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,
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p
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in
a
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o
n
i
n
t
h
e
o
p
t
i
m
i
z
a
t
i
o
n
f
ield
to
p
r
o
p
o
s
e
n
e
w
ap
p
r
o
ac
h
es.
B
u
t u
n
til t
h
is
m
o
m
en
t
n
o
alg
o
r
ith
m
is
e
f
f
ec
ti
v
e
f
ac
i
n
g
s
u
ch
p
r
o
b
lem
s
.
Flo
w
s
h
o
p
s
c
h
ed
u
l
in
g
p
r
o
b
lem
s
w
er
e
p
r
o
v
ed
to
b
e
NP
-
h
ar
d
w
h
en
t
h
e
n
u
m
b
er
o
f
m
ac
h
in
es
m
≥
3
[
6
]
.
Hen
ce
,
ex
ac
t
m
e
th
o
d
s
ca
n
n
o
t
b
e
u
tili
s
ed
to
f
in
d
an
o
p
ti
m
al
s
o
lu
tio
n
f
o
r
th
e
p
r
o
b
lem
s
.
R
esear
ch
er
s
h
a
v
e
d
ev
elo
p
ed
m
a
n
y
h
eu
r
i
s
tics
an
d
m
eta
-
h
e
u
r
is
t
ics
f
o
r
th
e
f
lo
w
s
h
o
p
s
c
h
ed
u
l
in
g
p
r
o
b
le
m
s
to
f
i
n
d
n
ea
r
o
p
ti
m
al
s
o
lu
tio
n
s
[
7
].
I
n
t
h
e
ca
s
e
w
h
er
e
it
is
d
es
ir
ed
to
s
o
lv
e
th
e
p
r
o
b
lem
i
n
a
n
e
x
ac
t
m
an
n
er
,
tech
n
iq
u
e
s
s
u
c
h
as
lin
ea
r
p
r
o
g
r
a
m
m
in
g
,
d
y
n
a
m
ic
p
r
o
g
r
am
m
i
n
g
o
r
th
e
b
r
an
ch
a
n
d
b
o
u
n
d
m
eth
o
d
ar
e
o
f
ten
u
s
ed
as
s
h
o
w
n
i
n
Fig
u
r
e
1
.
I
t
s
h
o
u
ld
b
e
r
em
e
m
b
er
ed
th
at
th
e
ex
ec
u
tio
n
ti
m
e
an
d
th
e
m
e
m
o
r
y
s
p
ac
e
o
f
th
es
e
m
et
h
o
d
s
in
cr
ea
s
e
ex
p
o
n
en
t
iall
y
d
ep
en
d
i
n
g
o
n
th
e
s
ize
o
f
th
e
p
r
o
b
le
m
s
to
b
e
tr
ea
ted
.
Fig
u
r
e
1
.
A
g
e
n
er
al
clas
s
i
f
icat
io
n
o
f
m
e
th
o
d
s
f
o
r
s
o
lv
i
n
g
o
p
t
i
m
izatio
n
p
r
o
b
lem
s
T
h
e
n
ee
d
to
f
in
d
q
u
ic
k
l
y
a
s
o
lu
tio
n
o
f
g
o
o
d
q
u
alit
y
f
a
v
o
r
s
th
e
ap
p
ea
r
an
ce
o
f
ap
p
r
o
x
i
m
ate
o
r
s
to
ch
ast
ic
al
g
o
r
ith
m
s
s
u
ch
as
h
e
u
r
is
tic
s
,
s
ee
f
o
r
e
x
a
m
p
le:
J
o
h
n
s
o
n
[
8
]
,
P
alm
er
[
9
]
,
C
a
m
p
b
ell,
Du
d
e
k
,
a
n
d
S
m
it
h
[
10
].
Me
ta
-
h
eu
r
i
s
tics
ar
e
g
en
er
al
h
e
u
r
is
tic
p
r
o
ce
d
u
r
e
s
th
at
ca
n
b
e
u
s
ed
f
o
r
m
a
n
y
p
r
o
b
le
m
s
,
in
o
u
r
ca
s
e,
to
th
e
P
FS
P
.
T
h
ese
m
et
h
o
d
s
n
o
r
m
al
l
y
s
tar
t
f
r
o
m
a
r
a
n
d
o
m
s
eq
u
en
ce
o
r
a
s
eq
u
e
n
ce
co
n
s
tr
u
cted
b
y
h
e
u
r
is
t
ics
an
d
iter
ate
u
n
ti
l
a
s
to
p
p
in
g
cr
iter
io
n
is
m
et.
T
h
er
e
is
lar
g
e
p
ar
t
o
f
r
esear
ch
w
o
r
k
d
o
n
e
f
o
r
th
e
P
FS
P
an
d
m
eta
-
h
eu
r
i
s
tics
,
w
e
w
ill
s
h
o
w
o
u
t
a
f
e
w
n
o
te
w
o
r
t
h
y
p
ap
er
s
m
ai
n
l
y
d
ea
li
n
g
w
i
th
s
i
m
u
lated
an
n
ea
l
in
g
(
S
A
)
[
11
]
,
tab
o
o
s
ea
r
ch
(
T
S)
[1
2
]
,
g
en
etic
alg
o
r
ith
m
s
(
G
A
)
[1
3
].
I
n
th
i
s
ar
ticle,
w
e
ar
e
in
ter
este
d
in
a
m
e
ta
-
h
e
u
r
is
tic
t
h
at
is
i
n
s
p
ir
ed
b
y
th
e
n
atu
r
al
b
eh
a
v
io
r
o
f
a
b
ir
d
s
p
ec
ies
ca
lled
C
u
ck
o
o
.
T
h
is
ch
o
ice
w
as
m
o
t
iv
ated
b
y
s
ev
er
al
r
ea
s
o
n
s
;
f
ir
s
tl
y
,
it
’
s
a
n
e
w
al
g
o
r
ith
m
(
m
eta
-
h
eu
r
i
s
tic)
d
ev
elo
p
ed
v
er
y
r
ec
e
n
tl
y
b
y
Ya
n
g
a
n
d
Deb
[
1
4
]
.
Seco
n
d
l
y
,
th
er
e
i
s
n
o
t
a
v
er
y
d
e
tailed
s
tu
d
y
o
n
t
h
e
p
ar
am
eter
s
o
f
th
is
m
eta
-
h
e
u
r
is
tic.
T
h
r
ee
,
it
h
as
b
ee
n
s
u
cc
es
s
f
u
ll
y
ap
p
lied
in
s
e
v
er
al
t
y
p
e
s
o
f
p
r
o
b
lem
s
.
So
w
e
p
r
o
p
o
s
e
an
ad
ap
tatio
n
o
f
C
S
a
lg
o
r
ith
m
f
o
r
f
lo
w
s
h
o
p
s
c
h
ed
u
lin
g
p
r
o
b
le
m
a
n
d
a
s
e
n
s
i
tiv
it
y
an
al
y
s
is
ac
co
r
d
in
g
to
th
e
p
ar
a
m
eter
s
o
f
t
h
e
alg
o
r
i
th
m
to
m
i
n
i
m
ize
t
h
e
to
tal
co
m
p
letio
n
ti
m
e
C
m
ax
.
Af
ter
s
ec
tio
n
1
w
h
ich
w
a
s
d
ev
o
ted
to
an
in
tr
o
d
u
ctio
n
t
o
au
to
m
ated
in
d
u
s
tr
ial
m
a
n
u
f
ac
tu
r
i
n
g
s
y
s
te
m
s
,
clas
s
i
f
icatio
n
o
f
o
u
r
p
r
o
b
lem
an
d
m
et
h
o
d
s
o
f
r
eso
lu
tio
n
.
T
h
e
r
est
o
f
th
i
s
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
:
Sectio
n
2
p
r
esen
ts
a
b
r
ief
liter
atu
r
e
r
ev
ie
w
t
h
at
b
r
in
g
s
to
g
et
h
er
th
e
m
o
s
t
i
m
p
o
r
ta
n
t
w
o
r
k
s
o
f
c
u
ck
o
o
r
esear
ch
th
at
w
as
d
o
n
e
in
d
if
f
er
en
t
f
ie
ld
s
.
A
d
escr
ip
tio
n
o
f
th
e
alg
o
r
ith
m
to
s
tu
d
y
a
n
d
i
ts
ad
ap
tatio
n
is
s
u
m
m
ar
ized
in
s
ec
t
io
n
3
.
T
h
e
last
s
ec
tio
n
is
d
ev
o
ted
to
th
e
r
esu
lt
s
f
o
u
n
d
a
n
d
th
e
ir
in
ter
p
r
etatio
n
s
.
