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nta
ti
v
e
planni
ng
pr
oblem
(
M
our
ad
B
ouatouche
)
407
p
r
i
or
i
t
i
e
s
,
g
e
t
r
i
d
o
f
di
s
tr
a
c
t
i
o
n
s
,
a
s
s
i
gn
tas
ks
,
a
nd
p
u
t
i
n
p
l
a
c
e
a
w
e
l
l
-
or
ga
ni
z
e
d
t
i
m
e
m
a
n
a
ge
m
e
nt
s
y
s
t
e
m
[2
]
,
[
3
]
.
N
owa
d
a
y
s
,
t
h
e
ph
a
r
m
a
c
e
u
t
i
c
a
l
b
us
i
n
e
s
s
i
s
f
a
c
i
n
g
s
e
v
e
r
e
c
o
m
pe
t
i
t
i
o
n
i
n
a
c
h
a
l
l
e
n
g
i
n
g
e
c
o
n
o
m
i
c
e
n
vi
r
o
nm
e
n
t.
T
o
s
u
c
c
e
e
d
,
t
h
e
y
n
e
e
d
to
e
i
t
h
e
r
s
u
r
pa
s
s
t
h
e
i
r
r
i
v
a
l
s
i
n
m
a
r
ke
t
s
h
a
r
e
or
ke
e
p
a
n
a
dv
a
n
t
a
ge
o
v
e
r
th
e
m
t
h
r
ou
gh
c
r
e
a
t
i
v
e
i
de
a
s
or
o
f
f
e
r
i
n
gs
.
T
h
e
f
i
e
l
d
s
a
l
e
s
f
or
c
e
’
s
e
f
f
i
c
a
c
y
i
s
c
r
u
c
i
a
l
i
n
e
v
e
r
y
s
i
t
ua
t
i
o
n
.
T
h
a
t
’
s
why
,
t
h
e
y
m
us
t
pr
e
c
i
s
e
l
y
p
l
a
n
t
h
e
i
r
ope
r
a
t
i
o
n
s
to
g
e
t
n
e
w
c
l
i
e
n
t
s
,
i
m
pr
o
v
e
s
a
l
e
s
pe
r
c
u
s
to
m
e
r
,
a
n
d
m
e
e
t
de
f
i
n
e
d
g
oa
l
s
.
S
a
l
e
s
r
oun
ds
a
r
e
a
ke
y
c
o
m
p
o
n
e
n
t
o
f
s
a
l
e
s
te
c
h
ni
que
.
S
a
l
e
s
r
e
p
r
e
s
e
n
ta
t
i
v
e
s
f
a
c
e
i
m
p
o
r
tan
t
c
h
a
l
l
e
n
ge
s
wh
e
n
i
t
c
o
m
e
s
to
p
r
e
p
a
r
a
t
i
o
n
,
pl
a
nni
n
g
,
a
n
d
op
t
i
m
i
z
a
t
i
o
n
.
T
o
m
a
xi
mi
z
e
p
oten
t
i
a
l
s
a
l
e
s
,
ph
a
r
m
a
c
e
ut
i
c
a
l
s
a
l
e
s
r
e
ps
m
u
s
t
e
s
t
a
b
l
i
s
h
a
d
a
i
l
y
p
l
a
n
n
e
r
ou
t
l
i
ni
n
g
whi
c
h
h
e
a
l
t
h
c
a
r
e
p
r
o
vi
de
r
s
to
vi
s
i
t
a
n
d
i
n
wh
a
t
or
de
r
.
Us
ua
l
l
y
,
t
h
e
d
oc
tor
p
r
ov
i
de
s
a
t
i
m
e
w
i
n
d
o
w
wh
e
n
s
h
e
m
e
e
t
s
w
i
t
h
r
e
pr
e
s
e
n
ta
t
i
v
e
s
b
e
twe
e
n
oth
e
r
p
r
o
f
e
s
s
i
o
n
a
l
r
e
s
p
o
n
s
i
bi
l
i
t
i
e
s
(
s
u
c
h
a
s
e
x
a
m
i
ni
n
g
pa
t
i
e
n
t
s
a
n
d
f
i
l
i
n
g
d
oc
um
e
n
t
s
)
.
