
t
c
o
lo
n
y
o
p
ti
m
iz
a
ti
o
n
f
o
r
lea
r
n
in
g
Ba
y
e
sia
n
n
e
tw
o
rk
s”
.
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
A
p
p
ro
x
im
a
te R
e
a
so
n
i
n
g
.
No
v
e
m
b
e
r
2
0
0
2
;
3
1
(3
):
2
9
1
-
3
1
1
,
[1
5
]
Do
rig
o
M
a
n
d
Krz
y
s
z
to
f
S
.
“
A
n
In
tro
d
u
c
ti
o
n
to
A
n
t
Co
lo
n
y
Op
ti
m
iza
ti
o
n
”
.
A
c
h
a
p
ter
in
A
p
p
ro
x
ima
ti
o
n
A
l
g
o
rit
h
m
s an
d
M
e
tah
e
u
risti
c
s,
a
b
o
o
k
e
d
it
e
d
b
y
T
.
F.
G
o
n
za
lez
.
2
0
0
6
.
[1
6
]
Do
rig
o
M
,
DiCa
ro
G
a
n
d
G
a
m
b
a
rd
e
ll
a
L
M
.
“
A
n
t
a
lg
o
rit
h
m
s
f
o
r
d
isc
re
te
o
p
ti
m
iz
a
ti
o
n
”
.
Arti
fi
c
ia
l
L
if
e
J
o
u
rn
a
l
.
1
9
9
9
;
5
:
1
3
7
-
1
7
2
.
[1
7
]
S
tü
tzle
T
,
Ho
o
s H
.
“
M
A
X
-
M
IN
A
n
t
S
y
ste
m
”
.
Fu
tu
re
Ge
n
e
ra
ti
o
n
Co
mp
u
ter
S
y
ste
m
.
2
0
0
0
;
1
6
(
9
):
8
8
9
-
9
1
4
.
[1
8
]
P
a
a
rm
a
n
L
D.
“
De
sig
n
a
n
d
A
n
a
l
y
s
is
o
f
A
n
a
lo
g
F
il
ters
”
.
No
rwe
ll
,
M
A:
Klu
we
r
.
2
0
0
7
.
[1
9
]
V
u
ra
l
RA
,
Yild
iri
m
T
,
Ka
d
io
g
lu
T
a
n
d
Ba
s
a
rg
a
n
A
.
“
P
e
rf
o
rm
a
n
c
e
Ev
a
lu
a
ti
o
n
o
f
Ev
o
lu
ti
o
n
a
ry
A
l
g
o
rit
h
m
s
f
o
r
Op
ti
m
a
l
F
il
ter De
sig
n
”
.
IEE
E
tra
n
sa
c
ti
o
n
s o
n
e
v
o
l
u
ti
o
n
a
ry
c
o
mp
u
t
a
ti
o
n
.
2
0
1
2
;
1
6
(1
):
1
3
5
-
1
4
7
.
[2
0
]
V
u
ra
l
RA
a
n
d
Yild
iri
m
T
.
“
Co
mp
o
n
e
n
t
v
a
lu
e
se
lec
ti
o
n
f
o
r
a
n
a
l
o
g
a
c
ti
v
e
fi
lt
e
r
u
sin
g
p
a
rticle
swa
r
m
o
p
ti
miz
a
ti
o
n
”
.
in
P
r
o
c
.
2
n
d
ICCA
E.
2
0
1
0
;
1
:
2
5
–
2
8
.
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