2.
L
I
T
T
E
RA
T
UR
E
RE
V
I
E
W
As
w
e
m
e
n
tio
n
ed
p
r
ev
io
u
s
l
y
,
C
u
c
k
o
o
s
ea
r
ch
is
a
r
ec
e
n
tl
y
d
ev
elo
p
ed
m
eta
-
h
e
u
r
is
tic
al
g
o
r
i
th
m
b
ased
o
n
th
e
p
ar
asi
tic
b
r
ee
d
in
g
b
e
h
av
io
r
o
f
ce
r
tai
n
s
p
ec
ies
o
f
c
u
ck
o
o
an
d
t
h
e
p
r
ese
n
ce
o
f
L
év
y
f
l
ig
h
ts
in
s
u
ch
r
ep
r
o
d
u
ctio
n
s
tr
ateg
y
[
1
5
].
No
w
ad
a
y
s
cu
c
k
o
o
s
ea
r
ch
h
a
s
b
ee
n
u
s
ed
i
n
al
m
o
s
t
e
v
er
y
ar
ea
o
f
:
s
c
h
ed
u
l
in
g
[
1
6
]
,
f
u
n
c
t
i
o
n
o
p
t
i
m
i
z
a
t
i
o
n
[
1
7
]
,
e
n
g
i
n
e
e
r
i
n
g
o
p
t
i
m
i
z
a
t
i
o
n
[
1
7
]
,
a
n
d
p
l
a
n
n
i
n
g
[
1
7
].
T
h
e
r
e
c
e
n
t
a
p
p
l
i
c
a
t
i
o
n
s
o
f
C
S
f
o
r
o
p
t
i
m
i
z
a
t
i
o
n
p
r
o
b
l
e
m
s
h
a
v
e
s
h
o
w
n
i
t
s
p
r
o
m
i
s
i
n
g
e
f
f
e
c
t
i
v
e
n
e
s
s
[
1
8
]
.
F
o
r
e
x
a
m
p
l
e
,
t
h
e
w
o
r
k
o
f
W
a
n
g
et
al
.
[
1
6
]
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.
11
,
No
.
3
,
J
u
n
e
2
0
2
1
:
2
2
6
6
-
2274
2268
w
h
er
e
a
n
e
w
c
u
ck
o
o
s
ea
r
c
h
al
g
o
r
ith
m
w
i
th
lo
ca
l
s
ea
r
c
h
(
N
C
S)
i
s
p
r
o
p
o
s
ed
f
o
r
s
o
lv
i
n
g
t
h
e
p
er
m
u
tatio
n
f
lo
w
s
h
o
p
s
c
h
ed
u
l
in
g
p
r
o
b
le
m
.
He
n
ce
,
f
o
r
p
o
p
u
latio
n
in
i
tializatio
n
;
t
h
e
y
u
s
ed
t
h
e
NE
H
h
eu
r
i
s
t
ic
(
N
w
az
et
a
l
.
)
to
g
en
er
ate
h
ig
h
q
u
ali
t
y
i
n
itial
s
o
lu
tio
n
s
.
A
n
o
v
e
l
v
ar
ia
n
t
o
f
th
e
C
S
al
g
o
r
ith
m
r
ef
er
r
ed
as
th
e
I
n
ter
-
Sp
ec
ies
C
u
c
k
o
o
Sear
ch
.
W
e
ca
n
also
m
en
tio
n
t
h
e
w
o
r
k
d
o
n
e
b
y
T
s
ip
ian
iti
s
et
a
l
.
[
1
9
]
w
h
er
e
th
i
s
w
o
r
k
w
as
d
o
n
e
f
o
r
test
i
n
g
th
e
d
y
n
a
m
ic
tu
n
i
n
g
o
f
ce
r
tain
i
m
p
o
r
tan
t
p
ar
a
m
e
ter
s
o
f
t
h
e
C
S
al
g
o
r
ith
m
,
n
a
m
el
y
th
e
p
r
o
b
ab
ilit
y
o
f
f
r
ac
tio
n
(
p
a)
(
an
al
ien
e
g
g
to
b
e
f
o
u
n
d
b
y
th
e
h
o
s
t
b
ir
d
)
an
d
th
e
s
tep
s
ize
o
f
L
èv
y
f
li
g
h
ts
,
i
n
co
m
b
in
atio
n
w
i
th
th
e
i
m
p
le
m
en
ta
tio
n
o
f
t
h
r
ee
f
u
n
ctio
n
s
:
o
n
e
s
tat
ic
an
d
t
w
o
d
y
n
a
m
ic
ap
p
r
o
ac
h
es.
Fi
n
all
y
,
a
h
y
b
r
id
o
p
ti
m
iza
tio
n
alg
o
r
ith
m
is
d
e
v
elo
p
ed
b
y
co
m
b
in
i
n
g
t
h
e
m
o
s
t
e
f
f
icien
t
f
e
atu
r
es
o
f
C
S
w
it
h
t
h
o
s
e
o
f
a
n
o
th
er
s
w
ar
m
-
b
a
s
ed
o
p
tim
izer
,
n
a
m
el
y
b
ir
d
s
w
ar
m
alg
o
r
ith
m
(
B
S
A
)
to
ac
ce
ler
ate
th
e
co
n
v
er
g
e
n
ce
to
w
ar
d
s
g
lo
b
al
o
p
tim
u
m
.
I
SC
S
[1
5
]
is
d
e
v
elo
p
ed
to
m
i
n
i
m
ize
t
h
e
m
a
k
esp
an
an
d
m
e
an
f
lo
w
ti
m
e
in
b
o
t
h
h
y
b
r
id
f
lo
w
-
s
h
o
p
s
ch
ed
u
lin
g
(
HFS)
an
d
p
er
m
u
tatio
n
f
lo
w
-
s
h
o
p
s
eq
u
e
n
cin
g
p
r
o
b
lem
s
(
P
FS
P
)
.
Fu
r
th
er
m
o
r
e,
a
h
y
b
r
id
m
eta
-
h
eu
r
i
s
tic
b
ased
o
n
cu
ck
o
o
s
ea
r
ch
alg
o
r
ith
m
an
d
d
if
f
er
en
t
ial
ev
o
lu
tio
n
f
o
r
n
u
m
e
r
ical
o
p
ti
m
izatio
n
is
p
r
o
p
o
s
ed
b
y
X
i
et
a
l
.
[
20
]
,
th
e
alg
o
r
it
h
m
h
as
b
ee
n
test
ed
an
d
co
m
p
ar
ed
w
it
h
o
th
er
m
eta
-
h
e
u
r
is
t
ics,
C
o
m
p
u
tatio
n
a
l
ex
p
er
i
m
e
n
ts
d
ep
th
o
n
a
w
id
e
r
an
g
e
o
f
s
e
ts
o
f
p
r
o
b
le
m
s
s
h
o
w
th
at
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
o
u
tp
er
f
o
r
m
s
m
an
y
o
th
er
m
eta
-
h
eu
r
i
s
tics
.
Z
h
a
n
g
et
a
l
.
[2
1
]
,
m
ad
e
a
s
tu
d
y
r
ef
e
r
en
ce
d
s
tate
o
f
t
h
e
ar
t
s
w
ar
m
in
telli
g
e
n
ce
b
ased
in
telli
g
e
n
t
alg
o
r
it
h
m
s
,
esp
ec
ia
ll
y
an
t
co
lo
n
ie
s
an
d
th
e
cu
c
k
o
o
s
ea
r
ch
alg
o
r
ith
m
,
w
i
th
t
h
e
m
o
d
i
f
icat
io
n
o
f
ea
ch
alg
o
r
ith
m
an
d
h
y
b
r
id
izatio
n
s
tr
ateg
y
o
f
t
h
e
m
e
n
tio
n
ed
alg
o
r
ith
m
s
.
T
h
ey
p
r
esen
ted
th
e
m
o
d
i
f
ied
A
C
O
(
an
t
co
lo
n
y
o
p
ti
m
izatio
n
)
,
m
o
d
if
ie
d
C
S,
an
d
HA
C
C
S
(
h
y
b
r
id
an
t
co
lo
n
y
a
n
d
cu
ck
o
o
s
ea
r
ch
)
alg
o
r
ith
m
s
to
s
o
lv
e
th
e
h
ea
tin
g
r
o
u
te
p
r
o
b
le
m
.
T
h
e
p
r
o
p
o
s
ed
a
lg
o
r
ith
m
s
w
er
e
ap
p
lied
to
th
e
Z
F
(
z
h
u
o
z
h
o
u
-
f
a
n
g
s
h
an
)
h
ea
ti
n
g
en
g
i
n
ee
r
i
n
g
p
r
o
j
ec
t.
