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[
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es
th
e
a
m
o
u
n
t
o
f
p
h
er
o
m
o
n
e
u
s
i
n
g
t
h
e
lo
ca
l
p
h
er
o
m
o
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e
u
p
d
a
tin
g
p
r
o
ce
d
u
r
e
d
escr
ib
ed
b
y
E
q
u
atio
n
(
3
)
.
On
ce
all
an
t
s
h
a
v
e
ter
m
i
n
ated
a
s
o
lu
tio
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u
n
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f
p
h
er
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m
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e
is
m
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d
i
f
ied
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
4
,
A
u
g
u
s
t
2
0
1
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1
6
1
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2
1
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8
2164
ag
ain
u
s
i
n
g
t
h
e
g
lo
b
al
p
h
er
o
m
o
n
e
u
p
d
atin
g
p
r
o
ce
d
u
r
e.
T
h
e
o
b
tain
ed
in
f
o
r
m
a
tio
n
f
r
o
m
t
h
e
f
ir
s
t
iter
atio
n
ar
e
u
s
ed
to
ca
lcu
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t
h
e
f
i
r
s
t i
n
p
u
ts
o
f
t
h
e
F
L
C
al
g
o
r
ith
m
,
an
d
s
o
o
n
.
2
.
1
.
F
uzzif
ica
t
io
n
T
h
e
Fu
zz
i
f
icatio
n
p
r
o
ce
s
s
is
t
h
e
f
ir
s
t
s
tep
in
t
h
e
FLC
s
y
s
te
m
,
w
h
ich
co
r
r
esp
o
n
d
s
to
tr
a
n
s
f
o
r
m
i
n
g
th
e
cr
is
p
v
al
u
es
in
to
f
u
zz
y
g
r
ad
es
u
s
in
g
t
h
e
m
e
m
b
er
s
h
ip
f
u
n
cti
o
n
s
.
T
h
is
p
r
o
ce
s
s
f
ac
i
litates
t
h
e
ap
p
licatio
n
o
f
th
e
r
u
le
s
et.
Fo
r
ea
ch
i
n
p
u
t
v
ar
iab
le,
w
e
d
ef
in
e
t
h
r
ee
m
e
m
b
er
s
h
i
p
f
u
n
ctio
n
s
(
MF)
,
to
d
ef
in
e
a
q
u
alitati
v
e
ca
teg
o
r
y
f
o
r
each
o
n
e
:
{
L
o
w
,
Me
d
iu
m
,
Hig
h
}.
I
n
p
r
ac
tice,
t
h
er
e
ar
e
v
ar
io
u
s
s
h
ap
es
o
f
m
e
m
b
er
s
h
i
p
f
u
n
ctio
n
s
t
h
at
ca
n
b
e
u
s
ed
in
th
e
f
u
z
zi
f
icatio
n
p
r
o
ce
s
s
,
f
o
r
ex
am
p
le:
T
r
ian
g
u
lar
MFs,
T
r
ap
ez
o
id
al
MFs,
Gau
s
s
ia
n
MFs,
Gen
er
alize
d
b
ell
MF
s
,
-
S
h
a
p
ed
Me
m
b
er
s
h
ip
F
u
n
ctio
n
,
S
-
Sh
ap
ed
Me
m
b
er
s
h
ip
F
u
n
ctio
n
[
1
5
]
.
I
n
th
i
s
w
o
r
k
,
w
e
u
s
ed
t
h
e
tr
ian
g
u
lar
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
t
h
at
ar
e
v
er
y
p
o
p
u
lar
,
ea
s
y
to
i
m
p
le
m
en
t,
an
d
r
esp
o
n
d
to
o
u
r
n
ee
d
s
.
T
h
e
in
p
u
t
v
ar
iab
les
w
it
h
th
eir
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
ar
e
illu
s
tr
ated
i
n
F
ig
u
r
e
3
an
d
Fig
u
r
e
4.
Fig
u
r
e
3
.
I
ter
atio
n
as in
p
u
t
v
ar
iab
le.
Fig
u
r
e
4
.
Div
er
s
it
y
as i
n
p
u
t
v
a
r
iab
le
Fig
u
r
e
3
ill
u
s
tr
ate
s
t
h
e
t
h
r
ee
t
r
ian
g
u
lar
m
e
m
b
er
s
h
ip
f
u
n
ct
io
n
s
o
f
t
h
e
iter
atio
n
i
n
p
u
t
v
ar
ia
b
le
w
i
th
a
r
an
g
e
f
r
o
m
0
to
1
.
