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rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
14
,
No
.
6
,
Dec
em
b
er
20
24
,
p
p
.
6
7
4
3
~
6
7
5
2
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
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.
1
1
5
9
1
/ijece.
v
14
i
6
.
pp
6
7
4
3
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6
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5
2
6743
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CC B
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C
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Dh
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p
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Scien
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Srin
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as Un
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k
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Kar
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I
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E
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c
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I
NT
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th
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ase
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th
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[
1
]
.
T
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ase
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B
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tr
alize
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o
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in
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astru
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es
[
2
]
.
W
e
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u
s
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em
t
o
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b
s
er
v
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en
v
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en
tal
ev
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n
ts
,
h
u
m
an
ac
tiv
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,
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d
n
atu
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al
ca
lam
ities
[
3
]
.
W
SN’
s
k
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th
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r
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atch
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ata
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ir
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h
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in
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esti
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f
m
o
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ile
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o
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.
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v
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th
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n
etwo
r
k
h
as
a
p
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m
an
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m
m
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s
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d
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r
eq
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ir
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th
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o
f
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ter
m
ed
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o
d
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th
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W
SN,
p
r
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ter
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ased
co
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m
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n
icatio
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p
lay
a
cr
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r
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le
in
d
ec
r
ea
s
in
g
en
er
g
y
u
tili
za
tio
n
[
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
7
4
3
-
6
7
5
2
6744
T
o
r
e
d
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c
e
l
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[
5
]
.
M
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c
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d
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s
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t
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p
s
.
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a
c
h
c
l
u
s
t
e
r
s
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l
e
ct
s
a
c
l
u
s
t
e
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h
e
a
d
(
C
H
)
b
as
e
d
o
n
t
h
e
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n
e
r
g
y
b
e
t
w
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n
t
h
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c
l
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t
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r
'
s
f
o
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m
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r
n
o
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e
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w
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t
h
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n
a
s
p
ec
i
f
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t
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m
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f
r
a
m
e
.
C
H
g
a
t
h
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s
d
a
ta
f
r
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m
c
l
u
s
t
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r
n
o
d
es
a
n
d
s
e
n
d
s
i
t
ei
t
h
e
r
d
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r
e
ct
l
y
t
o
th
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n
k
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o
d
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o
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v
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a
C
H
t
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f
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t
c
l
u
s
t
e
r
s
i
n
t
h
e
n
e
t
w
o
r
k
[
6
]
.
I
n
t
h
e
p
a
s
t
,
t
h
e
r
e
h
a
v
e
b
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n
s
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v
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a
l
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g
p
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c
o
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d
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c
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e
d
i
n
r
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l
a
t
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o
n
t
o
W
SN
s
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c
u
r
it
y
a
n
d
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n
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g
y
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f
f
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c
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n
c
y
.
I
n
a
n
y
c
a
s
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,
i
t
i
s
c
r
u
ci
a
l
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d
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p
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r
i
o
r
r
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f
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a
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d
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g
y
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f
f
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c
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n
c
y
i
n
W
S
N
,
g
i
v
e
n
t
h
a
t
m
i
li
t
a
r
y
w
o
r
k
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s
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p
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a
t
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g
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n
a
c
c
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b
l
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a
d
d
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t
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a
l
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g
y
f
o
r
s
a
f
e
c
o
m
m
u
n
i
c
a
t
i
o
n
[
7
]
.
T
h
e
f
o
l
l
o
w
i
n
g
r
e
l
a
t
e
d
w
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r
k
a
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a
m
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s
t
h
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v
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r
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et
y
o
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p
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t
i
o
n
c
o
n
c
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r
n
s
a
n
d
t
h
e
i
r
v
a
lu
e
:
a
t
t
a
c
k
s
s
u
c
h
a
s
S
y
b
i
l
a
n
d
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k
h
o
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d
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d
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t
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r
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c
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h
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t
w
o
r
k
'
s
t
h
r
o
u
g
h
p
u
t
[
8
]
.
R
e
c
e
n
t
y
e
a
r
s
h
a
v
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w
i
t
n
es
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m
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f
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k
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p
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d
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b
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m
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t
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s
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w
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v
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b
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c
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m
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k
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m
m
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m
m
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p
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y
,
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b
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f
m
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t
h
o
d
d
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t
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r
m
i
n
a
t
i
o
n
[
9
]
.
O
n
e
u
n
i
q
u
e
a
d
v
a
n
ta
g
e
o
f
W
SN
is
t
h
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m
a
k
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p
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m
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d
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v
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d
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r
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l
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p
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a
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o
n
s
,
w
h
i
c
h
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n
c
r
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as
es
e
f
f
i
c
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n
c
y
,
e
f
f
e
ct
i
v
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n
es
s
,
a
n
d
s
a
f
e
t
y
.
W
S
Ns
a
r
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f
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to
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d
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s
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t
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n
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t
w
o
r
k
[
1
0
]
.
T
h
e
r
e
c
o
m
m
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n
d
e
d
m
e
t
h
o
d
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k
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h
a
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d
d
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d
a
b
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y
,
r
e
d
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c
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d
p
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u
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a
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d
b
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.