A
r
e
p
r
e
s
e
n
t
a
t
i
v
e
m
us
t
a
r
r
i
v
e
a
t
th
e
o
f
f
i
c
e
b
e
f
or
e
t
h
e
d
oc
tor
’
s
t
i
m
e
wi
n
d
ow
c
l
o
s
e
s
[
3
]
.
S
e
v
e
r
a
l
s
a
l
e
s
r
e
p
r
e
s
e
n
ta
t
i
ve
s
g
o
to
th
e
d
oc
tor
a
n
d
ph
a
r
m
a
c
i
s
t
o
n
o
n
e
or
m
or
e
d
a
y
s
.
I
t
i
s
i
m
po
s
s
i
bl
e
to
ta
k
e
e
v
e
r
y
t
hi
n
g.
I
n
t
h
a
t
c
a
s
e
,
t
h
e
s
a
l
e
s
r
e
p
r
e
s
e
n
ta
t
i
v
e
s
s
e
l
e
c
t
wh
a
t
th
e
y
b
e
l
i
e
v
e
to
b
e
th
e
m
o
s
t
a
tt
r
a
c
t
i
v
e
p
o
i
n
t
s
o
f
i
n
t
e
r
e
s
t
(
P
O
I
)
(
d
oc
tor
s
,
ph
a
r
m
a
c
i
s
ts
)
.
T
h
i
s
pa
pe
r
’
s
o
bj
e
c
t
i
v
e
i
s
to
i
d
e
n
t
i
f
y
op
t
i
m
a
l
r
ou
t
e
pl
a
n
ni
n
g
f
or
s
a
l
e
s
r
e
p
r
e
s
e
n
t
a
t
i
v
e
s
or
oth
e
r
f
i
e
l
d
w
or
k
e
r
s
,
i
n
c
l
udi
n
g
t
h
os
e
i
n
t
h
e
r
e
tai
l
f
ood
or
ph
a
r
m
a
c
e
u
t
i
c
a
l
s
e
c
tor
s
,
wh
o
a
r
e
tas
ke
d
wi
t
h
vi
s
i
t
i
n
g
t
h
e
i
r
c
us
to
m
e
r
s
r
e
g
u
l
a
r
l
y
a
t
s
pe
c
i
f
i
c
i
n
ter
v
a
l
s
to
m
a
xi
m
iz
e
th
e
v
a
l
ue
g
a
t
h
e
r
e
d
o
n
a
pa
r
t
i
c
ul
a
r
d
a
y
whi
l
e
a
dh
e
r
i
n
g
to
c
e
r
tai
n
c
o
n
s
tr
a
i
n
t
s
(
s
u
c
h
a
s
de
a
d
l
i
n
e
s
a
n
d
tot
a
l
t
i
m
e
)
.
A
r
ou
te
i
s
a
p
l
a
n
n
e
d
s
e
r
i
e
s
o
f
c
us
to
m
e
r
vi
s
i
t
s
.
T
h
a
t
i
s
,
we
a
tt
e
m
p
t
to
c
r
e
a
te
a
n
op
t
i
m
a
l
r
ou
te
b
a
s
e
d
o
n
m
a
xi
m
izi
n
g
t
h
e
r
e
wa
r
ds
o
f
t
h
e
s
a
l
e
s
r
e
p
r
e
s
e
n
ta
t
i
v
e
,
p
r
o
v
i
de
d
th
a
t
t
h
e
vi
s
i
t
s
to
d
oc
tor
s
or
p
h
a
r
m
a
c
i
s
t
s
m
us
t
b
e
c
a
r
r
i
e
d
ou
t
wi
t
hi
n
t
h
e
s
pe
c
i
f
i
e
d
pe
r
i
od
[
2
]
.