T
h
e
r
esu
lts
s
h
o
w
t
h
at
m
o
d
if
ied
A
C
O
ca
n
f
i
n
d
th
e
r
o
u
te
(
s
o
l
u
tio
n
)
w
it
h
t
h
e
s
m
allest
o
b
j
ec
tiv
e
f
u
n
ct
io
n
v
al
u
e,
w
h
il
e
th
e
m
o
d
if
ied
C
S
ca
n
f
in
d
t
h
e
r
o
u
te
o
v
er
lap
p
ed
t
o
th
e
m
a
n
u
al
s
elec
ted
r
o
u
te
b
etter
.
Fu
r
th
er
m
o
r
e,
th
e
m
o
d
i
f
ied
C
S
r
an
m
o
r
e
q
u
ick
l
y
b
u
t
s
tu
ck
i
n
to
th
e
p
r
e
m
at
u
r
e
co
n
v
e
r
g
en
ce
.
Fo
llo
w
i
n
g
th
e
co
n
v
er
g
en
ce
s
t
u
d
y
m
e
n
tio
n
ed
ab
o
v
e,
t
h
e
b
est
r
o
u
te
c
h
o
s
en
b
y
th
e
h
y
b
r
id
al
g
o
r
ith
m
H
AC
C
S
w
a
s
t
h
e
s
a
m
e
as th
e
m
o
d
i
f
ied
A
C
O
alg
o
r
it
h
m
,
b
u
t
w
it
h
g
r
ea
ter
e
f
f
icien
c
y
an
d
b
etter
s
tab
ilit
y
.
3.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
t
h
e
p
ast
s
ev
er
al
y
ea
r
s
,
d
i
f
f
er
en
t
k
i
n
d
s
o
f
n
atu
r
e
-
i
n
s
p
ir
ed
o
p
tim
iza
tio
n
a
lg
o
r
it
h
m
s
h
av
e
b
ee
n
cr
ea
t
ed
,
an
d
th
e
y
b
ec
o
m
e
v
er
y
p
o
p
u
lar
.
W
e
ca
n
m
o
n
t
io
n
,
p
ar
ticles
s
w
ar
m
o
p
ti
m
iza
t
io
n
P
SO
[
2
2
]
w
a
s
in
s
p
ir
ed
b
y
f
is
h
a
n
d
b
ir
d
s
w
a
r
m
i
n
telli
g
e
n
ce
,
a
n
t
co
lo
n
y
o
p
ti
m
izatio
n
(
A
C
O)
[2
1
]
an
d
T
h
e
A
P
I
alg
o
r
it
h
m
w
er
e
in
s
p
ir
ed
f
r
o
m
t
h
e
f
o
r
ag
in
g
b
eh
a
v
io
u
r
o
f
a
p
o
p
u
latio
n
o
f
a
n
ts
[
2
3
]
.
C
u
c
k
o
o
s
ea
r
c
h
i
s
a
r
ec
en
t
m
e
ta
-
h
eu
r
i
s
tic.
I
t
h
a
s
en
r
ic
h
ed
th
e
n
u
m
b
er
o
f
p
o
p
u
latio
n
-
b
ased
m
eta
-
h
e
u
r
is
tics
s
o
l
u
tio
n
s
,
w
h
i
ch
is
i
n
s
p
ir
ed
b
y
th
e
p
ar
ad
ig
m
o
f
b
ir
d
s
g
r
o
u
p
i
n
g
.
I
n
Yan
g
an
d
Deb
i
n
v
e
n
ted
a
n
e
w
alg
o
r
it
h
m
m
e
ta
-
h
e
u
r
is
ti
c
in
s
p
ir
ed
b
y
n
at
u
r
e.
Sp
ec
if
i
ca
ll
y
,
t
h
e
alg
o
r
ith
m
w
as
i
n
s
p
ir
ed
b
y
th
e
o
b
lig
ate
b
r
o
o
d
p
ar
asit
ic
b
eh
av
io
r
o
f
s
o
m
e
cu
c
k
o
o
s
p
ec
ies
f
r
o
m
w
h
ich
co
m
e
s
th
e
n
a
m
e
:
c
u
ck
o
o
s
ea
r
ch
(
C
S
)
in
co
m
b
in
at
io
n
w
it
h
L
ev
y
's
f
li
g
h
t
b
e
h
av
io
r
o
f
s
o
m
e
b
ir
d
s
an
d
f
r
u
it
f
lies
i
n
n
atu
r
e
[1
4
].
A
s
ec
o
n
d
v
er
s
io
n
w
as
cr
ea
ted
b
y
Y
a
n
g
an
d
De
b
in
2
0
1
0
n
am
ed
cu
c
k
o
o
o
p
ti
m
izatio
n
alg
o
r
ith
m
(
C
O
A
)
[
1
7
].
I
n
ad
d
itio
n
to
th
e
C
S,
t
h
e
C
O
A
al
g
o
r
ith
m
,
i
s
a
n
alg
o
r
ith
m
w
it
h
s
i
m
ilar
in
s
p
ir
atio
n
i
n
n
atu
r
e,
h
as
r
ec
en
tl
y
at
tr
ac
ted
m
u
c
h
atte
n
ti
o
n
s
in
s
o
lv
i
n
g
o
p
ti
m
izatio
n
p
r
o
b
lem
s
.
T
h
e
p
io
n
ee
r
s
o
f
th
e
C
S
alg
o
r
ith
m
w
er
e
in
s
p
ir
ed
b
y
th
e
p
ar
asit
ic
r
ep
r
o
d
u
ctio
n
b
eh
av
io
r
o
f
s
o
m
e
s
p
ec
ies
o
f
cu
c
k
o
o
th
at
la
y
th
eir
eg
g
s
w
i
th
in
t
h
e
n
e
s
ts
o
f
o
th
er
s
p
ec
ies
b
y
en
tr
u
s
tin
g
t
h
e
r
esp
o
n
s
ib
ili
t
y
o
f
in
cu
b
ati
n
g
,
f
ee
d
in
g
a
n
d
r
ea
r
i
n
g
t
h
eir
c
h
ic
k
s
to
t
h
e
h
o
s
t
b
ir
d
s
.
T
h
ese
ca
n
d
etec
t
cu
c
k
o
o
’
s
eg
g
s
i
n
th
e
ir
n
e
s
ts
;
d
u
r
in
g
t
h
is
ca
s
e
t
h
e
h
o
s
t
b
ir
d
w
il
l
eit
h
er
ej
ec
t
t
h
e
c
u
c
k
o
o
’
s
eg
g
s
o
u
t
o
f
it
s
n
es
t
o
r
ab
an
d
o
n
it
s
o
w
n
n
e
s
t
a
n
d
b
u
ild
an
o
th
er
o
n
e
i
n
an
o
t
h
er
l
o
ca
tio
n
.
T
h
e
C
S p
r
o
p
o
s
ed
b
y
Ya
n
g
a
n
d
Deb
in
2
0
0
9
is
b
ased
o
n
th
e
f
o
llo
w
in
g
t
h
r
ee
b
asic r
u
les
:
E
ac
h
cu
c
k
o
o
la
y
s
o
n
e
e
g
g
at
a
ti
m
e.
He
d
r
o
p
s
it in
a
n
es
t th
a
t h
e
ch
o
o
s
es r
a
n
d
o
m
l
y
.
A
n
est o
f
g
o
o
d
q
u
alit
y
w
i
ll b
e
ca
r
r
ied
o
v
er
to
th
e
n
ex
t g
e
n
er
a
tio
n
.
T
h
e
n
u
m
b
er
o
f
h
o
s
t
n
est
s
is
f
i
x
ed
,
an
d
an
eg
g
laid
b
y
a
cu
c
k
o
o
ca
n
b
e
d
is
co
v
er
ed
b
y
th
e
h
o
s
t b
ir
d
ac
co
r
d
in
g
to
a
p
r
o
b
a
b
ilit
y
P
a
∈
[
0
,
1
]
.
I
n
th
is
ca
s
e,
th
e
h
o
s
t b
i
r
d
ca
n
eith
er
th
r
o
w
th
e
e
g
g
a
wa
y
o
r
ab
an
d
o
n
th
e
n
e
s
t,
an
d
b
u
i
ld
a
co
m
p
lete
l
y
n
e
w
n
e
s
t
i
n
a
n
e
w
lo
ca
tio
n
r
an
d
o
m
l
y
ch
o
s
e
n
.