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w
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w
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l
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f
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n
d
l
o
w
e
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s
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p
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r
a
t
i
n
g
c
o
s
ts
[
1
1
]
.
T
h
e
m
o
s
t
i
m
p
o
r
ta
n
t
co
n
t
r
i
b
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ti
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o
f
th
is
p
a
p
er
is
e
v
o
l
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ti
o
n
a
r
y
al
g
o
r
it
h
m
b
ase
d
s
h
o
r
t
est
p
at
h
s
el
ec
t
io
n
in
W
SN.
E
ac
h
Se
n
s
o
r
n
o
d
e
’
s
r
o
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t
in
g
is
c
r
e
at
ed
b
y
t
h
e
e
v
o
lu
tio
n
a
r
y
al
g
o
r
it
h
m
(
E
A
)
,
wh
ic
h
t
h
e
n
f
in
d
s
a
g
r
o
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p
o
f
n
ei
g
h
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o
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o
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.
B
ec
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s
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f
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f
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n
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it
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,
E
A
c
r
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at
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d
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cts
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f
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g
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r
q
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ty
t
h
a
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o
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h
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ex
is
ti
n
g
alg
o
r
it
h
m
s
.
I
n
c
r
e
ase
i
n
t
h
r
o
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g
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p
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a
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d
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y
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f
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wa
s
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n
ab
le
d
wi
th
th
e
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v
a
l
u
ati
o
n
d
o
n
e
b
y
th
is
o
p
t
im
al
n
o
d
e
b
as
ed
s
h
o
r
tes
t
p
at
h
i
d
e
n
t
if
i
ca
t
io
n
f
o
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a
ir
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h
o
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g
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h
e
p
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th
f
r
o
m
s
o
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r
c
e
t
o
d
est
in
ati
o
n
.
T
h
is
m
et
h
o
d
co
n
t
r
i
b
u
tes
t
o
t
h
e
n
etw
o
r
k
’
s
m
a
x
i
m
u
m
s
t
ab
ilit
y
b
y
r
e
d
u
ci
n
g
d
e
la
y
a
n
d
e
n
er
g
y
u
s
a
g
e
[
1
2
]
.
I
n
s
tu
d
y
[
1
3
]
,
an
ef
f
icien
t
m
eta
-
h
eu
r
is
tic
in
th
e
f
o
r
m
o
f
a
g
en
etic
alg
o
r
ith
m
is
d
is
cu
s
s
ed
,
th
at
o
v
er
co
m
es
a
n
u
m
b
er
o
f
f
au
lts
o
f
p
r
ev
io
u
s
m
eta
-
h
e
u
r
is
tics
,
a
lo
n
g
with
an
ac
cu
r
ate
m
eth
o
d
f
o
r
ca
lcu
latin
g
th
e
f
itn
ess
f
u
n
ctio
n
.
J
ain
et
a
l.
[
1
4
]
c
o
n
d
u
cted
a
n
i
n
v
esti
g
atio
n
in
to
en
e
r
g
y
m
an
a
g
em
en
t
f
r
o
m
th
e
p
er
s
p
ec
tiv
e
o
f
wir
eless
s
en
s
o
r
n
etwo
r
k
s
.
I
n
s
tu
d
y
[
1
5
]
,
th
e
en
er
g
y
-
awa
r
e
r
o
u
tin
g
(
E
AR
)
p
r
o
to
co
l
esti
m
ates
th
e
co
n
n
ec
tio
n
q
u
ality
o
f
s
en
s
o
r
n
o
d
es,
aim
in
g
to
cu
t d
o
wn
o
n
en
e
r
g
y
co
n
s
u
m
p
tio
n
an
d
ch
o
o
s
e
a
p
r
ef
e
r
r
e
d
n
ex
t h
o
p
.
I
n
s
t
u
d
y
[
1
6
]
,
t
h
e
e
f
f
e
c
t
i
v
e
n
es
s
i
s
q
u
an
t
i
f
i
e
d
f
o
r
d
y
n
a
m
i
c
s
c
a
li
n
g
a
n
d
m
o
d
u
l
a
t
i
o
n
s
c
h
e
m
e
.
E
a
c
h
n
o
d
e
i
n
[
1
7
]
e
n
e
r
g
y
e
f
f
i
c
i
e
n
t
wi
r
e
l
es
s
s
e
n
s
o
r
n
e
tw
o
r
k
h
a
s
o
n
l
y
t
h
e
a
b
i
l
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t
y
t
o
t
r
a
n
s
m
i
t
d
i
r
e
c
tl
y
a
s
s
t
r
i
ct
r
e
s
t
r
i
c
t
e
d
n
u
m
b
e
r
o
f
t
h
e
n
o
d
e
s
.
T
h
e
l
i
m
i
t
e
d
t
r
a
n
s
m
is
s
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o
n
r
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g
e
i
s
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m
p
l
ie
d
b
y
t
h
e
s
c
a
r
c
e
r
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o
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r
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s
f
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Fig
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atio
n
,
m
ak
e
u
p
th
e
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
.