T
h
e
s
a
l
e
s
r
e
p
r
e
s
e
n
ta
t
i
v
e
pl
a
nni
n
g
p
r
o
bl
e
m
i
s
s
i
m
il
a
r
to
a
n
or
i
e
n
tee
r
i
n
g
p
r
obl
e
m
(
OP
)
.
E
a
c
h
s
a
l
e
s
r
e
p
r
e
s
e
n
ta
t
i
v
e
n
e
e
ds
to
ta
c
k
l
e
a
n
OP
v
a
r
i
a
n
t
[
4
]
.
On
e
o
f
t
h
e
m
o
s
t
i
m
p
or
t
a
n
t
a
s
p
e
c
ts
o
f
s
a
l
e
s
r
e
p
r
e
s
e
n
ta
t
i
v
e
pl
a
nni
n
g
i
s
c
r
e
a
t
i
n
g
r
ou
tes
f
or
s
u
i
t
a
bl
e
s
a
l
e
s
r
e
pr
e
s
e
n
t
a
t
i
v
e
s
.
S
e
v
e
r
a
l
m
e
t
h
ods
f
or
r
e
s
o
l
vi
ng
t
h
e
s
a
l
e
s
r
e
pr
e
s
e
n
ta
t
i
v
e
p
l
a
n
ni
n
g
a
n
d
OP
a
r
e
de
s
c
r
i
b
e
d
i
n
[
1
]
,
[
5
].
T
h
e
r
e
a
r
e
v
a
r
i
ous
m
e
t
h
ods
f
or
tac
kl
i
n
g
th
e
s
a
l
e
s
r
e
pr
e
s
e
n
ta
t
i
v
e
pl
a
nni
n
g
p
r
o
b
l
e
m
;
[
3
]
s
o
l
v
e
d
t
h
e
p
r
o
b
l
e
m
by
a
p
p
l
y
i
n
g
a
v
a
r
i
a
bl
e
n
e
i
ghb
or
h
ood
s
e
a
r
c
h
a
l
g
or
i
t
hm
,
whi
c
h
t
h
e
y
m
ode
l
e
d
us
i
n
g
t
h
e
m
u
l
t
i
-
pe
r
i
od
O
P
wi
t
h
m
u
l
t
i
pl
e
t
i
m
e
wi
n
d
ows
(
M
uO
P
M
T
W
)
.
Z
h
a
n
g
e
t
a
l
.
[
2]
p
r
op
os
e
d
us
i
n
g
S
OP
T
W
to
m
od
e
l
th
e
s
a
l
e
s
r
e
p
r
e
s
e
n
ta
t
i
v
e
pl
a
n
ni
n
g
p
r
o
bl
e
m
.
T
h
e
s
a
l
e
s
r
e
p
r
e
s
e
n
t
a
t
i
v
e
p
l
a
n
ni
n
g
pr
o
b
l
e
m
i
s
e
qui
v
a
l
e
n
t
to
th
e
O
P
v
a
r
i
a
n
t
s
.
M
a
ny
o
f
t
h
e
a
l
g
or
i
t
h
m
s
m
e
n
t
i
o
n
e
d
i
n
th
e
l
i
t
e
r
a
tu
r
e
de
a
l
w
i
t
h
OP
wi
t
h
wi
n
d
ow
t
i
m
e
(
O
P
T
W
)
or
(
T
)
O
P
T
W
.
T
h
e
s
a
l
e
s
r
e
p
r
e
s
e
n
ta
t
i
v
e
pl
a
n
ni
n
g
pr
o
b
l
e
m
c
ou
l
d
a
l
s
o
b
e
s
o
l
v
e
d
us
i
n
g
t
h
e
s
e
a
l
g
or
i
t
hm
s
.