I
n
Fig
u
r
e
4
th
e
th
r
ee
tr
ian
g
u
lar
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
o
f
th
e
Di
v
er
s
it
y
i
n
p
u
t
v
ar
iab
le
ar
e
s
h
o
w
n
w
i
th
a
r
an
g
e
f
r
o
m
0
to
1
.
L
et
x
th
e
cr
i
s
p
v
al
u
e
o
f
I
ter
atio
n
an
d
y
th
e
cr
is
p
v
al
u
e
o
f
Div
er
s
it
y
.
Fo
r
ea
ch
in
p
u
t
w
e
ca
lcu
la
te
th
e
d
eg
r
ee
o
f
m
e
m
b
er
s
h
ip
:
,
,
an
d
.
W
h
er
e,
L
o
w
=
[
0
,
0
.
5
]
,
Me
d
iu
m
=[
0
,
1
]
,
an
d
Hig
h
=[
0
.
5
,
1
]
2
.
2
.
Rule
E
v
a
lua
t
io
n
I
n
f
u
zz
y
lo
g
ic,
th
er
e
ar
e
v
ar
io
u
s
w
a
y
s
to
d
ef
i
n
e
a
f
u
zz
y
r
u
l
e.
I
n
d
ee
d
,
th
ey
ca
n
b
e
d
iv
id
ed
in
to
th
r
ee
m
ai
n
clas
s
es
:
t
h
e
f
u
zz
y
co
n
j
u
n
ctio
n
,
th
e
f
u
zz
y
d
is
j
u
n
ctio
n
,
an
d
th
e
f
u
zz
y
i
m
p
licatio
n
[
1
6
]
.
I
n
th
i
s
w
o
r
k
,
w
e
u
s
ed
a
Ma
m
d
an
i’
s
f
u
zz
y
co
n
j
u
n
ct
io
n
f
u
zz
y
r
u
le.
O
n
ce
t
h
e
v
ar
iab
les
a
n
d
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
ar
e
d
esi
g
n
ed
,
w
e
d
ef
i
n
e
t
h
e
r
u
le
b
ase
w
h
ich
is
co
m
p
o
s
ed
b
y
I
F
-
T
h
en
r
u
le
s
.
I
n
f
ac
t,
th
e
r
u
le
b
ase
is
d
ev
e
lo
p
ed
ac
co
r
d
in
g
to
s
o
m
e
k
n
o
w
led
g
e
ab
o
u
t t
h
e
AC
S a
l
g
o
r
ith
m
a
n
d
t
h
e
ch
o
s
e
n
m
etr
ics.
T
h
e
r
u
les (
F
i
g
u
r
e
5
)
o
f
t
h
e
p
r
o
p
o
s
ed
f
u
zz
y
s
y
s
te
m
w
it
h
I
ter
atio
n
an
d
Di
v
er
s
it
y
as i
n
p
u
t a
n
d
as o
u
tp
u
t
ar
e
as f
o
llo
w
s
:
I
f
(
I
t
e
r
a
t
i
o
n
i
s L
o
w
)
a
n
d
(
D
i
v
e
r
si
t
y
i
s
L
o
w
)
t
h
e
n
(
i
s
L
o
w
)
I
f
(
I
t
e
r
a
t
i
o
n
i
s L
o
w
)
a
n
d
(
D
i
v
e
r
si
t
y
i
s
M
e
d
i
u
m)
t
h
e
n
(
i
s
M
e
d
i
u
mL
o
w
)
I
f
(
I
t
e
r
a
t
i
o
n
i
s L
o
w
)
a
n
d
(
D
i
v
e
r
si
t
y
i
s
H
i
g
h
)
t
h
e
n
(
i
s
M
e
d
i
u
m)
I
f
(
I
t
e
r
a
t
i
o
n
i
s
M
e
d
i
u
m)
a
n
d
(
D
i
v
e
r
si
t
y
i
s
L
o
w
)
t
h
e
n
(
i
s
M
e
d
i
u
mL
o
w
)
I
f
(
I
t
e
r
a
t
i
o
n
i
s
M
e
d
i
u
m)
a
n
d
(
D
i
v
e
r
si
t
y
i
s M
e
d
i
u
m)
t
h
e
n
(
i
s
M
e
d
i
u
m)
I
f
(
I
t
e
r
a
t
i
o
n
i
s
M
e
d
i
u
m)
a
n
d
(
D
i
v
e
r
si
t
y
i
s Hig
h
)
t
h
e
n
(
i
s
M
e
d
i
u
mH
i
g
h
)
I
f
(
I
t
e
r
a
t
i
o
n
i
s Hig
h
)
a
n
d
(
D
i
v
e
r
si
t
y
i
s
L
o
w
)
t
h
e
n
(
i
s
M
e
d
i
u
m)
I
f
(
I
t
e
r
a
t
i
o
n
i
s Hig
h
)
a
n
d
(
D
i
v
e
r
si
t
y
i
s
M
e
d
i
u
m)
t
h
e
n
(
i
s
M
e
d
i
u
mH
i
g
h
)
I
f
(
I
t
e
r
a
t
i
o
n
i
s Hig
h
)
a
n
d
(
D
i
v
e
r
si
t
y
i
s
H
i
g
h
)
t
h
e
n
(
i
s Hig
h
)
Fig
u
r
e
5
.