Her
e,
5
0
lin
k
s
an
d
2
5
n
o
d
e
s
ar
e
m
ea
s
u
r
ed
with
2
5
ch
r
o
m
o
s
o
m
es a
n
d
5
0
lin
k
s
ar
b
itra
r
ily
h
it th
e
alg
o
r
ith
m
to
o
f
f
er
th
e
s
u
b
s
eq
u
en
t c
o
s
t m
atr
ix
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
7
4
3
-
6
7
5
2
6746
a.
T
h
e
s
tep
s
in
v
o
lv
ed
−
Select
th
e
ty
p
e
o
f
e
n
co
d
in
g
−
C
h
o
o
s
e
p
o
p
u
latio
n
s
ize
−
R
an
d
o
m
ly
ch
o
o
s
e
in
itial p
o
p
u
latio
n
−
Select
p
ar
tial c
h
r
o
m
o
s
o
m
es
−
C
r
o
s
s
o
v
er
an
d
m
u
tatio
n
−
Of
f
s
p
r
in
g
−
R
ec
o
m
b
in
atio
n
B
ased
o
n
th
e
p
r
esen
t
p
o
s
s
ess
i
o
n
s
r
elate
d
to
th
e
p
r
o
b
ab
ilis
tic
m
o
d
el
th
e
b
est
s
o
lu
tio
n
is
o
b
tain
ed
.
T
h
e
s
elec
tio
n
p
r
o
p
e
r
ty
will
ex
am
i
n
e
th
e
f
u
n
d
am
e
n
tal
b
ased
o
n
f
itn
ess
.
T
h
is
will
ca
u
s
e
o
f
f
s
p
r
in
g
to
ap
p
ea
r
in
T
ab
le
1
f
o
r
r
ep
r
o
d
u
ctio
n
,
a
h
i
g
h
er
f
itn
ess
v
alu
e
is
r
e
g
u
lar
ly
m
ea
s
u
r
ed
.
C
r
o
s
s
o
v
er
r
esear
ch
r
elev
a
n
t
ap
p
r
o
ac
h
am
o
n
g
th
e
s
ets
tak
en
in
to
co
n
s
id
er
atio
n
.
I
f
m
o
r
e
th
an
o
n
e
attr
ib
u
te
co
n
tr
ib
u
tes
to
th
e
f
r
eq
u
en
t
ch
ar
ac
ter
,
th
en
th
e
attr
ib
u
te
will b
e
test
ed
f
o
r
cr
o
s
s
o
v
er
p
r
o
p
er
ty
an
d
ta
k
en
i
n
to
co
n
s
id
er
atio
n
.
T
ab
le
1
.
C
r
o
s
s
o
v
er
N
o
d
e
N
5
N
o
d
e
N
12
O
f
f
sp
r
i
n
g
O
f
f
sp
r
i
n
g
0
0
1
0
1
0
1
1
0
0
N
o
d
e
N
4
N
o
d
e
13
C
r
o
ss
o
v
e
r
r
a
n
d
o
m
g
e
n
e
r
a
t
i
o
n
0
0
1
0
0
0
1
1
0
1
E
v
er
y
n
o
d
e
in
a
m
ea
s
u
r
ed
n
etwo
r
k
o
r
g
an
ize
f
ig
u
r
e
is
r
an
d
o
m
ly
p
o
s
itio
n
ed
,
an
d
th
e
d
y
n
am
ic
o
p
tim
u
m
p
ath
is
ex
am
in
e
d
u
s
in
g
th
e
c
u
r
r
en
t
m
eth
o
d
o
lo
g
y
.
T
h
e
alg
o
r
ith
m
g
e
n
er
ates
b
o
th
th
e
wo
r
s
t
an
d
b
est
o
u
tco
m
es
in
ea
ch
c
y
cle
with
t
h
e
p
o
o
r
est
r
esu
lts
b
ein
g
d
is
ca
r
d
ed
.
E
v
er
y
r
o
u
n
d
o
f
alg
o
r
ith
m
p
r
o
v
id
es
th
e
e
n
tire
wo
r
s
t
an
d
b
est
r
esu
lts
,
t
h
e
wo
r
s
t
r
esu
lts
is
ca
lled
o
f
f
.
T
h
e
m
o
s
t
ex
ce
llen
t
o
n
es
f
o
r
ea
ch
n
o
d
e
elem
e
n
t
ex
am
in
ed
in
T
ab
le
2
is
n
o
w
ap
p
lied
to
a
r
a
n
d
o
m
s
am
p
le
o
f
n
o
d
es.
T
ab
le
2
.
C
o
s
t e
s
tim
atio
n
N
o
d
e
1
3
4
8
9
12
17
18
22
25
BCE
0
0
0
0
1
0
0
0
1
1
0
0
1
0
0
0
1
0
0
0
0
1
0
0
1
0
1
1
0
0
1
0
0
0
1
1
0
0
1
0
1
0
1
1
0
1
1
0
0
1
C
o
s
t
24
21
43
45
38
36
85
15
23
b.
Fit
n
ess
f
u
n
ctio
n
An
e
v
o
l
u
tio
n
ar
y
illu
s
tr
atio
n
tech
n
iq
u
e
u
tili
ze
d
to
ac
q
u
i
r
e
th
e
o
b
jectiv
e
f
u
n
ctio
n
with
ar
b
itra
r
y
v
ar
iab
les
o
f
n
u
m
er
als.