B
ased
o
n
th
ese
th
r
ee
r
u
les,
th
e
b
asic
s
tep
s
o
f
th
e
cu
ck
o
o
s
e
ar
ch
(
C
S)
ca
n
b
e
s
u
m
m
ar
ized
as
th
e
p
s
eu
d
o
co
d
e
sh
o
w
n
in
F
ig
u
r
e
2
[1
4
]
.
A
c
u
ck
o
o
i g
e
n
er
ates
a
n
e
w
s
o
lu
tio
n
(
+
1
)
v
ia
L
é
v
y
f
li
g
h
t
s
,
ac
co
r
d
in
g
to
(
1
)
;
(
+
1
)
=
(
)
+
⊕
é
(
,
)
(
1
)
w
h
er
e
α
>0
is
th
e
m
a
x
i
m
al
le
n
g
t
h
o
f
t
h
e
s
tep
w
h
ic
h
s
h
o
u
ld
b
e
b
o
u
n
d
o
n
th
e
s
ca
le
o
f
th
e
s
p
ac
e
o
f
s
ea
r
ch
f
o
r
th
e
p
r
o
b
lem
to
b
e
h
a
n
d
led
.
I
n
m
o
s
t c
ase
s
,
=
1
is
u
s
ed
[
1
4
]
.
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th
e
r
a
n
d
o
m
w
al
k
v
ia
t
h
e
L
év
y
f
li
g
h
t
is
m
o
r
e
e
f
f
ec
ti
v
e
in
t
h
e
ex
p
lo
r
atio
n
o
f
t
h
e
s
ea
r
c
h
s
p
ac
e
b
ec
au
s
e
its
s
tep
s
ize
is
m
u
ch
lo
n
g
er
i
n
lo
n
g
-
ter
m
[
1
8
].
T
h
e
L
é
v
y
f
li
g
h
t
r
ep
r
esen
ts
es
s
en
tia
ll
y
a
r
a
n
d
o
m
w
al
k
w
h
er
ea
s
th
e
r
an
d
o
m
s
t
ep
s
ize
(
α
)
is
d
ef
i
n
ed
f
r
o
m
a
L
év
y
d
is
tr
ib
u
tio
n
.
é
∼
=
,
−
(
1
<
≤
3
)
(
2
)
I
t sh
o
u
ld
b
e
n
o
ted
th
at
t
h
e
L
év
y
h
a
s
an
i
n
f
in
i
te
v
ar
ia
n
ce
w
it
h
an
in
f
i
n
ite
m
ea
n
[
1
4
].
T
h
e
m
ai
n
ch
ar
ac
ter
i
s
tic
o
f
t
h
e
alg
o
r
it
h
m
C
S
i
s
its
s
i
m
p
l
icit
y
.
I
n
f
ac
t,
b
y
co
m
p
ar
in
g
w
it
h
o
th
er
p
o
p
u
latio
n
o
r
ag
e
n
t
-
b
ased
m
et
a
-
h
eu
r
i
s
tics
al
g
o
r
ith
m
s
,
t
h
er
e
is
a
s
i
m
ilar
it
y
w
i
th
P
SO
an
d
GA
,
b
u
t
C
S
it
u
s
e
s
s
o
m
e
s
o
r
t
o
f
el
itis
m
a
n
d
/o
r
s
elec
tio
n
s
i
m
ilar
to
th
at
e
m
p
l
y
ed
i
n
h
ar
m
o
n
y
s
ea
r
c
h
.
Fu
r
t
h
er
m
o
r
e,
th
e
r
an
d
o
m
izatio
n
i
s
m
o
r
e
ef
f
icie
n
t a
s
t
h
e
s
tep
len
g
t
h
is
h
ea
v
y
-
t
ailed
,
an
d
an
y
lar
g
e
s
tep
is
p
o
s
s
ib
le.
3
.
1
.
Ada
pta
t
io
n
a
lg
o
rit
h
m
C
S i
s
a
p
o
p
u
latio
n
-
b
ased
al
g
o
r
ith
m
th
er
e
w
i
th
f
e
w
p
ar
a
m
ete
r
s
to
b
e
d
ef
in
ed
,
an
d
t
h
u
s
it
is
p
o
ten
tiall
y
m
o
r
e
g
e
n
er
ic
to
ad
ap
t
to
a
w
i
d
er
class
o
f
o
p
ti
m
izatio
n
p
r
o
b
le
m
s
.
T
h
e
o
r
ig
i
n
al
v
er
s
io
n
o
f
t
h
e
C
S
alg
o
r
it
h
m
w
a
s
p
r
o
p
o
s
ed
to
s
o
lv
e
co
n
tin
u
o
u
s
p
r
o
b
lem
s
o
f
o
p
ti
m
izatio
n
[
2
4
]
.
Ho
w
e
v
er
,
th
e
p
r
o
b
lem
s
o
f
o
p
tim
izatio
n
ar
e
n
o
t
all
co
n
ti
n
u
o
u
s
t
y
p
e;
t
h
e
y
c
an
also
b
e
d
is
cr
ete
[
2
1
]
o
r
b
in
ar
y
t
y
p
e
[
1
8
]
.
Gen
er
all
y
,
t
h
e
m
o
d
i
f
icat
io
n
ca
n
b
e
ca
teg
o
r
ized
in
to
t
w
o
clas
s
es.
First
cla
s
s
is
th
e
ad
j
u
s
t
m
en
t
o
f
t
h
e
p
ar
a
m
eter
s
,
a
n
d
t
h
e
s
ec
o
n
d
is
h
y
b
r
id
izatio
n
w
it
h
o
th
er
i
n
telli
g
e
n
ce
alg
o
r
it
h
m
s
[
2
5
]
.
T
h
e
w
o
r
k
in
t
h
is
s
ec
tio
n
w
ill
f
o
cu
s
o
n
t
h
e
ad
ap
tatio
n
o
f
t
h
e
C
S
to
f
lo
w
s
h
o
p
s
ch
ed
u
li
n
g
p
r
o
b
lem
,
it
is
th
e
m
o
s
t
w
e
ll
-
k
n
o
w
n
co
m
b
i
n
ato
r
ial
o
p
ti
m
iza
tio
n
p
r
o
b
lem
i
n
th
e
r
ea
l
w
o
r
ld
.
T
h
e
C
S
a
lg
o
r
it
h
m
is
a
d
ap
ted
an
d
ap
p
lied
o
n
th
e
F
S
SP
w
it
h
i
ts
o
w
n
p
r
o
ce
d
u
r
e
with
o
u
t
u
s
i
n
g
o
t
h
er
i
m
p
r
o
v
e
m
en
t
s
to
s
h
o
w
it
s
p
er
f
o
r
m
a
n
ce
to
t
h
is
t
y
p
e
o
f
p
r
o
b
le
m
a
n
d
to
ha
v
e
r
es
u
lts
to
b
e
s
tu
d
ied
.
T
h
e
r
esu
lt
o
f
ad
ap
tatio
n
is
a
n
e
w
b
a
s
ic
v
er
s
io
n
o
f
C
S
n
a
m
ed
"
b
asic
d
is
cr
ete
cu
ck
o
o
s
ea
r
ch
(
B
DC
S)"
.
T
h
e
p
r
o
ce
d
u
r
e
o
f
ad
ap
tatio
n
r
eq
u
ir
es a
d
ef
in
i
tio
n
o
f
t
h
e
f
o
llo
w
i
n
g
ele
m
e
n
t
s
:
3
.
1
.1
.
Nest
I
n
th
e
ca
s
e
o
f
o
u
r
co
m
b
i
n
ato
r
i
al
o
p
ti
m
izatio
n
p
r
o
b
le
m
(
P
FS
P
)
,
a
n
est
is
co
n
s
id
er
ed
as
an
in
d
iv
id
u
al
o
f
th
e
p
o
p
u
latio
n
w
i
th
i
ts
o
w
n
s
o
lu
tio
n
.
Mo
r
eo
v
er
,
a
n
est h
a
s
th
e
f
o
llo
w
i
n
g
p
r
o
p
er
ties
:
T
h
e
n
u
m
b
er
o
f
n
est
s
is
eq
u
al
t
o
th
e
s
ize
o
f
t
h
e
p
o
p
u
latio
n
.
T
h
e
to
tal
n
u
m
b
er
o
f
n
est
s
is
f
i
x
ed
f
r
o
m
t
h
e
b
eg
i
n
i
n
g
.
An
ab
an
d
o
n
ed
o
r
d
estro
y
ed
n
e
s
t in
v
o
lv
e
s
r
ep
lacin
g
an
i
n
d
iv
i
d
u
al
o
f
th
e
p
o
p
u
latio
n
w
it
h
a
n
e
w
o
n
e.
