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all
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tim
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1
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3
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d
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p
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6
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c
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as
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f
l
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[
8
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P
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ticle
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Op
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SO)
[
9
,
1
0
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A
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t
C
o
lo
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A
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[
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1
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Op
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1
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I
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v
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W
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Op
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[
1
3
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P
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Se
ar
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[
1
4
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ased
Op
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1
5
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An
tlio
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Op
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ith
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[
1
6
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an
d
C
at
S
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m
Op
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[
1
7
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.
I
n
th
is
w
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k
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t
wo
o
p
tim
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a
lg
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r
it
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m
s
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ap
p
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t
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ca
n
n
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ten
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a
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p
r
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m
s
;
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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10
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No
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2
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A
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0
2
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4
7
7
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1478
An
tlio
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Op
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m
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(
AL
O)
alg
o
r
ith
m
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1
8
]
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d
n
e
w
p
r
o
p
o
s
ed
h
y
b
r
id
o
p
ti
m
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r
ith
m
b
ased
o
n
Gr
ass
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p
p
er
Op
ti
m
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n
A
l
g
o
r
ith
m
(
GO
A
)
[
1
9
]
an
d
AL
O
.
T
h
e
r
est
o
f
th
is
p
ap
er
is
o
r
g
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f
o
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s
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n
s
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ti
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2
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p
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p
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3
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t
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ca
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s
is
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ted
.
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en
,
th
e
f
it
n
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s
s
f
u
n
c
tio
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a
n
d
th
e
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lt
s
w
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d
i
s
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s
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t
h
e
p
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clu
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ed
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n
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.
2.
T
H
E
P
RO
P
O
SE
D
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RID
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P
T
I
M
I
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A
T
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O
N
AL
G
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T
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M
2
.
1
.
T
he
t
heo
ry
o
f
t
he
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bri
d
m
et
ho
d
In
[
2
0
-
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,
w
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p
r
o
p
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t
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s
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Op
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(
GO
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[
1
9
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an
d
A
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Op
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m
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1
8
]
.
A
L
O
is
r
o
b
u
s
t
in
ex
p
lo
itatio
n
p
r
o
ce
s
s
,
w
h
ic
h
h
a
s
b
ee
n
p
r
o
v
ed
in
m
a
n
y
p
a
p
er
s
in
th
e
liter
at
u
r
e
lik
e
[
2
3
,
2
4
]
.
T
h
e
s
o
cial
f
o
r
ce
s
in
g
r
ass
h
o
p
p
er
’
s
s
w
ar
m
s
h
o
w
t
h
e
s
tr
o
n
g
ab
ilit
y
in
e
x
p
lo
r
atio
n
p
r
o
ce
s
s
all
o
v
er
th
e
s
ea
r
ch
s
p
ac
e.
So
,
th
e
s
e
f
ea
t
u
r
es
g
i
v
e
t
h
e
c
h
an
ce
to
co
m
b
i
n
e
t
h
e
s
e
t
w
o
m
et
h
o
d
s
,
GO
A
a
n
d
AL
O
,
in
a
n
o
v
el
h
y
b
r
id
m
et
h
o
d
,
to
m
ak
e
a
h
u
g
e
en
h
an
ce
m
e
n
t
o
n
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
ese
al
g
o
r
ith
m
s
.
T
h
e
b
en
ef
it
s
o
f
h
y
b
r
id
izatio
n
ca
n
b
e
r
e
p
r
esen
ted
in
o
v
er
co
m
i
n
g
t
h
e
m
ain
d
is
ad
v
a
n
ta
g
es
o
f
GO
A
a
n
d
AL
O
.
T
h
e
f
u
ll
d
ep
en
d
en
ce
o
n
r
o
u
lett
e
w
h
ee
l selec
t
io
n
m
eth
o
d
is
t
h
e
m
ai
n
d
is
ad
v
a
n
tag
e
i
n
AL
O,
s
in
ce
it
m
a
y
ca
u
s
e;
lo
s
s
o
f
d
iv
er
s
it
y
,
ea
r
l
y
co
n
v
er
g
en
ce
a
n
d
n
o
t
en
o
u
g
h
p
r
ess
u
r
e
to
s
elec
t
th
e
f
itte
s
t
s
ea
r
ch
a
g
en
t
s
a
m
o
n
g
s
a
m
e
f
it
n
es
s
s
ea
r
c
h
a
g
e
n
ts
.
W
h
er
ea
s
t
h
e
m
ai
n
d
r
a
w
b
ac
k
o
f
G
O
A
ex
i
s
ts
in
c
p
ar
a
m
e
ter
eq
u
atio
n
,
w
h
ic
h
w
ea
k
en
s
th
e
ex
p
lo
itatio
n
p
r
o
ce
s
s
in
t
h
e
alg
o
r
ith
m
.