B
ased
o
n
th
ese
p
er
s
o
n
s
ar
e
ca
lcu
late
an
d
ag
r
ee
d
f
o
r
o
p
tim
i
za
tio
n
.
B
elo
w
is
a
r
ep
r
esen
tatio
n
o
f
th
e
o
p
tim
al
f
itn
ess
f
u
n
ctio
n
u
s
ed
f
o
r
esti
m
a
te
,
b
est
f
itn
ess
f
u
n
ctio
n
.
=
∑
(
,
+
)
−
=
(
3
)
At
f
ir
s
t,
th
er
e
is
n
o
leg
itima
te
f
lo
w.
I
n
th
e
f
o
llo
win
g
iter
ativ
e
cr
ea
tio
n
alo
n
g
with
o
b
jecti
v
e
f
u
n
ctio
n
s
,
wh
er
e
s
tan
d
s
f
o
r
ar
b
itra
r
y
cr
ea
tio
n
n
u
m
b
er
s
.
(
∙
)
=
(
)
+
(
4
)
wh
er
e
(
)
is
o
b
jectiv
e
f
u
n
ctio
n
a
n
d
is
p
en
alty
.
T
h
is
is
th
e
m
ain
s
tep
wh
ile
p
r
im
ar
y
p
o
p
u
latio
n
is
cr
ea
ted
.
2
.
3
.
G
ener
a
lizing
decisi
o
n m
o
del
T
h
e
f
u
n
d
am
e
n
tals
o
f
an
o
b
j
ec
tiv
e
f
u
n
ctio
n
ar
e
d
esig
n
ed
to
o
p
tim
ize
f
o
r
th
e
b
est
p
o
s
s
ib
le.
T
h
e
o
p
tim
izatio
n
its
elf
h
as
ch
ar
ac
ter
is
tics
l
ik
e
m
u
tatio
n
an
d
cr
o
s
s
o
v
er
in
th
e
s
elec
tio
n
r
o
u
lette
wh
ee
l.
T
h
e
n
o
d
es
in
th
is
n
etwo
r
k
,
wh
ich
a
r
e
d
is
tr
ib
u
ted
at
r
a
n
d
o
m
,
ar
e
r
e
g
ar
d
e
d
as
a
p
o
p
u
latio
n
.
T
h
e
g
o
al
f
u
n
ctio
n
is
ac
tu
alize
d
to
p
r
o
v
id
e
ass
ess
ed
r
o
u
te
f
in
d
in
g
in
lig
h
t
o
f
th
e
h
eu
r
is
tic
m
o
d
els.
I
t
will
g
o
th
r
o
u
g
h
N
iter
atio
n
s
u
n
til
eith
er
th
e
r
o
u
te
is
f
o
u
n
d
o
r
e
v
er
y
c
o
n
ce
iv
ab
le
in
p
u
t
is
lin
k
e
d
to
t
h
e
p
r
o
b
ab
ilis
tic
ap
p
r
o
ac
h
,
wh
ic
h
ev
er
c
o
m
es
f
ir
s
t.
B
ased
o
n
th
e
r
an
d
o
m
in
p
u
t,
ev
er
y
cr
ea
tiv
e
o
u
tc
o
m
e
th
e
b
est
r
esu
lt
in
Fig
u
r
e
2
.
I
llu
s
tr
ates
h
o
w
th
e
wo
r
s
t
o
p
tio
n
s
ar
e
elim
in
ated
f
r
o
m
co
n
s
id
er
atio
n
wh
ile
th
e
g
r
ea
test
o
u
tco
m
e
is
s
im
p
lifie
d
an
d
wid
ely
ac
ce
p
ted
.
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:
2088
-
8
7
0
8
Op
tima
l sh
o
r
test
p
a
th
s
elec
ti
o
n
u
s
in
g
a
n
ev
o
lu
tio
n
a
r
y
…
(
Dh
a
mo
d
h
a
r
a
n
Ud
a
y
a
S
u
r
iya
R
a
jku
ma
r
)
6747
Ass
u
m
e
th
at
th
e
f
ir
s
t
-
g
en
er
ati
o
n
ca
n
d
i
d
ate
s
o
lu
tio
n
f
o
r
n
o
d
e
N
5
is
r
ep
r
esen
ted
as 0
0
1
0
1
a
n
d
n
o
d
e
N
12
ca
n
b
e
r
ep
r
esen
ted
as
0
1
1
0
0
.
T
h
e
p
r
o
b
ab
ilis
tic
m
o
d
els
r
ec
eiv
e
in
f
o
r
m
atio
n
f
r
o
m
ea
ch
g
en
er
atio
n
th
at
h
as
b
ee
n
b
o
r
n
in
o
r
d
er
to
p
r
o
v
id
e
t
h
e
r
esu
lt.
Her
e
af
ter
th
e
o
f
f
s
p
r
in
g
,
it wa
s
N
5
is
co
n
v
er
ted
in
to
N
4
r
ep
r
esen
ted
as
0
0
1
0
0
an
d
N
12
h
as
N
13
r
ep
r
ese
n
ted
as 0
1
1
0
1
th
e
cr
o
s
s
o
v
er
d
etail
is
d
ep
icted
in
T
ab
le
1
.