An
eg
g
i
n
a
n
es
t r
ep
r
esen
ts
a
s
o
lu
tio
n
,
i
n
o
u
r
ca
s
e
r
ep
r
esen
ts
a
s
eq
u
en
ce
o
f
j
o
b
s
.
3
.
1
.2
.
E
g
g
As
w
e
h
a
v
e
s
aid
b
ef
o
r
e,
a
cu
ck
o
o
ca
n
la
y
a
s
in
g
le
eg
g
in
a
s
i
n
g
le
n
est,
w
h
ic
h
g
i
v
es
t
h
e
eg
g
s
th
e
f
o
llo
w
in
g
c
h
ar
ac
ter
is
tic
s
:
An
“
eg
g
i
n
a
n
es
t”
is
an
ad
o
p
ted
s
o
lu
tio
n
b
y
t
h
e
i
n
d
iv
id
u
al
o
f
th
e
p
o
p
u
latio
n
.
An
eg
g
d
etec
ted
an
d
r
ej
ec
ted
b
y
a
c
u
ck
o
o
i
m
p
lie
s
a
b
ad
s
o
lu
tio
n
.
A
c
u
ck
o
o
eg
g
is
a
n
e
w
ca
n
d
id
ate
s
o
lu
tio
n
f
o
r
a
p
lace
in
th
e
p
o
p
u
latio
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.
11
,
No
.
3
,
J
u
n
e
2
0
2
1
:
2
2
6
6
-
2274
2270
3
.
1
.3
.
O
bje
ct
iv
e
f
un
ct
io
n
T
h
e
o
b
j
ec
tiv
e
f
u
n
c
tio
n
(
o
f
test
,
f
it
n
es
s
)
is
a
f
u
n
ctio
n
w
h
ic
h
,
w
it
h
ea
c
h
s
o
lu
tio
n
i
n
t
h
e
s
p
ac
e
o
f
s
ea
r
c
h
ass
o
ciate
s
a
n
u
m
er
ical
v
alu
e
to
r
ep
r
esen
t
it
s
q
u
ali
t
y
o
r
f
i
tn
es
s
.
So
t
h
e
q
u
al
it
y
o
r
f
it
n
e
s
s
o
f
a
s
o
l
u
tio
n
is
p
r
o
p
o
r
tio
n
al
to
th
e
v
al
u
e
o
f
t
h
e
o
b
j
ec
tiv
e
f
u
n
ct
io
n
.
I
n
o
u
r
p
r
o
b
lem
,
a
n
es
t (
s
eq
u
en
ce
)
o
f
b
e
tter
q
u
alit
y
g
i
v
es
u
s
an
o
p
ti
m
al
C
m
a
x
o
r
a
s
o
lu
tio
n
clo
s
e
to
o
p
tim
al
C
m
a
x
.
3
.
1
.
4
.
Sea
rc
h sp
a
ce
T
h
e
s
ea
r
ch
s
p
ac
e
in
a
v
er
y
co
m
b
in
ato
r
ial
o
p
ti
m
iza
tio
n
ca
s
e
s
w
i
ll
b
e
s
ee
n
as
a
"
g
r
ap
h
"
w
h
er
e
v
er
tices
r
ep
r
esen
t so
lu
tio
n
s
a
n
d
ed
g
es
co
n
n
ec
t
n
e
ig
h
b
o
r
in
g
p
air
s
o
f
s
o
lu
tio
n
s
[
2
6
].
a.
Mo
v
e
m
e
n
t
I
n
t
h
e
co
m
b
i
n
ato
r
ial
p
r
o
b
le
m
,
th
e
co
o
r
d
in
ates
o
f
a
s
o
lu
tio
n
in
s
p
ac
e
r
esear
c
h
ar
e
m
o
d
if
i
ed
th
r
o
u
g
h
th
e
p
r
o
p
er
ti
es
o
f
t
h
e
p
r
o
b
le
m
b
ein
g
ad
d
r
ess
ed
.
Ge
n
er
all
y
,
th
e
c
h
a
n
g
e
o
f
p
o
s
itio
n
w
it
h
i
n
th
e
co
m
b
i
n
ato
r
ial
s
p
ac
e
is
d
o
n
e
b
y
a
ch
an
g
e
w
it
h
in
t
h
e
o
r
d
er
o
f
t
h
e
ele
m
e
n
ts
o
f
th
e
s
o
lu
t
io
n
,
b
y
a
co
m
b
i
n
at
io
n
,
a
p
er
m
u
ta
tio
n
,
o
r
a
s
et
o
f
o
p
er
ato
r
s
n
a
m
ed
p
er
tu
r
b
atio
n
o
r
m
o
v
e
m
en
t.
b.
Neig
h
b
o
r
h
o
o
d
T
h
e
co
n
ce
p
t
o
f
n
eig
h
b
o
r
h
o
o
d
r
eq
u
ir
es
th
at
t
h
e
n
e
w
s
o
l
u
tio
n
o
f
a
g
iv
e
n
s
o
lu
tio
n
b
e
g
en
er
a
ted
b
y
th
e
s
m
al
lest
p
o
s
s
ib
le
p
er
tu
r
b
an
ce
.
T
h
is
d
is
r
u
p
tio
n
s
h
o
u
ld
m
ak
e
t
h
e
m
i
n
i
m
u
m
c
h
a
n
g
e
s
to
th
e
p
r
esen
t
s
o
lu
tio
n
.
c.
L
é
v
y
f
li
g
h
ts
L
é
v
y
F
lig
h
t
s
h
as
a
s
o
b
j
ec
tiv
e,
an
in
te
n
s
i
v
e
r
esear
ch
ar
o
u
n
d
a
s
o
lu
tio
n
,
f
o
llo
w
ed
b
y
lo
n
g
-
t
er
m
s
tep
s
.
A
cc
o
r
d
in
g
to
Ya
n
g
a
n
d
Deb
,
lo
o
k
in
g
f
o
r
a
n
e
w
b
etter
s
o
lu
tio
n
is
m
o
r
e
ef
f
icie
n
t
v
ia
L
ev
y
f
l
ig
h
ts
.
So
,
to
en
h
a
n
ce
t
h
e
q
u
alit
y
o
f
t
h
e
s
ea
r
ch
w
e
'
ll
a
s
s
o
c
i
a
t
e
t
h
e
l
e
n
g
t
h
o
f
t
h
e
s
t
e
p
w
i
t
h
t
h
e
v
a
l
u
e
g
e
n
e
r
a
t
e
d
b
y
L
é
v
y
f
lig
h
t
s
.
d.
Step
T
h
e
s
tep
is
t
h
at
t
h
e
d
is
ta
n
ce
b
et
w
ee
n
t
w
o
s
o
l
u
tio
n
s
.
I
t
’
s
b
as
ed
o
n
t
h
e
to
p
o
lo
g
y
o
f
s
p
ac
e
an
d
th
er
ef
o
r
e
th
e
co
n
ce
p
t
o
f
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ei
g
h
b
o
r
h
o
o
d
.
Du
r
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t
h
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s
w
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w
e
h
a
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h
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te
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n
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i
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t
w
i
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eir
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g
t
h
,
t
h
e
ch
ar
ac
ter
an
d
also
th
e
n
u
m
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o
f
s
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cc
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s
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i
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e
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atio
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I
n
B
DC
S,
L
e
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s
h
a
v
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all
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p
lace
m
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s
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o
f
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l
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tio
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b
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Ho
w
e
v
er
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w
e
w
ill
s
h
o
w
h
o
w
to
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t
a
s
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l
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i
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s
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ac
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an
d
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o
w
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m
o
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e
f
r
o
m
t
h
e
p
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t
s
o
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tio
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to
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o
th
er
u
s
in
g
s
o
m
e
o
p
er
ato
r
s
,
s
u
ch
as
s
w
ap
,
in
s
er
t,
a
n
d
in
v
er
s
e
[
1
6
]
.
I
n
th
is
p
ap
er
,
th
e
s
w
ap
p
i
n
g
s
tr
ateg
y
s
ea
r
ch
i
s
u
s
ed
to
g
e
n
er
ate
a
n
e
ig
h
b
o
r
o
f
t
h
e
c
u
r
r
en
t
s
o
lu
t
io
n
.
T
h
e
f
ir
s
t
o
p
er
ato
r
is
‘
s
w
ap
’
; t
w
o
j
o
b
s
at
d
i
f
f
er
e
n
t
p
o
s
itio
n
s
i
n
th
e
c
u
r
r
en
t
s
o
l
u
ti
o
n
ar
e
s
elec
ted
an
d
s
w
itc
h
ed
.