IF
-
T
HE
N
r
u
les o
f
o
u
r
f
u
zz
y
s
y
s
te
m
T
o
ev
alu
ate
th
e
f
u
zz
y
r
u
les
w
e
u
s
ed
th
e
Mi
n
f
u
zz
y
s
et
o
p
er
atio
n
,
ass
u
m
i
n
g
t
h
at
w
e
ar
e
u
s
i
n
g
t
h
e
Ma
m
d
an
i
’
s
co
n
j
u
n
ct
io
n
o
p
er
ato
r
(
A
ND)
.
Fo
r
ea
ch
r
u
le
w
e
r
et
u
r
n
t
h
e
lo
w
est
v
al
u
e
f
r
o
m
t
h
e
ca
lc
u
lated
d
eg
r
ee
s
o
f
m
e
m
b
er
s
h
ip
o
f
th
e
t
w
o
i
n
p
u
t
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Th
e
E
ffect
o
f U
p
d
a
tin
g
th
e
Lo
ca
l P
h
ero
mo
n
e
o
n
A
C
S
P
erfo
r
ma
n
ce
u
s
in
g
F
u
z
z
y
Lo
g
ic
(
A
b
d
ella
tif
E
l
A
fia
)
2165
{
}
(
7
)
W
h
er
e,
i
is
the
in
d
e
x
o
f
the
r
ul
e
,
j
an
d
k
ar
e
in
d
e
x
es
f
o
r
t
h
e
f
u
zz
y
s
ets
{
L
o
w
,
Me
d
i
u
m
,
Hig
h
}
f
o
r
x
an
d
y
.
T
h
en
,
th
e
r
es
u
lt
s
o
f
all
r
u
les ar
e
s
u
m
m
ed
to
g
e
th
er
to
p
r
o
d
u
ce
a
s
et
o
f
f
u
zz
y
o
u
tp
u
ts
.
2
.
3
.
Def
uzzif
ica
t
io
n
Af
ter
t
h
e
ev
al
u
atio
n
o
f
t
h
e
r
u
les,
w
e
o
b
tain
a
f
u
zz
y
o
u
tp
u
t
th
at
’
s
n
ee
d
to
b
e
tr
an
s
f
o
r
m
ed
to
a
cr
i
s
p
v
alu
e
u
s
i
n
g
o
n
e
o
f
t
h
e
d
ef
u
z
zif
icatio
n
m
et
h
o
d
s
.
I
n
f
ac
t,
t
h
e
co
m
m
o
n
l
y
u
s
ed
tech
n
iq
u
es
f
o
r
d
e
f
u
zz
i
f
icat
io
n
ar
e:
Me
an
o
f
Ma
x
i
m
u
m
(
M
OM
)
m
e
th
o
d
,
C
en
ter
o
f
Gr
a
v
it
y
m
et
h
o
d
,
an
d
th
e
h
ei
g
h
t
m
e
th
o
d
[
1
6
]
,
[
17
]
.
Fig
u
r
e
6
s
h
o
w
s
th
e
p
ar
a
m
eter
as
o
u
tp
u
t
v
ar
iab
le,
w
i
th
a
r
an
g
e
f
r
o
m
0
to
1
,
an
d
g
r
an
u
lated
i
n
to
f
i
v
e
tr
ian
g
u
lar
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
.