T
h
e
ch
r
o
m
o
s
o
m
al
o
u
tp
u
t
is
a
d
ju
s
ted
wh
en
co
n
s
id
er
in
g
th
e
p
ath
af
ter
r
an
d
o
m
g
en
er
ati
o
n
,
wh
er
e
th
e
b
eg
in
n
in
g
p
o
p
u
latio
n
is
tak
en
in
to
ac
co
u
n
t
with
a
m
in
o
r
v
al
u
e
o
f
1
to
a
n
u
tm
o
s
t
v
alu
e
o
f
2
5
.
Mu
tatio
n
lim
its
th
e
s
o
lu
tio
n
s
th
at
a
r
e
s
ettin
g
f
o
r
lo
ca
l
o
p
tim
al.
T
h
e
B
C
D
v
alu
e
o
f
n
o
d
e
1
is
co
n
s
id
er
ed
as
0
0
0
0
1
,
n
o
d
e
3
as
0
0
0
1
1
an
d
co
s
t w
eig
h
t is 2
4
li
k
e
th
at
f
o
r
n
o
d
e
2
5
th
e
co
s
t w
eig
h
t is 2
3
th
at
is
d
is
p
lay
ed
i
n
T
ab
le
2
.
Th
eo
r
em
-
1
:
L
et
r
ep
r
esen
t
th
e
p
o
s
itio
n
o
f
g
en
e
r
atio
n
s
.
I
f
g
e
n
er
atio
n
’
d
ep
en
d
s
o
n
{
}
in
s
u
ch
a
way
th
at
ev
er
y
n
o
d
e
in
s
tates ‘
γ
’
th
en
w
e
ca
n
d
ec
o
m
p
o
s
e
in
,
s
u
ch
|
γ
|
≥
’
,
we
ca
n
s
p
lit ‘
γ
′
in
to
:
a.
γ
≠
(
)
|
γ
|
>
0
(
o
r
)
|
γ
|
≥
1
.
b.
|
αβ
|
≤
’
c.
T
o
tal
≥
0
,
th
e
n
etwo
r
k
n
o
d
e
αβ
z
j
wh
er
e
,
an
d
r
ep
r
esen
t
th
e
n
o
d
es
in
th
e
n
etwo
r
k
,
is
th
e
b
est
p
ath
b
etwe
en
p
o
s
s
ib
le
r
o
u
tes
am
o
n
g
α
an
d
β
.
T
h
e
co
s
t
m
atr
ix
th
at
was
tak
en
in
to
ac
co
u
n
t
f
o
r
th
e
p
o
p
u
latio
n
.
E
ac
h
n
o
d
e
in
th
e
n
et
wo
r
k
h
as
a
weig
h
t
th
at
co
n
n
ec
ts
it
to
th
e
o
t
h
er
n
o
d
es.
E
v
er
y
n
o
d
e
u
n
iq
u
en
ess
is
r
u
n
th
r
o
u
g
h
th
e
f
itn
ess
f
u
n
ctio
n
tech
n
iq
u
e
f
o
r
ea
ch
p
o
in
t
to
g
et
an
ac
cu
r
at
e
esti
m
atio
n
.
T
h
e
s
ize
p
r
o
b
ab
ilit
y
v
ec
to
r
is
s
p
ec
if
ied
as
,
P
i
⇒
Pr
ob
(
P
i
,
L
)
,
wi
n
=
1
L
e
an
d
L
ose
=
1
L
P
.
T
h
e
s
o
lu
tio
n
is
p
r
esen
t
ed
in
T
ab
le
3
,
wh
ic
h
is
co
m
p
ac
t
g
en
etic
alg
o
r
ith
m
.
Fro
m
T
ab
le
3
b
ased
o
n
r
an
d
o
m
g
e
n
er
atio
n
th
e
ac
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ated
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e
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o
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y
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ig
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est
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i.e
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o
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.
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h
is
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r
esen
ted
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n
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ab
le
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.
Fig
u
r
e
2
.
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v
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tio
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o
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m
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o
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r
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n
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d
iv
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als
T
ab
le
3
.
Step
1
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lcu
latio
n
o
f
ev
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tio
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ar
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alg
o
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ith
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T
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et
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am
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d
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p
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e
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io
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s
tab
le
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g
iv
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o
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N
∑
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n
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(
M
i
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k
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e,
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i
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E
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tr
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s
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ased
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r
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m
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s
ix
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ar
e
elim
in
atin
g
t
h
e
s
tr
in
g
co
u
n
t a
f
ter
th
at
we
ca
n
g
et
th
e
ex
p
ec
ted
c
o
u
n
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
0
8
8
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0
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I
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t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
14
,
No
.
6
,
Dec
em
b
e
r
20
24
:
6
7
4
3
-
6
7
5
2
6748
T
ab
le
4
.
Step
2
ca
lcu
latio
n
o
f
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
S
t
r
i
n
g
N
O
P
o
si
t
i
o
n
X
R
a
n
d
o
m
c
r
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o
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e
r
F
(
x
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3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
s
h
o
r
test
p
at
h
alg
o
r
ith
m
cl
ea
r
ly
d
em
o
n
s
tr
ates
u
s
in
g
2
5
n
o
d
es
an
d
5
0
lin
k
s
a
r
e
tak
e
n
in
t
o
ac
co
u
n
t.