B
y
p
er
f
o
r
m
i
n
g
th
e
o
p
er
atio
n
,
a
ne
w
s
o
lu
t
io
n
is
g
etti
n
g
.
F
ig
u
r
e
3
d
escr
ib
es th
e
o
p
er
atio
n
o
f
th
e
ex
c
h
an
g
e
o
p
er
ato
r
.
C
u
r
r
en
t so
lu
t
io
n
(
x
)
:
j
o
b
1
j
o
b
3
j
o
b
6
j
o
b
4
j
o
b
5
j
o
b
2
Ne
w
s
o
lu
tio
n
(
x
’
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:
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o
b
1
j
o
b
5
j
o
b
6
j
o
b
4
j
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b
3
j
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b
2
Fig
u
r
e
3
.
T
h
e
s
w
ap
o
p
er
ato
r
T
h
e
s
ec
o
n
d
o
p
er
ato
r
is
‘
in
s
er
t
’
;
t
h
e
ch
o
s
e
n
j
o
b
is
m
o
v
ed
f
r
o
m
it
s
cu
r
r
en
t
p
o
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i
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i
n
s
o
l
u
ti
o
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(
x
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an
d
in
s
er
ted
to
an
o
t
h
er
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itio
n
.
Af
ter
t
h
is
o
p
er
atio
n
,
w
e
ca
n
g
et
a
n
e
w
s
o
lu
tio
n
(
x
’
)
.
Fi
g
u
r
e
4
s
h
o
w
s
t
h
e
i
n
s
er
t
o
p
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ato
r
.
T
h
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last
ope
r
ato
r
is
‘
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v
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’
;
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h
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tas
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et
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tw
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x
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ter
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n
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Fig
u
r
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5
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ates
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li
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m
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latio
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ar
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r
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w
ith
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o
m
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s
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d
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p
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k
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n
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r
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m
t
h
e
OR
-
L
ib
r
ar
y
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I
n
th
is
p
ap
er
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s
ix
p
r
o
b
le
m
s
f
r
o
m
th
r
ee
c
lass
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s
o
f
P
FS
P
tes
t
p
r
o
b
lem
s
d
esi
g
n
ed
b
y
T
aillar
d
ar
e
s
elec
ted
f
o
r
o
u
r
s
tu
d
ie
s
[
2
7
]
.
T
h
e
f
ir
s
t
t
w
o
p
r
o
b
lem
s
ar
e
s
m
a
ll
in
s
ta
n
ce
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T
aillar
d
1
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aillar
d
2)
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s
er
ted
in
T
ab
le
s
1
-
2
.
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h
e
s
ec
o
n
d
p
r
o
b
lem
s
ar
e
m
ed
i
u
m
i
n
s
ta
n
ce
s
(
T
aillar
d
3
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aillar
d
4)
em
p
lo
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ed
i
n
T
ab
le
s
3
-
4
an
d
t
h
e
las
t
t
w
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p
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lem
s
s
elac
ted
ar
e
a
lar
g
e
i
n
s
ta
n
ce
s
(
T
aillar
d
5
-
T
aillar
d
6)
f
o
u
n
d
i
n
T
ab
le
s
5
-
6
.
T
h
ese
p
r
o
b
lem
s
h
av
e
b
ee
n
w
id
el
y
u
t
ilis
ed
as
b
en
c
h
m
ar
k
s
f
o
r
test
in
g
th
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p
er
f
o
r
m
a
n
ce
o
f
alg
o
r
it
h
m
s
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y
m
a
n
y
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esear
ch
er
s
s
u
ch
as
W
an
g
H
.
et
a
l
.
f
o
r
FS
SP
[
1
6
]
,
Van
Ho
o
r
n
et
el
.
f
o
r
jo
b
s
h
o
p
[
2
8
],
a
n
d
B
en
zian
i
et
a
l
.
f
o
r
o
p
en
s
h
o
p
[2
9
].
T
h
e
tab
les
s
u
m
m
ar
ize
th
e
ex
p
er
i
m
en
tal
r
esu
lt
s
,
th
e
f
ir
s
t
co
lu
m
n
s
h
o
w
s
t
h
e
p
r
o
b
lem
s
ize
an
d
w
e
g
iv
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in
th
e
co
lu
m
n
‘
lo
w
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/
u
p
p
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b
o
u
n
d
’
t
h
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b
est
lo
w
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b
o
u
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d
(
o
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tain
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b
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r
an
ch
an
d
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)
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d
th
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p
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d
(
th
e
s
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lu
tio
n
s
m
o
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t
k
n
o
w
n
in
t
h
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liter
at
u
r
e
)
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o
r
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ch
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s
ta
n
ce
.
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h
e
'
n
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t/it
t
'
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lu
m
n
s
h
o
w
s
th
e
n
u
m
b
er
o
f
th
e
n
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s
t
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th
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m
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er
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f
iter
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s
.
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h
e
'
pa
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m
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in
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n
ta
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o
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o
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ests
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d
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f
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m
0
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3
to
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9
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it
h
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s
tep
o
f
0
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2
.
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co
llected
th
e
r
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lts
t
h
en
in
clu
d
ed
th
e
m
i
n
s
i
x
tab
les
w
h
ic
h
w
e
d
iv
id
ed
o
n
th
e
b
a
s
is
o
f
th
e
p
r
o
b
lem
s
ize
.
T
h
e
p
r
o
b
lem
w
i
th
f
iv
e
(
5
)
m
ac
h
i
n
es i
s
a
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m
all
p
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(
1
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m
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m
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i
n
T
ab
les
3
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4
,
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n
d
t
h
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p
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le
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w
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w
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t
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is
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le
m
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le
s
5
-
6
.
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n
y
w
a
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,
w
e
w
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ll d
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s
s
th
e
r
e
s
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l
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s
m
all,
m
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a
n
d
lar
g
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p
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lem
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.
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h
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ex
p
er
i
m
e
n
ts
v
ar
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ac
co
r
d
in
g
to
th
r
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(
3
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pa
r
am
eter
s
w
h
ic
h
ar
e:
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e
p
r
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b
ab
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f
f
r
ac
tio
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r
d
estru
ctio
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(
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ts
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Nes
t)
an
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n
u
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b
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o
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s
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itt
)
.
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alg
o
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ith
m
w
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co
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in
MA
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35
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5.
CO
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I
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w
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s
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t
k
n
o
wn
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ce
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in
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h
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liter
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T
aillar
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ce
s
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,
t
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aid
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DC
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u
itab
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w
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ca
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ap
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iate
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it
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t b
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.
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.
11
,
No
.
3
,
J
u
n
e
2
0
2
1
:
2
2
6
6
-
2274
2274
RE
F
E
R
E
NC
E
S
[1
]
Jo
se
M
.
F
ra
m
in
a
n
,
Ra
in
e
r
L
e
iste
n
,
Ru
b
é
n
R
u
iz
G
a
rc
ía,
“
M
a
n
u
f
a
c
tu
rin
g
S
c
h
e
d
u
li
n
g
S
y
ste
m
s:
A
n
I
n
teg
ra
ted
V
iew
o
n
M
o
d
e
ls,
M
e
th
o
d
s a
n
d
T
o
o
ls,”
S
p
rin
g
e
r L
o
n
d
o
n
He
id
e
lb
e
rg
Ne
w
Y
o
rk
Do
rd
re
c
h
t
,
fi
rs
t
e
d
it
io
n
,
2
0
1
4
.
[2
]
Kh
e
d
im
,
A
.
,
e
t
a
l.
,
“
Ord
o
n
n
a
n
c
e
m
e
n
t
te
m
p
s
ré
e
l
d
’u
n
F
M
S
p
a
r
l’
a
p
p
ro
c
h
e
d
e
l’alg
o
rit
h
m
e
d
e
c
o
lo
n
ies
d
’a
b
e
il
les
,
”
Co
ll
o
q
u
e
I
n
ter
n
a
ti
o
n
a
l
S
u
r L
e
M
o
n
it
o
rin
g
De
s S
y
stè
m
e
s In
d
u
strie
ls
,
CIM
S
I
,
2
0
1
4
.
[3
]
K.
E.
S
tec
k
e
,
“
De
sig
n
,
p
lan
n
in
g
,
sc
h
e
d
u
li
n
g
a
n
d
c
o
n
tro
l
p
ro
b
lem
s
in
f
lex
ib
le
m
a
n
u
f
a
c
tu
rin
g
sy
ste
m
s,”
An
n
a
ls
o
f
Op
e
ra
ti
o
n
s R
e
se
a
rc
h
,
v
o
l
.