W
h
er
e,
o
u
tp
u
t
s
et
is
:
S
= {
}.
Fig
u
r
e
6
.
as o
u
tp
u
t
v
ar
iab
le
I
n
o
r
d
er
t
o
o
b
tain
th
e
o
u
tp
u
t
v
ar
iab
le
in
cr
is
p
v
alu
e,
w
e
d
ef
u
zz
i
f
y
t
h
e
o
b
tain
ed
r
esu
l
t
s
f
r
o
m
t
h
e
in
f
er
en
ce
p
r
o
ce
s
s
u
s
in
g
t
h
e
C
e
n
ter
o
f
Gr
av
i
t
y
(
C
OG)
alg
o
r
it
h
m
d
escr
ib
ed
b
y
E
q
u
atio
n
8
:
∑
[
]
∑
[
]
(
8
)
W
h
er
e,
p
=9
is
th
e
n
u
m
b
er
o
f
o
u
tp
u
t
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
,
is
th
e
s
in
g
leto
n
o
f
o
u
tp
u
t
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
,
an
d
th
e
r
esu
lt o
f
al
l r
u
le
ev
al
u
atio
n
as s
h
o
w
n
i
n
T
ab
le
1
.
T
ab
le
1
.
T
h
e
s
in
g
le
to
n
o
f
t
h
e
o
u
tp
u
t
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
X
L
o
w
M
e
d
i
u
m
H
i
g
h
Y
L
o
w
=
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=
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=
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e
d
i
u
m
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=
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g
h
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=
5
/
6
W
h
er
e,
co
r
r
esp
o
n
d
s
to
L
o
w
m
e
m
b
er
s
h
ip
f
u
n
ct
io
n
o
f
t
h
e
o
u
tp
u
t,
co
r
r
esp
o
n
d
s
to
Me
d
iu
m
L
o
w
m
e
m
b
er
s
h
ip
f
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ctio
n
o
f
th
e
o
u
tp
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t,
co
r
r
esp
o
n
d
s
to
Me
d
iu
m
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
o
f
t
h
e
o
u
tp
u
t a
n
d
s
o
o
n
.
3.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
T
o
test
th
e
ef
f
ec
ti
v
e
n
es
s
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
,
w
e
co
m
p
ar
ed
it
to
th
e
s
tan
d
ar
d
AC
S
w
ith
a
s
e
t
o
f
b
en
ch
m
ar
k
T
SP
in
s
tan
ce
s
f
r
o
m
t
h
e
T
SP
L
I
B
[
1
8
].
T
a
b
le
2
tab
u
lates
t
h
e
s
ize
an
d
t
h
e
o
(
p
ti
m
al
to
u
r
len
g
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o
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ch
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s
tan
ce
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W
e
r
u
n
th
e
AC
S
alg
o
r
ith
m
w
it
h
th
e
f
o
llo
w
i
n
g
p
ar
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ter
s
:
=
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9
,
=
0
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1
an
d
m
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,
w
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h
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er
e
p
r
o
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est
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etti
n
g
p
ar
a
m
eter
s
f
o
r
AC
S
p
er
f
o
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m
an
ce
[
19
]
.
P
r
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r
a
m
m
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g
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y
Ma
t
lab
R
2
0
1
3
a,
th
e
in
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ta
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s
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ill b
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ti
m
es sep
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to
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: 1
0
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as
th
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m
ax
i
m
u
m
n
u
m
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er
o
f
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C
S
iter
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s
w
it
h
o
u
t
i
m
p
r
o
v
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n
g
.
T
h
e
in
itial
p
o
s
itio
n
o
f
an
ts
is
s
et
r
an
d
o
m
l
y
o
n
all
ex
p
er
i
m
e
n
ts
.
T
h
e
b
est co
m
p
ar
is
o
n
r
es
u
lt
s
ar
e
s
h
o
w
n
i
n
T
ab
le
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
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0
8
8
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I
J
E
C
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2
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1
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9
8
0
3
6
9
3
.
1
.