T
h
e
im
p
lem
en
tatio
n
is
ca
r
r
ie
d
o
u
t
u
s
in
g
a
NS2
an
d
th
e
r
esu
lts
ar
e
co
m
p
ar
in
g
with
p
r
ev
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u
s
s
h
o
r
test
p
ath
m
eth
o
d
s
with
p
r
o
p
o
s
ed
ev
o
lu
t
io
n
ar
y
alg
o
r
ith
m
a
n
d
p
ar
a
m
et
er
s
u
s
ed
.
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m
er
o
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s
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cr
ea
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n
s
m
ea
s
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r
ed
n
o
w
is
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tm
o
s
t
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d
s
in
g
le
esti
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ate
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m
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r
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er
y
n
o
d
e
in
all
il
lu
s
tr
atio
n
is
s
h
o
wn
in
Fig
u
r
e
3
.
T
ab
le
5
co
m
p
a
r
es
in
d
iv
id
u
al
g
en
er
atio
n
co
s
t
an
d
r
o
u
te
co
s
t.
R
esu
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ar
e
co
m
p
a
r
ed
ag
ain
s
t
th
e
v
ec
t
o
r
s
ize’
s
win
n
in
g
p
r
o
b
a
b
ilit
y
.
T
h
e
co
m
p
u
tin
g
tim
e
is
th
e
a
m
o
u
n
t
o
f
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e
r
e
q
u
ir
ed
f
o
r
ea
ch
d
ata
tr
an
s
f
er
af
ter
p
ath
in
v
esti
g
atio
n
.
T
h
e
Pro
p
o
s
ed
m
et
h
o
d
is
co
m
p
ar
ed
with
ex
is
tin
g
m
eth
o
d
2
5
n
o
d
es
ar
e
tak
en
in
to
co
n
s
id
er
atio
n
.
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ac
h
iter
atio
n
o
f
th
e
r
o
u
te
d
is
co
v
e
r
y
p
r
o
ce
s
s
is
test
ed
f
o
r
f
ewe
r
th
an
5
0
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en
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atio
n
s
,
with
a
d
ir
ec
t
lin
k
b
e
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n
2
5
n
o
d
es
5
0
ed
g
es.
T
h
e
two
way
s
f
o
r
f
in
d
i
n
g
th
e
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est
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d
wo
r
s
t
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ath
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e
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m
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ar
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d
it
is
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is
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v
er
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at
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e
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m
p
ac
t
E
v
o
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tio
n
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r
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g
o
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ith
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ap
p
r
o
ac
h
p
r
o
d
u
ce
s
th
e
b
est
r
esu
lt.
Fig
u
r
es
3
an
d
4
s
h
o
w
t
h
e
co
m
p
ar
is
o
n
o
f
co
m
p
u
tatio
n
al
tim
e.
Fo
r
5
n
o
d
es,
th
e
tim
e
tak
en
to
p
r
o
ce
s
s
th
e
d
ata
in
Dijk
s
tr
a’
s
alg
o
r
ith
m
ca
n
tak
e
6
m
s
,
in
th
e
g
en
etic
alg
o
r
ith
m
,
it
ca
n
tak
e
5
m
s
;
a
n
d
i
n
th
e
p
r
o
p
o
s
ed
ev
o
l
u
tio
n
ar
y
alg
o
r
ith
m
,
it
ca
n
tak
e
2
m
s
.
lik
ewise,
we
ca
n
co
m
p
ar
e
with
1
0
,
1
5
,
2
0
,
an
d
2
5
n
o
d
es.
T
h
e
co
m
p
u
tatio
n
al
tim
e
f
o
r
2
5
n
o
d
es
was
1
0
m
s
in
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
wh
er
e
as
ex
is
tin
g
m
eth
o
d
s
it
tak
es
1
9
a
n
d
2
5
m
s
,
r
esp
ec
tiv
ely
.
I
n
Fig
u
r
e
4
,
th
e
co
m
p
u
tatio
n
al
tim
e
f
o
r
5
0
n
o
d
es wa
s
1
5
m
s
in
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
wh
er
ea
s
in
th
e
ex
is
tin
g
m
eth
o
d
,
ca
n
tak
e
2
2
an
d
1
8
m
s
.
Fo
r
2
5
0
n
o
d
es
,
th
e
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
c
an
tak
e
3
6
m
s
,
a
s
ig
n
if
ican
tly
s
h
o
r
ter
tim
e
th
an
ex
is
tin
g
m
eth
o
d
s
4
8
an
d
4
2
m
s
.
T
h
e
g
r
ap
h
b
elo
w
illu
s
tr
ates
th
e
co
m
p
ar
is
o
n
o
f
r
o
u
te
f
ailu
r
es.
T
ab
le
5
.
B
est f
itn
ess
alg
o
r
ith
m
f
o
r
d
is
co
v
er
y
o
f
b
est p
ath
R
o
u
t
e
B
e
st
f
i
t
n
e
ss
v
a
l
u
e
B
e
st
p
a
t
h
P
a
t
h
-
1
0
.