T
h
e
m
o
s
t
s
i
m
i
l
a
r
O
P
v
e
r
s
i
o
n
to
th
i
s
p
r
o
bl
e
m
i
s
c
a
l
l
e
d
t
h
e
OP
T
W
,
i
n
whi
c
h
a
s
a
l
e
s
r
e
p
r
e
s
e
n
t
a
t
i
v
e
i
s
s
c
h
e
d
u
l
e
d
to
v
i
s
i
t
v
a
r
i
o
us
d
oc
tor
s
to
c
o
l
l
e
c
t
r
e
wa
r
ds
w
i
t
hi
n
a
p
r
e
de
te
r
m
i
n
e
d
b
u
dge
t
a
n
d
wi
n
d
ow
o
f
t
i
m
e
[
1
]
.
T
o
s
o
l
v
e
O
P
T
W
,
[
6
]
de
v
e
l
op
e
d
a
n
e
x
a
c
t
s
o
l
u
t
i
o
n
b
a
s
e
d
o
n
bi
d
i
r
e
c
t
i
o
n
a
l
d
y
n
a
m
i
c
p
r
ogr
a
m
m
i
n
g.
A
n
e
x
tr
e
m
e
l
y
qui
c
k
I
L
S
a
pp
r
oa
c
h
to
s
o
l
vi
n
g
t
h
e
T
O
P
T
W
i
s
pr
op
os
e
d
by
V
a
n
s
t
e
e
n
w
e
ge
n
e
t
a
l
.
[
7
]
.
T
h
e
a
n
t
c
o
l
o
ny
s
y
s
te
m
(
A
C
S
)
m
e
t
h
o
d
wa
s
c
r
e
a
ted
b
y
[
8
]
.
A
v
a
r
i
e
t
y
o
f
di
f
f
e
r
e
n
t
m
e
t
a
h
e
u
r
i
s
t
i
c
s
a
r
e
a
l
s
o
a
v
a
i
l
a
bl
e
i
n
l
i
t
e
r
a
tu
r
e
,
s
uc
h
a
s
a
c
o
m
bi
n
a
t
i
o
n
o
f
a
g
r
e
e
d
y
r
a
n
d
o
m
i
z
e
d
a
da
p
t
i
v
e
s
e
a
r
c
h
pr
oc
e
du
r
e
(
GR
A
S
P
)
a
n
d
a
n
e
v
o
l
u
t
i
o
n
a
r
y
l
oc
a
l
s
e
a
r
c
h
(
E
L
S
)
,
a
g
r
a
n
ul
a
r
v
a
r
i
a
bl
e
n
e
i
gh
b
or
h
ood
s
e
a
r
c
h
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[
1
6
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.
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[
15]
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15]
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16]
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h
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(
r
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b
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tt
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s
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β
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f
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ly
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g
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s
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I
(
r
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c
h
a
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s
w
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t
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gr
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a
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p
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s
s
h
o
wn
i
n
Al
go
r
i
t
hm
1
[
17]
,
[
18]
.
Al
go
r
i
t
hm
1
.
T
h
e
s
t
a
n
da
r
d
f
i
r
e
f
ly
a
l
go
r
i
t
hm
1: Objective function
f
(
x
)
,
x
= (
x
1
,
x
2
,...,
x
d
)
T
2: Initialize a population of fireflies
x
i
(
i
=
1
,
2
,...,
n
)
3: Calculate the light intensity
I
i
4: Specify light absorption coefficient
γ
5:
while
(
t
<
Max_iteration
)
do
6:
for
i
←
1 to
n
(all n fireflies)
do
7:
for
j
←
1 to
n
(all n fireflies)
do
8:
if
(
I
j
>
I
i
)
then
9: Move firefly i to
the j in all d dimensions.
10:
end if
11: Attractiveness depends on distance
r
via
e
−
γr
2
12:
Assess novel solutions and adjust the amount of light intensity.
13:
end for
14:
end for
15:
Find the current best
fireflies by ranking them
16:
end while
17: Sort the firefly and select the best one.
3.
2.