Co
m
pa
riso
n o
n t
he
So
lutio
n
A
cc
ura
cy
C
o
m
p
ar
in
g
th
e
r
u
n
n
in
g
o
f
AC
S
w
it
h
a
f
ix
ed
s
et
o
f
p
ar
a
m
eter
s
ag
ain
s
t
th
e
p
r
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p
o
s
ed
m
e
th
o
d
s
h
o
w
s
b
etter
r
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lts
f
o
r
b
o
th
m
i
n
i
m
u
m
a
n
d
av
er
a
g
e
len
g
t
h
in
m
o
s
t
o
f
th
e
i
n
s
tan
ce
s
,
esp
ec
iall
y
w
h
en
d
y
n
a
m
icall
y
ad
ap
tin
g
t
h
e
p
ar
am
e
ter
.
I
n
T
a
b
le
3
,
it
ca
n
also
b
e
o
b
s
er
v
ed
th
at
in
i
n
s
ta
n
ce
s
o
f
s
m
al
l
s
izes
th
e
b
est
f
o
u
n
d
s
o
lu
tio
n
s
f
o
r
th
e
t
w
o
al
g
o
r
it
h
m
s
ar
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al
m
o
s
t
t
h
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s
a
m
e
w
i
th
a
p
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iv
ile
g
e
in
t
h
e
av
er
a
g
e
s
o
lu
tio
n
s
f
o
r
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
.
B
u
t
in
la
r
g
e
s
ize
in
s
ta
n
ce
s
t
h
er
e
i
s
a
b
ig
d
i
f
f
er
e
n
ce
b
et
w
ee
n
t
h
e
b
es
t
s
o
lu
t
io
n
s
t
h
a
t
ar
e
f
o
u
n
d
f
o
r
ea
c
h
o
f
t
h
e
t
w
o
al
g
o
r
ith
m
s
.
T
h
u
s
,
it
ca
n
b
e
co
n
clu
d
ed
th
at
as
th
e
s
ize
o
f
p
r
o
b
lem
b
ec
o
m
e
s
lar
g
er
a
s
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ca
n
o
f
f
er
b
etter
r
es
u
lt
s
.
T
h
e
ex
p
er
i
m
en
tal
r
e
s
u
l
ts
r
e
f
lect
th
e
r
o
le
o
f
lear
n
i
n
g
to
p
r
o
v
id
e
th
e
b
est s
o
l
u
tio
n
s
.
T
ab
le
3
.
Su
m
m
ar
y
o
f
r
es
u
lt
s
u
s
in
g
AC
SF
L
al
g
o
r
ith
m
f
o
r
T
S
P
in
s
tan
ce
s
T
S
P
A
C
S
F
L
A
C
S
M
i
n
A
v
g
C
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t
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me
M
i
n
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me
a
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4
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2
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m
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n o
n t
he
Co
nv
er
g
ence
S
peed
I
t
ca
n
b
e
n
o
ted
f
r
o
m
t
h
e
T
ab
le
3
,
th
at
th
e
ti
m
e
o
f
f
in
d
i
n
g
t
h
e
b
est
le
n
g
th
f
o
r
t
h
e
p
r
o
p
o
s
ed
alg
o
r
it
h
m
w
h
e
n
ad
ap
tin
g
t
h
e
p
ar
a
m
eter
o
u
tp
er
f
o
r
m
s
b
o
th
t
h
e
co
n
v
e
n
tio
n
al
AC
S
a
n
d
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
h
e
n
v
ar
y
i
n
g
t
h
e
p
ar
a
m
e
ter
.
Ho
w
e
v
er
,
it
ca
n
b
e
s
ee
n
t
h
at
th
e
ti
m
e
o
f
f
i
n
d
in
g
t
h
e
s
a
m
e
b
est
l
en
g
t
h
is
les
s
i
n
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
h
e
n
ad
a
p
tin
g
t
h
e
p
ar
a
m
eter
t
h
a
n
th
e
t
w
o
o
th
er
s
.
T
h
e
F
i
g
u
r
es
7(
a
)
,
7(
b
)
,
7(
c
)
an
d
7
(
d
)
b
elo
w
s
h
o
w
t
h
e
r
esu
l
t
o
f
r
u
n
n
in
g
b
o
th
alg
o
r
it
h
m
s
o
n
f
o
u
r
ch
o
s
en
i
n
s
tan
ce
s
o
f
T
SP
b
en
ch
m
ar
k
s
w
h
ic
h
ar
e:
eil5
1
,
k
r
o
A
1
0
0
,
eil1
0
1
,
an
d
lin
1
0
5
.