3
,
n
o
.
1
,
p
p
.
3
-
1
2
,
1
9
8
5
.
[4
]
P
i
n
e
d
o
,
M
.
,
“
S
c
h
e
d
u
li
n
g
:
T
h
e
o
ry
,
A
lg
o
rit
h
m
s a
n
d
S
y
ste
m
s,”
Pre
n
ti
c
e
Ha
ll
,
Ne
w
J
e
rs
e
y
,
se
c
o
n
d
e
d
it
i
o
n
2
0
0
2
.
[5
]
G
ra
h
a
m
R.
L
.
,
L
a
w
l
e
r
E.
L
.
,
Len
stra
J.
K.
a
n
d
Ri
n
n
o
o
y
Ka
n
A
.
H.
G
.
,
“
Op
ti
m
isa
ti
o
n
a
n
d
a
p
p
ro
x
im
a
ti
o
n
i
n
d
e
term
in
isti
c
se
q
u
e
n
c
in
g
a
n
d
sc
h
e
d
u
li
n
g
:
a
su
rv
e
y
,
”
An
n
a
ls
o
f
Dis
c
re
te M
a
th
e
ma
ti
c
s
,
v
o
l.
5
,
p
p
.
2
8
7
-
3
2
6
,
1
9
7
9
.
[6
]
G
a
r
e
y
,
M
.
R.
D.,
Jo
h
n
so
n
,
D.
S
.
,
S
e
th
i,
R.
,
“
T
h
e
c
o
m
p
lex
it
y
o
f
f
lo
w
sh
o
p
a
n
d
jo
b
sh
o
p
sc
h
e
d
u
li
n
g
,
”
M
a
th
e
ma
ti
c
s o
f
Op
e
ra
ti
o
n
s R
e
se
a
rc
h
,
v
o
l
.
1
,
n
o
.
2
,
p
p
.
1
1
7
-
1
2
9
,
1
9
7
6
.
[7
]
A
le
x
a
n
d
e
r
J.
Be
n
a
v
id
e
s,
M
a
rc
u
s
Rit
t,
“
T
w
o
si
m
p
le
a
n
d
e
ff
e
c
ti
v
e
h
e
u
risti
c
s
f
o
r
m
in
i
m
izin
g
th
e
m
a
k
e
sp
a
n
in
n
o
n
-
p
e
rm
u
tatio
n
f
lo
w
sh
o
p
s,”
Co
m
p
u
t
e
rs
&
Op
e
ra
ti
o
n
s R
e
se
a
rc
h
,
v
o
l.
6
6
,
p
p
.
1
6
0
-
1
6
9
,
2
0
1
6
.
[8
]
Jo
h
n
s
o
n
,
S
.
M
.
,
“
Op
ti
m
a
l
Tw
o
a
n
d
T
h
re
e
S
tag
e
P
ro
d
u
c
ti
o
n
S
c
h
e
d
u
les
w
it
h
S
e
t
-
Up
T
i
m
e
s,
In
c
lu
d
e
d
,
”
Na
v
a
l
Res
e
a
rc
h
L
o
g
isti
c
s Qu
a
rte
rly
,
v
o
l
.
1
,
n
o
.
1
,
p
p
.
6
1
-
6
8
,
1
9
5
4
.
[9
]
P
a
lm
e
r,
D.
S
.
,
“
S
e
q
u
e
n
c
in
g
jo
b
s
th
ro
u
g
h
a
m
u
lt
i
-
sta
g
e
p
ro
c
e
ss
in
th
e
m
in
im
u
m
to
tal
ti
m
e
-
a
q
u
ick
m
e
th
o
d
o
f
o
b
tai
n
in
g
a
n
e
a
r
o
p
ti
m
u
m
,
”
J
o
u
rn
a
l
o
f
th
e
O
p
e
ra
ti
o
n
a
l
Res
e
a
rc
h
S
o
c
ie
ty
,
v
o
l.
1
6
,
p
p
.
1
0
1
-
1
0
7
,
1
9
6
5
.
[1
0
]
Ca
m
p
b
e
ll
,
H.
G
.
,
Du
d
e
k
,
R.
A
.
,
S
m
it
h
,
M
.
L
.
,
“
A
H
e
u
risti
c
A
l
g
o
rit
h
m
f
o
r
th
e
n
Jo
b
,
m
M
a
c
h
in
e
S
e
q
u
e
n
c
i
n
g
P
r
o
b
lem
,
”
M
a
n
a
g
e
me
n
t
S
c
ien
c
e
,
v
o
l.
1
6
,
n
o
.
1
0
,
p
p
.
6
3
0
-
6
3
7
,
1
9
7
0
.
[1
1
]
S
.
Kirk
p
a
tri
c
k
,
C.
D.
G
e
l
a
tt
,
M
.
P
.
V
e
c
c
h
i,
“
S
c
ien
c
e
O
p
ti
m
iz
a
ti
o
n
b
y
S
i
m
u
late
d
A
n
n
e
a
li
n
g
,
”
S
c
ien
c
e
,
v
o
l.
2
2
0
,
n
o
.
4
5
9
8
,
p
p
.
6
7
1
-
6
8
0
,
1
9
8
3
.
[1
2
]
F.
G
lo
v
e
r
,
“
T
a
b
u
S
e
a
rc
h
-
P
a
rt
I,
”
ORS
A
J
o
u
rn
a
l
o
n
C
o
mp
u
ti
n
g
,
v
o
l
.
1
,
n
o
.
3
,
1
9
8
9
.
[1
3
]
Ho
ll
a
n
d
,
J.,
“
A
d
a
p
tatio
n
i
n
n
a
t
u
ra
l
a
n
d
a
rti
f
icia
l
sy
ste
m
s,”
T
h
e
Un
ive
rs
it
y
o
f
M
ich
ig
a
n
Pre
ss
,
A
n
n
Ar
b
o
r
.
1
9
7
5
.
[1
4
]
X
.
S
.
Ya
n
g
,
a
n
d
S
.
De
b
,
“
Cu
c
k
o
o
se
a
rc
h
v
ia
L
é
v
y
f
li
g
h
ts,”
2
0
0
9
W
o
rld
Co
n
g
re
ss
o
n
Na
tu
re
&
Bi
o
lo
g
ica
ll
y
In
sp
ire
d
Co
mp
u
t
in
g
(
Na
BIC)
,
C
o
im
b
a
to
re
,
2
0
0
9
,
p
p
.
2
1
0
-
2
1
4
.
[1
5
]
P
re
e
tam
Da
s
g
u
p
ta,
a
n
d
S
w
a
g
a
ta
m
Da
s,
“
A
Disc
r
e
te
In
ter
-
S
p
e
c
ies
Cu
c
k
o
o
S
e
a
rc
h
f
o
r
F
lo
w
sh
o
p
S
c
h
e
d
u
li
n
g
P
r
o
b
lem
s,”
Co
mp
u
ter
s &
Op
e
ra
ti
o
n
s R
e
se
a
rc
h
,
v
o
l.
6
0
,
p
p
.
1
1
1
-
1
2
0
,
2
0
1
5
.
[1
6
]
W
a
n
g
H.,
W
a
n
g
W
.
J.,
S
u
n
H.,
Cu
i
Z.
H.,
Ra
h
n
a
m
a
y
a
n
S
.
,
Zen
g
S
.
Y.,
“
A
n
e
w
c
u
c
k
o
o
se
a
rc
h
a
lg
o
rit
h
m
w
it
h
h
y
b
rid
stra
teg
ies
f
o
r
f
lo
w
sh
o
p
sc
h
e
d
u
l
in
g
p
r
o
b
lem
s,”
S
o
ft
C
o
mp
u
t
in
g
,
v
o
l.
2
1
,
pp
.
4
2
9
7
-
4
3
0
7
,
2
0
1
6
.
[1
7
]
I.
F
ister
Jr.,
X
.
S
.
Ya
n
g
,
D.
F
ist
e
r,
I.
F
ister,
“
Cu
c
k
o
o
se
a
rc
h
:
A
b
rief
li
tera
tu
re
re
v
ie
w
,
in
:
Cu
c
k
o
o
S
e
a
rc
h
a
n
d
F
iref
l
y
A
lg
o
rit
h
m
:
T
h
e
o
r
y
a
n
d
Ap
p
li
c
a
ti
o
n
s,”
S
tu
d
ies
in
Co
mp
u
t
a
t
io
n
a
l
I
n
telli
g
e
n
c
e
,
v
o
l.
5
1
6
,
p
p
.