T
h
e
p
r
op
os
e
d
ad
ap
t
ive
ap
p
r
oac
h
f
o
r
t
h
e
f
ir
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l
y
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go
r
it
h
m
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o
t
p
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s
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bl
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to
di
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t
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ly
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to
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t
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t
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o
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o
bl
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m
s
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A
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to
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a
t
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s
f
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f
ly
m
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m
e
n
t
.
3.
2.
1.
T
h
e
r
e
p
r
e
s
e
n
t
at
ion
of
f
ir
e
f
l
y
On
e
o
f
t
h
e
m
o
s
t
c
r
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c
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pr
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bl
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m
s
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c
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th
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de
x
[
19
]
.
3.
2.
2.
I
n
it
ial
iz
at
ion
T
h
e
i
ni
t
i
a
l
po
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o
ve
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p
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
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a
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J
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N:
2
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4
7
52
A
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br
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ly
algor
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pr
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nta
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e
planni
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pr
oblem
(
M
our
ad
B
ouatouche
)
409
3.
2.
3.
L
igh
t
i
n
t
e
n
s
it
y
T
h
e
l
i
g
h
t
i
n
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a
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f
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ly
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m
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d
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ly
x
by
us
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(
1)
.
3.
2.
4.
Dis
t
an
c
e
I
n
H
a
m
mi
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d
i
s
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n
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ly
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f
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t
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a
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r
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by
a
d
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s
t
a
nc
e
o
f
r
i
,j
.
,
=
_
(
,
)
(
2)
T
h
e
n
u
m
be
r
o
f
n
o
n
-
c
o
r
r
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s
p
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s
t
h
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Ha
mm
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d
i
s
t
a
nc
e
be
t
we
e
n
t
w
o
pe
r
m
ut
a
t
i
o
n
s
.
3.
2.
5.
At
t
r
ac
t
ivenes
s
A
f
i
r
e
f
ly
’
s
a
t
tr
a
c
t
i
v
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n
e
s
s
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s
de
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ne
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by
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s
b
r
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gh
t
n
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s
s
,
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t
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d
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n
i
t
s
o
bj
e
c
t
i
v
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f
u
n
c
t
i
o
n
.
I
n
(
3)
r
e
p
r
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s
e
n
t
s
i
t
.
(
)
=
−
2
(
3
)
I
n
t
hi
s
c
o
n
t
e
x
t
,
r
r
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t
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s
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n
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e
be
t
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t
w
o
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(
r
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s
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h
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ly
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x
t
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we
u
t
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e
(
3
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to
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m
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f
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ly
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s
,
f
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f
ly
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w
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t
to
f
i
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f
ly
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;
o
t
h
e
r
w
i
s
e
,
f
i
r
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f
ly
i
w
il
l
m
o
v
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r
a
n
do
m
ly
.
3.
2.
6.
L
igh
t
a
b
s
or
p
t
ion
T
h
e
li
g
h
t
a
b
s
o
r
pt
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o
n
c
o
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f
f
i
c
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e
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t
(
γ)
de
s
c
r
i
b
e
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h
o
w
a
f
i
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f
ly
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s
a
t
t
r
a
c
t
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n
v
a
r
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e
s
.
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t
s
v
a
l
ue
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s
c
r
uc
i
a
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s
t
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bli
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t
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pe
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d
a
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d
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f
t
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F
A
.
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n
pr
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n
c
i
p
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e
,
γ
∈
[
0,
N]
,
b
ut
i
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r
e
a
l
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t
y
,
γ
de
pe
n
d
s
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bl
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m
’
s
f
e
a
t
ur
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s
t
h
a
t
m
us
t
b
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s
o
l
v
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d.
3.
2.
7.
M
ove
m
e
n
t
W
h
e
n
o
n
e
b
r
i
g
h
t
e
r
f
i
r
e
f
ly
,
j
,
a
tt
r
a
c
t
s
a
n
ot
h
e
r
,
i
,
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t
s
m
o
v
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t
i
s
d
i
c
t
a
t
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d
by
(
4)
.