T
h
e
F
ig
u
r
es
ac
t
u
all
y
g
o
i
n
l
in
e
w
it
h
t
h
e
s
e
o
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er
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atio
n
s
,
s
in
ce
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er
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ig
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i
f
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er
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s
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o
tice
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et
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ee
n
th
e
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o
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t
io
n
s
f
o
u
n
d
b
y
t
h
e
t
h
r
ee
alg
o
r
it
h
m
s
,
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
h
e
n
v
ar
y
i
n
g
p
ar
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eter
co
n
v
er
g
e
s
to
a
b
etter
s
o
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tio
n
t
h
a
n
t
h
e
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e
n
tio
n
al
o
n
e
an
d
t
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e
p
r
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p
o
s
ed
alg
o
r
ith
m
w
h
e
n
ad
ap
tin
g
th
e
p
ar
am
eter
i
n
all
th
e
f
i
g
u
r
es.
I
n
ad
d
itio
n
to
th
e
q
u
alit
y
o
f
s
o
lu
t
io
n
,
th
er
e
is
a
f
a
s
ter
co
n
v
er
g
e
n
ce
i
n
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
h
e
n
d
y
n
a
m
icall
y
ad
ap
tin
g
th
e
p
ar
a
m
e
t
er
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Th
e
E
ffect
o
f U
p
d
a
tin
g
th
e
Lo
ca
l P
h
ero
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ic
(
A
b
d
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tif
E
l
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2167
(
a)
(
b
)
(
c)
(
d
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Fig
u
r
e
7
.
(
a)
Sam
p
le
r
u
n
o
n
ei
l5
1
in
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RE
F
E
R
E
NC
E
S
[1
]
M.
Do
rig
o
,
L
.
M
.
G
a
m
b
a
rd
e
ll
a
,
“
A
n
t
Co
lo
n
y
S
y
ste
m
:
A
Co
o
p
e
ra
ti
v
e
L
e
a
rn
in
g
A
p
p
ro
a
c
h
to
t
h
e
T
ra
v
e
li
n
g
S
a
les
m
a
n
P
r
o
b
lem
”,
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Evo
lu
ti
o
n
a
ry
Co
mp
u
t
a
ti
o
n
,
1
(
1
)
,
5
3
-
6
6
,
1
9
9
7
.
[2
]
M.
Do
rig
o
,
C.
Bl
u
m
,
“
A
n
t
C
o
lo
n
y
Op
ti
m
iz
a
ti
o
n
T
h
e
o
ry
:
A
S
u
rv
e
y
”
,
T
h
e
o
re
ti
c
a
l
Co
mp
u
ter
S
c
ien
c
e
,
344
(
2
)
,
243
-
2
7
8
,
2
0
0
5
.
[3
]
M.
Do
rig
o
,
L
.
M
.
G
a
m
b
a
rd
e
ll
a
,
“
A
n
t
Co
lo
n
ies
f
o
r
t
h
e
T
ra
v
e
ll
in
g
S
a
les
m
a
n
P
ro
b
lem
”,
Bi
o
S
y
ste
ms
,
43
(
2
),
7
3
-
8
1
,
1
9
9
7
.
[4
]
H.
Yu
,
“
Op
t
im
ize
d
A
n
t
Co
lo
n
y
Alg
o
rit
h
m
b
y
L
o
c
a
l
P
h
e
r
o
m
o
n
e
Up
d
a
te
”,
Vo
l.
1
2
,
No
.
2
,
p
p
.
9
8
4
~
9
9
0
,
2
0
1
4
.
[5
]
F
.
X
iao
-
r
o
n
g
,
F
.
X
i
n
g
-
ji
e
,
“
A
D
y
n
a
m
ic
M
u
lt
i
-
n
e
st
A
n
t
C
o
l
o
n
y
A
l
g
o
rit
h
m
f
o
r
A
ircr
a
f
t
L
a
n
d
in
g
P
ro
b
lem
”,
T
EL
KOM
NIKA
In
d
o
n
e
si
a
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
,
V
o
l.
1
2
,
No
.
3
,
p
p
.
2
1
9
6
~
2
2
0
2
,
2
0
1
4
.
[6
]
M
.
Ja
f
a
ri,
H
.