4
9
-
6
2
,
2
0
1
4
[1
8
]
G
h
e
rb
o
u
d
j,
A
.
,
L
a
y
e
b
,
A
.
a
n
d
C
h
ik
h
i,
S
.
,
“
S
o
lv
in
g
0
-
1
k
n
a
p
sa
c
k
p
ro
b
lem
s
b
y
a
d
isc
re
teb
in
a
r
y
v
e
rs
io
n
o
f
c
u
c
k
o
o
se
a
rc
h
a
lg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Bi
o
-
In
s
p
ire
d
C
o
mp
u
ta
t
i
o
n
,
v
o
l.
4
,
n
o
.
4
,
p
p
.
2
2
9
-
2
3
6
,
2
0
1
2
.
[1
9
]
T
sip
ian
it
is,
A
.
,
a
n
d
T
so
m
p
a
n
a
k
i
s,
Y.,
“
I
m
p
ro
v
e
d
Cu
c
k
o
o
S
e
a
rc
h
a
lg
o
rit
h
m
ic
v
a
rian
ts
f
o
r
c
o
n
stra
in
e
d
n
o
n
li
n
e
a
r
o
p
ti
m
iza
ti
o
n
,
”
i
n
A
d
v
a
n
c
e
s in
En
g
in
e
e
rin
g
S
o
ft
w
a
re
,
v
o
l.
1
4
9
,
p
.
1
0
2
8
6
5
,
2
0
2
0
.
[2
0
]
Ju
n
X
i
,
a
n
d
L
i
m
in
g
Zh
e
n
g
,
“
A
h
y
b
rid
a
lg
o
rit
h
m
b
a
se
d
o
n
c
u
c
k
o
o
se
a
rc
h
a
n
d
d
if
fe
re
n
ti
a
l
e
v
o
lu
ti
o
n
f
o
r
n
u
m
e
rica
l
o
p
ti
m
iza
ti
o
n
,
”
J
o
u
rn
a
l
Co
m
p
u
t
in
g
,
Per
fo
rm
a
n
c
e
a
n
d
C
o
mm
u
n
ica
ti
o
n
S
y
ste
ms
,
v
o
l.
4
,
n
o
.
1
,
p
p
.
1
-
8
,
2
0
2
0
.
[2
1
]
Zh
a
n
g
,
Y.,
Zh
a
o
,
H.,
Ca
o
,
Y.
,
L
iu
,
Q.,
S
h
e
n
,
Z
.
,
W
a
n
g
,
J.,
Hu
,
M
.
,
“
A
H
y
b
rid
A
n
t
Co
lo
n
y
a
n
d
Cu
c
k
o
o
S
e
a
rc
h
A
l
g
o
rit
h
m
f
o
r
Ro
u
te Op
ti
m
i
z
a
ti
o
n
o
f
He
a
ti
n
g
En
g
in
e
e
rin
g
,
”
E
n
e
rg
ies
,
v
o
l.
1
1
,
n
o
.
1
0
,
p
.
2
6
7
5
,
2
0
1
8
.
[2
2
]
Ke
n
n
e
d
y
,
J.
a
n
d
Eb
e
r
h
a
rt,
R.
C
.
,
“
A
d
isc
re
te
b
in
a
ry
v
e
rsio
n
o
f
th
e
p
a
rti
c
le
sw
a
r
m
a
lg
o
rit
h
m
,
”
1
9
9
7
IEE
E
In
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
S
y
st
e
ms
,
M
a
n
,
a
n
d
Cy
b
e
rn
e
ti
c
s.
C
o
m
p
u
t
a
ti
o
n
a
l
Cy
b
e
r
n
e
ti
c
s
a
n
d
S
imu
l
a
ti
o
n
,
Orla
n
d
o
,
F
L
,
USA
,
v
o
l.
5
,
1
9
9
7
,
p
p
.
4
1
0
4
-
4
1
0
8
.
[2
3
]
B
o
u
m
e
d
i
e
n
e
F
.
Z
.
,
H
o
u
b
a
d
Y
.
,
B
e
s
s
e
n
o
u
c
i
H
.
N
.
,
H
a
s
s
a
m
A
.
,
G
h
o
m
r
i
L
.
,
“
A
M
a
k
e
s
p
a
n
M
i
n
i
m
i
z
a
t
i
o
n
A
P
I
A
l
g
o
r
i
t
h
m
f
o
r
F
l
o
w
S
h
o
p
S
c
h
e
d
u
l
i
n
g
,
”
i
n
E
l
e
c
t
r
o
t
e
h
n
i
c
a
,
E
l
e
c
t
r
o
n
i
c
a
,
A
u
t
o
m
a
t
i
c
a
(
E
E
A
)
,
v
o
l
.
6
7
,
n
o
.
2
,
p
p
.
123
-
1
2
9
,
2
0
1
9
.
[2
4
]
G
a
n
d
o
m
i,
A
.
H.,
Ya
n
g
,
X
.
-
S
.
,
&
A
la
v
i,
A
.
H.,
“
Cu
c
k
o
o
se
a
rc
h
a
lg
o
rit
h
m
:
a
m
e
tah
e
u
risti
c
a
p
p
ro
a
c
h
t
o
so
lv
e
stru
c
tu
ra
l
o
p
ti
m
iza
ti
o
n
p
ro
b
lem
s,
”
En
g
i
n
e
e
rin
g
w
it
h
C
o
mp
u
ter
s
,
v
o
l.
2
9
,
n
o
.
1
,
p
p
.
1
7
-
3
5
,
2
0
1
3
.
[2
5
]
Ch
iro
m
a
,
H.,
e
t
a
l
.
,
“
Bio
-
in
sp
ire
d
c
o
m
p
u
tatio
n
:
Re
c
e
n
t
d
e
v
e
lo
p
m
e
n
t
o
n
th
e
m
o
d
if
ica
ti
o
n
s
o
f
th
e
c
u
c
k
o
o
se
a
r
c
h
a
lg
o
rit
h
m
,
”
Ap
p
li
e
d
S
o
ft
C
o
mp
u
ti
n
g
,
v
o
l.
6
1
,
p
p
.
1
4
9
-
1
7
3
,
2
0
1
7
.
[2
6
]
Ou
a
a
ra
b
,
A
z
iz,
“
Ré
so
lu
ti
o
n
d
e
P
ro
b
lèm
e
s
d
'
Op
ti
m
is
a
ti
o
n
C
o
m
b
in
a
to
ire
p
a
r
d
e
s
M
é
tah
e
u
rist
iq
u
e
s
I
n
sp
irée
s
d
e
la
Na
tu
re
,
”
Rec
h
e
rc
h
e
d
u
C
o
u
c
o
u
v
i
a
les
Vo
ls d
e
L
é
v
y
,
2
0
1
5
.
[2
7
]
E.
T
a
il
lard
,
"
Be
n
c
h
m
a
rk
s
f
o
r
b
a
sic
sc
h
e
d
u
li
n
g
p
r
o
b
lem
s,"
Eu
ro
p
e
a
n
J
o
u
r
n
a
l
o
f
Op
e
ra
t
io
n
a
l
Res
e
a
rc
h
,
v
o
l.
6
4
,
n
o
.
2
,
p
p
.
2
7
8
-
2
8
5
,
1
9
9
3
.
[2
8
]
V
a
n
Ho
o
r
n
,
Je
lk
e
J.,
“
T
h
e
Cu
rre
n
t
sta
te
o
f
b
o
u
n
d
s
o
n
b
e
n
c
h
m
a
rk
in
sta
n
c
e
s
o
f
th
e
jo
b
-
sh
o
p
sc
h
e
d
u
li
n
g
p
ro
b
lem
,
”
J
o
u
rn
a
l
o
f
S
c
h
e
d
u
l
in
g
,
v
o
l.
2
1
,
n
o
.
1
,
p
p
.
1
2
7
-
1
2
8
,
2
0
1
8
.
[2
9
]
Be
n
z
ian
i,
Ya
c
in
e
,
Ka
c
e
m
,
I
m
e
d
,
E.
T
.
L
a
ro
c
h
e
,
P
ierre
,
“
G
e
n
e
ti
c
A
l
g
o
rit
h
m
f
o
r
Op
e
n
S
h
o
p
S
c
h
e
d
u
li
n
g
P
r
o
b
le
m
,
”
2
0
1
8
5
t
h
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
n
tro
l,
De
c
isio
n
a
n
d
I
n
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
ies
(
Co
DIT)
,
T
h
e
ss
a
lo
n
ik
i,
2
0
1
8
,
p
p
.
9
3
5
-
9
3
9
.
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