1
0
9
5
1
-
3
-
4
-
8
-
9
-
12
-
17
-
18
-
22
-
25
P
a
t
h
-
2
0
.
7
9
6
9
1
-
5
-
6
-
11
-
10
-
14
-
19
-
20
-
21
-
22
-
18
-
25
P
a
t
h
-
3
0
.
1
6
3
6
1
-
2
-
6
-
10
-
11
-
7
-
8
-
9
-
12
-
17
-
18
-
22
-
25
Fig
u
r
e
3
.
C
o
m
p
a
r
is
o
n
o
f
co
m
p
u
tatio
n
al
tim
e
with
p
r
o
p
o
s
ed
m
eth
o
d
s
u
s
in
g
2
5
n
o
d
es
Fig
u
r
e
4
.
C
o
m
p
a
r
is
o
n
o
f
co
m
p
u
tatio
n
al
tim
e
with
p
r
o
p
o
s
ed
m
eth
o
d
s
u
s
in
g
2
5
0
n
o
d
es
I
n
Fig
u
r
e
4
,
th
e
co
m
p
u
tatio
n
al
tim
e
f
o
r
5
0
n
o
d
es
was
1
5
m
s
in
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
wh
e
r
ea
s
in
th
e
ex
is
tin
g
m
eth
o
d
,
ca
n
tak
e
2
2
an
d
1
8
m
s
.
Fo
r
2
5
0
n
o
d
es,
th
e
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
ca
n
tak
e
3
6
m
s
,
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
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n
g
I
SS
N:
2088
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r
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ates
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m
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in
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u
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g
m
eth
o
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o
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tio
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o
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ith
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%.
L
ik
ewise,
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n
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m
p
ar
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with
1
0
,
1
5
,
2
0
,
an
d
2
5
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o
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es.
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u
r
e
5
.
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o
m
p
a
r
is
o
n
o
f
r
o
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te
f
ailu
r
e
r
atio
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p
r
o
p
o
s
ed
m
eth
o
d
s
u
s
in
g
2
5
n
o
d
es
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u
r
e
6
.
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o
m
p
a
r
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o
n
o
f
r
o
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te
f
ailu
r
e
r
atio
with
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r
o
p
o
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eth
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u
s
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2
5
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ar
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to
ex
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g
m
eth
o
d
s
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e
r
o
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te
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ailu
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e
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ate
f
o
r
2
5
n
o
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es
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ig
n
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ican
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lo
we
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s
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in
Fig
u
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e
5
.
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h
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co
m
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tatio
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al
tim
e
f
o
r
2
5
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o
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was
3
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wh
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s
with
ex
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tin
g
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s
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r
esp
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ely
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I
n
Fig
u
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6
,
th
e
r
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f
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in
th
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p
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ed
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e
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d
,
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p
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et
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s
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f
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is
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ter
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r
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Du
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CO
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co
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s
tr
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W
SN
s
f
r
o
m
a
co
llectio
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o
f
wir
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s
en
s
o
r
s
,
ea
ch
with
d
is
tin
ct
ca
p
a
cities
an
d
co
n
s
tr
ain
ts
,
m
ak
in
g
th
e
m
s
u
it
ab
le
f
o
r
s
p
ec
if
ic
u
s
es.
T
h
is
p
ap
er
p
r
o
v
i
d
es
an
e
v
o
lu
tio
n
ar
y
alg
o
r
ith
m
f
o
r
p
ath
d
is
co
v
er
y
i
n
wir
eless
s
en
s
o
r
n
etwo
r
k
s
.
T
h
e
cu
r
r
en
t
r
o
u
tin
g
p
ath
is
s
u
e
f
o
cu
s
es
o
n
d
y
n
am
ic
d
is
co
v
er
y
,
u
tili
zin
g
ch
r
o
m
o
s
o
m
e
d
etec
tio
n
tech
n
iq
u
es
to
g
e
n
er
ate
o
p
tim
al
s
o
lu
tio
n
s
an
d
p
r
o
v
id
e
esti
m
ated
o
u
tco
m
es
f
o
r
co
n
s
id
er
atio
n
.
I
t
ex
am
in
es
d
ata
tr
an
s
m
is
s
io
n
p
ath
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
,
b
o
t
h
o
f
wh
ich
h
av
e
s
im
ilar
p
r
o
p
er
ties
f
o
r
tak
e
o
u
t
d
ata
am
o
n
g
two
g
en
es.
T
h
e
cu
r
r
en
t
te
ch
n
iq
u
e
p
r
o
v
id
es
elev
ated
c
o
n
v
er
g
en
ce
in
s
tead
o
f
f
o
cu
s
in
g
o
n
t
h
e
o
p
tim
al
s
h
o
r
t
est
p
ath
.
Dif
f
er
en
t
ap
p
r
o
ac
h
es
ar
e
u
s
ed
to
in
v
esti
g
ate
th
e
r
o
u
te'
s
f
ailu
r
e.
I
n
th
is
s
ce
n
ar
io
,
ir
r
eg
u
lar
ef
f
o
r
ts
in
th
e
ea
r
ly
p
o
p
u
latio
n
m
i
n
im
ally
elev
ate
th
e
s
u
b
s
p
ac
e
d
if
f
i
cu
lty
.