Ou
r
p
r
o
p
o
s
ed
Hy
b
r
id
m
et
h
o
d
ca
n
ef
f
icie
n
tl
y
ex
p
lo
r
e
an
d
e
x
p
lo
it
th
e
w
h
o
le
s
ea
r
ch
s
p
ac
e
to
r
ea
ch
th
e
g
lo
b
al
o
p
ti
m
al
v
al
u
e
,
b
ec
au
s
e
o
f
m
er
g
i
n
g
th
e
f
ea
t
u
r
es
o
f
GO
A
an
d
AL
O
to
g
et
h
er
.
T
h
e
h
y
b
r
id
m
eth
o
d
h
as
s
o
m
e
f
ac
to
r
s
t
h
at
i
m
p
r
o
v
e
th
e
ab
ilit
y
o
f
e
x
p
lo
itatio
n
an
d
e
x
p
lo
r
atio
n
p
r
o
ce
s
s
es li
k
e:
t
h
e
p
o
p
u
latio
n
n
at
u
r
e
th
a
t
d
ec
r
ea
s
es
lo
ca
l
o
p
ti
m
a
s
tag
n
atio
n
,
th
e
attr
ac
tio
n
a
n
d
r
ep
u
ls
io
n
f
o
r
ce
s
i
n
GO
A
m
e
th
o
d
,
th
e
r
o
u
let
te
w
h
ee
l
s
elec
tio
n
m
et
h
o
d
an
d
r
an
d
o
m
w
al
k
s
in
AL
O
m
et
h
o
d
,
ch
o
o
s
in
g
d
i
f
f
er
e
n
t
s
a
m
p
le
s
o
f
le
s
s
an
d
av
er
a
g
e
f
itn
e
s
s
s
ea
r
ch
ag
e
n
t
s
f
r
o
m
n
e
x
t
p
o
s
itio
n
’
s
m
atr
i
x
,
an
d
t
h
e
m
o
d
if
icatio
n
s
o
f
c
p
ar
a
m
eter
i
n
GO
A
m
et
h
o
d
.
T
h
ese
f
ac
to
r
s
an
d
m
o
d
i
f
icatio
n
s
lead
to
b
etter
d
iv
er
s
it
y
o
f
t
h
e
s
ea
r
ch
ag
e
n
t
s
all
o
v
er
th
e
s
e
ar
ch
s
p
ac
e
an
d
h
i
g
h
p
r
o
b
a
b
ilit
y
f
o
r
lo
ca
l
o
p
ti
m
a
s
t
ag
n
at
io
n
a
v
o
id
an
ce
.
F
u
r
t
h
er
m
o
r
e,
th
e
in
te
n
s
it
y
o
f
s
ea
r
ch
a
g
en
ts
in
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
h
as
b
ee
n
r
ed
u
ce
d
r
ap
i
d
ly
co
m
p
ar
ed
w
ith
AL
O
a
n
d
GO
A
,
d
u
e
to
th
e
m
o
d
if
icatio
n
o
f
c
p
ar
am
eter
an
d
its
co
m
b
in
a
tio
n
w
it
h
o
th
er
s
h
r
in
k
i
n
g
f
ac
to
r
s
.
T
h
u
s
,
th
e
h
y
b
r
id
m
et
h
o
d
g
u
ar
a
n
tees
t
h
e
m
atu
r
e
an
d
f
as
t
co
n
v
er
g
e
n
ce
co
m
p
ar
ed
w
ith
AL
O
a
n
d
GO
A
[
2
2
]
.
T
h
e
f
o
llo
w
i
n
g
eq
u
atio
n
s
h
a
v
e
b
ee
n
u
s
ed
i
n
o
u
r
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
[
2
2
]
:
=
(
∑
−
2
(
|
−
|
)
=
1
≠
−
)
(
1
)
w
h
er
e
d
ef
in
es
th
e
n
e
x
t
p
o
s
iti
o
n
f
o
r
ℎ
g
r
as
s
h
o
p
p
er
an
d
ℎ
d
im
e
n
s
io
n
,
an
d
ar
e
th
e
u
p
p
er
an
d
lo
w
er
b
o
u
n
d
s
i
n
t
h
e
d
th
d
i
m
e
n
s
io
n
,
r
esp
ec
tiv
e
l
y
,
a
n
d
(
)
=
−
−
−
w
h
er
e
f
r
ep
r
esen
ts
t
h
e
i
n
ten
s
it
y
o
f
attr
ac
tio
n
f
o
r
ce
an
d
l
in
d
icate
s
th
e
attr
ac
tiv
e
le
n
g
t
h
s
ca
le
[
2
2
]
.
≤
(
)
/
2
=
−
−
(
2
)
(
2
)
>
2
=
(
−
+
1
)
×
(
×
10
)
(
3
)
w
h
er
e
an
d
ar
e
th
e
m
a
x
i
m
u
m
a
n
d
m
i
n
i
m
u
m
v
alu
e
s
,
r
esp
ec
t
iv
el
y
,
L
in
d
icate
s
th
e
m
ax
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
,
an
d
l
is
t
h
e
cu
r
r
en
t i
ter
atio
n
[
1
9
]
.