=
(
2
,
,
)
(
4)
W
h
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t
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d
i
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t
a
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c
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ij
b
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n
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li
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s
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a
n
d
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, a
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ly
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l
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h
w
il
l
b
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c
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o
s
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n
r
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do
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ly
f
r
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m
2
to
r
ij
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h
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s
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l
ut
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ns
t
h
a
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a
f
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ly
ha
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e
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i
nve
r
s
i
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n
m
ut
a
t
i
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pr
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t
,
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pe
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r
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pr
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t
a
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n
[
20
].
3.
2.
8.
I
n
ve
r
s
ion
o
p
e
r
at
or
An
i
nv
e
r
s
i
o
n
o
pe
r
a
to
r
i
s
e
m
p
l
o
y
e
d
to
ge
n
e
r
a
t
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n
e
w
f
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r
e
f
li
e
s
or
p
o
s
s
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bl
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s
o
l
ut
i
o
n
s
.
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n
t
h
e
b
e
g
i
nni
ng,
c
h
o
o
s
e
o
n
e
c
us
to
m
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r
(
C
1
)
a
t
r
a
n
do
m
f
r
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m
t
h
e
r
o
u
t
e
i
n
de
x
.
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x
t
,
c
h
oo
s
e
t
h
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s
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c
o
n
d
c
us
to
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(
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2
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b
y
a
dd
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t
h
e
m
o
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t
l
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n
gt
h
t
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f
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c
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n
i
nve
r
s
i
o
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pe
r
a
tor
c
a
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pr
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s
e
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s
he
d
a
s
s
h
o
wn
i
n
F
i
gur
e
1
[
20
].
F
i
gur
e
1.
I
n
v
e
r
s
i
o
n
o
pe
r
a
to
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
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5
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do
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3.
2.
9.
Gener
al
f
i
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f
l
y
a
l
go
r
it
h
m
m
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t
h
od
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hi
s
m
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t
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o
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a
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de
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m
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d
f
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a
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f
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f
ly
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li
g
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t
m
a
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d
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l
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to
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de
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f
f
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t
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p
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u
m
s
o
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ns
.
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h
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f
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f
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i
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o
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e
,
a
n
d
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t
w
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l
l
m
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v
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he
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t
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h
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di
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e
[
15]
.
T
h
e
i
nv
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r
s
i
o
n
m
ut
a
t
i
o
n
i
s
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a
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a
f
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ly
m
o
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s
r
a
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do
m
ly
.
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n
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h
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x
t
m
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t
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d
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j
us
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l
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t
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t
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i
t
y
.
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he
c
ur
r
e
n
t
s
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l
ut
i
o
n
i
s
i
t
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r
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t
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h
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wn
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n
Algo
r
i
t
hm
2
[
16]
.
Al
go
r
i
t
hm
2.
T
h
e
s
t
a
n
da
r
d
F
A
a
l
go
r
i
t
hm
f
o
r
s
a
l
e
s
r
e
pr
e
s
e
n
t
a
t
i
v
e
p
l
a
nni
ng
1:
Input
:
Problem instance ()
2:
Output
:
Optimal Route
S
∗
(
C
1
,
C
2
...
C
n
)
3: Initialize all the parameters
(
α
;
β
;
γ
;
n
)
4: Define size population,
number of iterations
5: Objective function
f
(
x
)
,
x
=(
x
1
,
x
2
,...,
x
d
)
T
6: Create a population of fireflies
x
i
(
i
=
1
,
2
,...,
n
)
7: Compute the light intensity Ii
8:
while
(
t
<
Max_iteration
)
do
9:
for
i
←
1 to
n
(all n fireflies)
do
10:
for
j
←
1 to
n
(all n fireflies)
do
11:
if
(
I
j
>
I
i
)
then
12 Determine the Hamming distance between x
i
and x
j
.