Kh
o
tan
lo
u
,
“
A
Ro
u
ti
n
g
A
lg
o
ri
th
m
Ba
se
d
o
n
A
n
t
Co
lo
n
y
,
L
o
c
a
l
S
e
a
rc
h
a
n
d
F
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z
z
y
In
f
e
r
e
n
c
e
to
Im
p
ro
v
e
En
e
rg
y
Co
n
su
m
p
ti
o
n
in
W
irele
s
s,
S
e
n
so
r
Ne
tw
o
rk
s
”,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
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o
l.
3
,
No
.
5
,
p
p
.
6
4
0
~
6
5
0
,
2
0
1
3
.
[7
]
C
.
Qi,
“
V
e
h
icle
Ro
u
ti
n
g
Op
t
i
m
iz
a
t
io
n
in
L
o
g
isti
c
s
Distrib
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ti
o
n
u
sin
g
Hy
b
rid
A
n
t
Co
lo
n
y
A
lg
o
rit
h
m
”,
T
EL
KOM
NIKA
,
V
o
l.
1
1
,
N
o
.
9
,
p
p
.
5
3
0
8
~
5
3
1
5
,
2
0
1
3
.
[8
]
D.N.
L
e
,
“
P
S
O
a
n
d
A
CO
A
l
g
o
rit
h
m
s
A
p
p
li
e
d
to
Op
ti
m
iza
ti
o
n
Re
so
u
rc
e
A
ll
o
c
a
ti
o
n
to
S
u
p
p
o
rt
Qo
S
Re
q
u
irem
e
n
ts
in
NG
N
”,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
In
f
o
rm
a
ti
o
n
&
Ne
two
rk
S
e
c
u
rity (
IJ
INS
)
,
V
o
l.
2
,
No
.
3
,
J
u
n
e
2
0
1
3
,
p
p
.
2
1
6
~
2
2
8
.
[9
]
F.
A
h
m
a
d
i
z
a
r
,
H
.
S
o
l
t
a
n
p
a
n
a
h
,
“
R
e
l
i
a
b
i
l
i
ty
O
p
t
im
i
z
a
t
i
o
n
o
f
a
S
e
r
i
e
s
S
y
s
t
e
m
w
i
t
h
M
u
l
t
i
p
l
e
-
C
h
o
i
c
e
a
n
d
B
u
d
g
e
t
C
o
n
s
t
r
a
i
n
t
s
u
s
i
n
g
a
n
Ef
f
i
c
i
e
n
t
A
n
t
C
o
l
o
n
y
A
p
p
r
o
a
c
h
”,
E
x
p
e
r
t
S
y
s
te
ms
w
ith
A
p
p
l
i
c
a
t
i
o
n
s
,
38
(
4
)
,
3
6
4
0
-
3
6
4
6
,
2
0
1
1
.
[1
0
]
F
.
Oliv
a
s,
F.
V
a
ld
e
z
,
O.
Ca
stil
l
o
,
“
A
n
t
Co
lo
n
y
Op
ti
m
iza
ti
o
n
w
it
h
P
a
ra
m
e
ter
A
d
a
p
tatio
n
Us
i
n
g
F
u
z
z
y
L
o
g
ic
f
o
r
T
S
P
P
r
o
b
lem
s
”,
In
De
sig
n
o
f
I
n
telli
g
e
n
t
S
y
ste
ms
Ba
se
d
o
n
F
u
zz
y
L
o
g
ic,
Ne
u
ra
l
Ne
two
rk
s
a
n
d
N
a
tu
re
-
In
s
p
ire
d
Op
ti
miza
ti
o
n
(p
p
.
5
9
3
-
6
0
3
).
Sp
r
in
g
e
r
In
tern
a
ti
o
n
a
l
P
u
b
li
sh
in
g
,
2
0
1
5
.
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1
]
F
.
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[1
7
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M
.
R
.
I.
,
Ju
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,
T
.
(2
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[1
8
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G.
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,
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.
[1
9
]
T
.
S
t
ü
t
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le
e
t
a
l
.
,
“
P
a
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A
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n
”
,
I
n
A
u
t
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n
o
m
o
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s
s
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a
r
c
h
(
p
p
.
1
9
1
-
2
1
5
)
,
S
p
r
i
n
g
e
r
B
e
r
l
i
n
H
e
i
d
e
l
b
e
r
g
.
2011
.
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