T
h
e
p
o
s
s
ib
le
n
u
m
b
er
o
f
attem
p
ts
to
ev
alu
ate
th
e
tar
g
et
task
in
cr
ea
s
es
wi
th
th
e
n
u
m
b
er
o
f
in
cr
em
en
ts
.
T
h
e
p
r
ec
is
e
in
p
u
ts
ac
h
iev
e
a
9
8
%
r
ate
o
f
p
r
o
g
r
es
s
iv
e
r
o
u
te
d
is
co
v
er
y
.
W
e
n
o
te
th
at
p
ath
d
is
co
v
er
y
f
ails
wh
en
ce
r
tain
n
o
d
es
s
tar
t
a
f
ew
r
o
u
tes
with
lo
w
e
n
er
g
y
lev
els.
A
n
u
m
b
er
o
f
c
o
m
p
u
tatio
n
s
ar
e
co
o
r
d
in
ated
with
th
e
cu
r
r
en
t
ca
lcu
latio
n
,
wh
ich
d
eter
m
in
es
d
y
n
am
ic
d
is
co
v
er
y
.
T
h
er
e
f
o
r
e,
as
n
o
d
e
s
ize
r
is
e
s
,
it
m
ay
b
e
p
o
s
s
ib
le
to
en
h
an
ce
r
o
u
te
f
in
d
in
g
b
y
u
s
in
g
a
v
a
r
iety
o
f
p
o
p
u
latio
n
g
en
er
atio
n
ap
p
r
o
ac
h
es.
RE
F
E
R
E
NC
E
S
[
1
]
D
.
A
i
r
e
h
r
o
u
r
,
J
.
A
.
G
u
t
i
e
r
r
e
z
,
a
n
d
S
.
K
.
R
a
y
,
“
S
e
c
Tr
u
st
-
R
P
L:
a
se
c
u
r
e
t
r
u
st
-
a
w
a
r
e
R
P
L
r
o
u
t
i
n
g
p
r
o
t
o
c
o
l
f
o
r
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s,”
Fu
t
u
r
e
G
e
n
e
r
a
t
i
o
n
C
o
m
p
u
t
e
r
S
y
s
t
e
m
s
,
v
o
l
.
9
3
,
p
p
.
8
6
0
–
8
7
6
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
f
u
t
u
r
e
.
2
0
1
8
.
0
3
.
0
2
1
.
[
2
]
A
.
H
a
mz
a
h
,
M
.
S
h
u
r
m
a
n
,
O
.
A
l
-
Ja
r
r
a
h
,
a
n
d
E
.
Ta
q
i
e
d
d
i
n
,
“
En
e
r
g
y
-
e
f
f
i
c
i
e
n
t
f
u
z
z
y
-
l
o
g
i
c
-
b
a
se
d
c
l
u
st
e
r
i
n
g
t
e
c
h
n
i
q
u
e
f
o
r
h
i
e
r
a
r
c
h
i
c
a
l
r
o
u
t
i
n
g
p
r
o
t
o
c
o
l
s
i
n
w
i
r
e
l
e
s
s
sen
so
r
n
e
t
w
o
r
k
s,
”
S
e
n
s
o
r
s
(
S
w
i
t
ze
r
l
a
n
d
)
,
v
o
l
.
1
9
,
n
o
.
3
,
2
0
1
9
,
d
o
i
:
1
0
.
3
3
9
0
/
s1
9
0
3
0
5
6
1
.
[
3
]
T.
M
.
B
e
h
e
r
a
e
t
a
l
.
,
“
E
n
e
r
g
y
-
e
f
f
i
c
i
e
n
t
r
o
u
t
i
n
g
p
r
o
t
o
c
o
l
s
f
o
r
w
i
r
e
l
e
ss
s
e
n
s
o
r
n
e
t
w
o
r
k
s:
a
r
c
h
i
t
e
c
t
u
r
e
s
,
st
r
a
t
e
g
i
e
s,
a
n
d
p
e
r
f
o
r
ma
n
c
e
,
”
El
e
c
t
r
o
n
i
c
s
,
v
o
l
.
1
1
,
n
o
.
1
5
,
Ju
l
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s
1
1
1
5
2
2
8
2
.
[
4
]
A
.
K
u
m
a
r
,
M
.
Z
h
a
o
,
K
.
J
.
W
o
n
g
,
Y
.
L
.
G
u
a
n
,
a
n
d
P
.
H
.
J
.
C
h
o
n
g
,
“
A
c
o
m
p
r
e
h
e
n
s
i
v
e
s
t
u
d
y
o
f
I
o
T
a
n
d
W
S
N
M
A
C
p
r
o
t
o
c
o
l
s
:
r
e
se
a
r
c
h
i
s
s
u
e
s
,
c
h
a
l
l
e
n
g
e
s
a
n
d
o
p
p
o
r
t
u
n
i
t
i
e
s
,
”
I
E
E
E
A
c
c
e
s
s
,
v
o
l
.
6
,
p
p
.
7
6
2
2
8
–
7
6
2
6
2
,
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ES
S
.
2
0
1
8
.
2
8
8
3
3
9
1
.
[
5
]
N
.
M
u
r
u
g
a
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