13:
Move firefly
x
i
towards
x
j
, using inversion mutation
14:
Update the position of
x
i
15:
Check the Feasibility of the solution
16:
end if
17:
Attractiveness depends on distance
r
via
e
−
γr
2
18:
Assess novel solutions and adjust the amount of light intensity.
19:
end for
20:
end for
21: Select the best
fireflies by ranking.
22:
end while
23: Sort the fireflies and select the best one
3.
3.
L
oc
al
s
e
a
r
c
h
T
o
i
m
pr
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v
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t
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f
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n
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t
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pe
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d
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h
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t
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Al
go
r
i
t
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3
[
21]
.
Al
go
r
i
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hm
3
.
T
h
e
l
o
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a
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a
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go
r
i
t
hm
1:
Input
:
Solution of firefly
2:
Output
:
Best
found solution
3:
while
termination criterion not reached
do
4:
2
-
Opt;
5:
Swap;
6:
Replace;
7:
Insert;
8:
end while
9: Return local optimum;
Evaluation Warning : The document was created with Spire.PDF for Python.
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1
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]
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T
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[2
2
]
–
[2
5]
.
W
hi
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pr
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[
2
]
,
[
3
]
.
Us
i
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g
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f
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f
ly
m
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go
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4
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Al
go
r
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4
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P
s
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r
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h
1:
Input
: Problem instance ()
2:
Output
: Optimal Route R
’
(C
1
, C
2
...C
n
)
3: F=−∞
4: R
’
= {};
5:
while
termination criterion not reached
do
6:
R
0
← Firely Method solution ();
7: R ← local search (R
0
);
8:
if
(F(R) > F)
then
9:
R
’
=R
10:
F=F(R)
11:
end if
12:
end while
13:
Return
R
’
4.
RE
S
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AN
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CU
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a
n
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R
A
M
.
4.
1.
B
e
n
c
h
m
ar
k
in
s
t
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c
e
s
T
h
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f
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l
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GVNS
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d
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B
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a
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o
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h
[
6
]
–
[
13]
.
R
i
g
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i
a
n
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S
a
l
a
ni
[
6
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(
c
100,
r
100,
a
n
d
r
c
100)
[
26]
.
4.
2.
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I
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4
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s
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e
4
(
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413
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F
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3.
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.
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a
ba
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e
,
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.
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e
le
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nd
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.
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r
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o
,
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y
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,
”
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N
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L
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ba
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e
,
R
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a
ns
in
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J
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l
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c
h
ov
s
ký, a
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R
. W
o
l
f
l
e
r
C
a
lv
o, “
T
he
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e
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m
o
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in
g
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e
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w
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h t
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s
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e
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s
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r
c
h,”
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ur
ope
an
J
our
nal
of
O
pe
r
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R
e
s
e
a
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c
h
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n,
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.
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n
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e
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gh
e
,
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nd
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.
V
a
n
O
udh
e
us
de
n,
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h
e
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ti
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o
ns
tr
a
in
t
t
e
a
m
o
r
i
e
nt
e
e
r
in
g
pr
o
bl
e
m
w
it
h
mul
ti
pl
e
ti
m
e
w
in
d
o
w
s
,”
T
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ti
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Sc
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ti
f
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l
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n
y
a
lg
o
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it
h
m
a
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h
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e
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e
a
m
or
ie
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te
e
r
in
g
pr
o
b
le
m
w
it
h
ti
m
e
w
in
d
o
w
s
,”
C
om
put
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r
s
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M
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B
o
ua
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K
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B
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di
,
“
G
R
A
S
P
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ta
bu
s
e
a
r
c
h
a
lg
o
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i
th
ms
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o
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th
e
r
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nni
ng
pr
o
bl
e
m
in
s
p
a
ti
a
l
c
r
o
w
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o
ur
c
in
g,”
I
nt
e
r
nat
io
nal
J
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nal
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A
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M
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e
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A
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r
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S
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H
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A
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d,
“
L
i
o
n
o
pt
im
i
z
a
ti
o
n
a
lg
o
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o
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nt
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bl
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m
w
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im
e
w
in
do
w
,”
I
ndone
s
ia
n
J
our
nal
of
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le
c
tr
ic
al
E
ngi
ne
e
r
in
g
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C
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Sc
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or
mul
ti
m
o
da
l
o
pt
im
i
z
a
ti
o
n,”
in
S
to
c
has
ti
c
A
lg
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it
hm
s
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F
oundati
ons
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A
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ic
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O
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a
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e
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n, E
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O
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na
ti
o
n
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di
r
e
c
ti
o
na
l
ov
e
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u
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nt
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g h
y
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id
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l
y
–
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n
e
ti
c
a
lg
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hm,”
E
ne
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gi
e
s
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“
A
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l
h
y
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id
f
ir
e
f
l
y
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lg
o
r
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h
m
f
o
r
gl
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ba
l
o
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z
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n,”
P
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N
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B
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S
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l
y
o
pt
im
i
z
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ti
o
n
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i
ng
a
r
ti
f
ic
i
a
l
im
mune
s
y
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te
m
f
o
r
f
e
a
tu
r
e
s
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e
t
s
e
l
e
c
ti
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n,”
I
nt
e
r
nat
io
nal
J
our
nal
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I
nt
e
ll
ig
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nt
E
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G
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K
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J
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ti
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“
E
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ti
o
na
r
y
di
s
c
r
e
te
f
ir
e
f
l
y
a
lg
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it
hm
f
o
r
tr
a
ve
ll
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g
s
a
le
s
ma
n
pr
o
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l
e
m,”
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A
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e
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e
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e
nt
Sy
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te
m
s
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. B
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u
c
ha
c
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, E
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S
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G
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K
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J
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t
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,
“
D
is
c
r
e
t
e
f
i
r
e
f
l
y
a
lg
o
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hm
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o
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t
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e
l
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s
a
le
s
ma
n
pr
o
bl
e
m,”
in
S
w
ar
m
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nt
e
ll
ig
e
nc
e
and
B
io
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I
ns
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r
e
d C
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io
n
, E
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e
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W
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S
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u
f
f
r
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u,
P
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V
a
ns
te
e
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g
e
n,
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.
V
a
nd
e
n
B
e
r
gh
e
,
a
nd
D
.
V
a
n
O
udhe
us
de
n,
“
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pa
th
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l
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ki
ng
a
ppr
o
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c
h
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o
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th
e
t
e
a
m
o
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e
nt
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e
r
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pr
o
b
le
m,”
C
om
put
e
r
s
&
O
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r
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c
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D
e
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c
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R
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m,
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B
e
lk
a
di
,
a
nd
A
.
E
l
M
ha
me
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,
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is
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r
e
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ti
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lg
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hm
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o
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a
r
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anadian
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our
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f
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le
c
t
r
ic
al
and
C
om
put
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r
E
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S
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J
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K
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S
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R
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C
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M
ur
th
y
,
“
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li
t
e
f
i
r
e
f
l
y
a
nd
lo
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h
o
r
t
t
e
r
m
m
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m
or
y
ba
s
e
d
m
o
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l
f
o
r
t
e
x
tu
r
e
c
la
s
s
if
i
c
a
ti
o
n,”
I
nt
e
r
nat
io
nal
J
our
nal
o
f
I
nt
e
ll
ig
e
nt
E
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ne
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r
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Sy
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H
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S
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C
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L
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P
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W
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H
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H
a
s
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“
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w
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p
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e
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di
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hm
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li
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ma
n
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r
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M
ul
ti
-
di
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ip
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T
r
e
nds
i
n A
r
ti
f
ic
ia
l
I
nt
e
ll
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e
nc
e
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A
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A
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M
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M
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h,
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.,
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in
ts
,”
O
pe
r
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io
ns
R
e
s
e
a
r
c
h
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o
. 2, pp. 254
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