=
(
4
)
=
(
5
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Th
e
o
p
tima
l syn
th
esis
o
f sca
n
n
ed
lin
ea
r
a
n
ten
n
a
a
r
r
a
ys
(
A
n
a
s
A
A
ma
ir
eh
)
1479
w
h
er
e
an
d
in
d
icate
t
h
e
m
i
n
i
m
u
m
a
n
d
m
a
x
i
m
u
m
o
f
al
l
v
ar
iab
les
at
t
th
iter
atio
n
,
r
esp
ec
tiv
el
y
,
an
d
r
ep
r
esen
ts
a
r
atio
,
s
u
c
h
t
h
at
=
10
w
h
er
e
t
r
ep
r
esen
t
s
th
e
c
u
r
r
e
n
t
iter
atio
n
,
T
i
n
d
icate
s
t
h
e
m
ax
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
,
an
d
w
i
s
a
co
n
s
tan
t th
at
d
ep
en
d
s
o
n
t
h
e
cu
r
r
en
t iter
atio
n
[
1
8
]
.
2
.2
.
F
l
o
w
cha
r
t
o
f
t
he
hy
brid a
lg
o
rit
h
m
[
1
6
]
I
n
Fig
u
r
e
1
s
h
o
w
t
h
e
f
lo
w
c
h
ar
t
o
f
th
e
h
y
b
r
id
alg
o
r
it
h
m
[
1
6
]
.
Fig
u
r
e
1
.
F
lo
w
c
h
ar
t o
f
t
h
e
h
y
b
r
id
alg
o
r
ith
m
3.
G
E
O
M
E
T
RY
A
ND
P
RO
B
L
E
M
F
O
R
M
UL
AT
I
O
N
I
n
s
ca
n
n
ed
ar
r
a
y
an
te
n
n
as,
t
h
e
m
aj
o
r
lo
b
e
is
s
teer
ed
to
a
s
p
e
cif
ic
d
ir
ec
tio
n
,
w
h
ic
h
ca
n
b
e
ap
p
lied
in
w
id
e
ap
p
licatio
n
s
li
k
e
m
o
b
ile
an
d
ce
l
lu
lar
co
m
m
u
n
ica
tio
n
s
.
T
o
ac
co
m
p
lis
h
th
is
,
a
p
r
o
g
r
ess
i
v
e
p
h
ase
s
h
if
t
i
s
ad
d
ed
in
th
e
f
ee
d
i
n
g
c
u
r
r
en
t
s
[
2
5
]
.
Hen
ce
,
th
e
ar
r
a
y
f
ac
to
r
eq
u
atio
n
f
o
r
N
-
ele
m
en
t
s
ca
n
n
e
d
an
te
n
n
a
ar
r
a
y
t
h
at
lie
s
alo
n
g
th
e
x
-
ax
i
s
,
w
it
h
λ
/2
s
p
ac
in
g
b
et
w
ee
n
ad
j
ac
en
t e
lem
en
ts
,
ca
n
b
e
w
r
itte
n
as
f
o
llo
w
s
[
2
5
]
:
(
)
=
∑
e
xp
(
(
−
1
)
[
c
os
(
)
−
c
os
(
)
]
)
=
1
(
6
)
w
h
er
e
r
ep
r
esen
ts
th
e
e
x
cita
ti
o
n
a
m
p
lit
u
d
e
o
f
t
h
e
n
th
ele
m
e
n
t.
is
th
e
a
n
g
le
at
w
h
ic
h
t
h
e
m
aj
o
r
lo
b
e
is
s
teer
ed
.
T
h
e
g
eo
m
etr
y
o
f
s
u
c
h
a
s
ca
n
n
ed
ar
r
a
y
w
it
h
N
ele
m
e
n
ts
i
s
s
h
o
w
n
i
n
Fi
g
u
r
e
2
.
Fig
u
r
e
2
.
Geo
m
e
tr
y
o
f
N
-
ele
m
en
t
li
n
ea
r
ar
r
ay
T
h
e
m
ai
n
o
b
j
ec
tiv
e
in
t
h
is
p
a
p
er
is
to
m
i
n
i
m
ize
t
h
e
m
ax
i
m
u
m
s
id
e
lo
b
e
lev
el
(
S
L
L
)
w
ith
r
esp
ec
t
to
th
e
m
a
in
lo
b
e.
W
ith
t
h
e
p
u
r
p
o
s
e
o
f
ac
co
m
p
lis
h
i
n
g
th
i
s
g
o
al,
th
e
f
o
llo
w
i
n
g
f
i
tn
e
s
s
f
u
n
ctio
n
is
m
i
n
i
m
ized
[
2
4
]
:
=
ma
x
{
20
l
og
10
|
(
)
(
)
|
}
(
7
)
s
u
b
j
ec
t to
Î
[
0
,
]
(8
)
w
h
er
e
is
th
e
an
g
le
in
th
e
d
ir
ec
tio
n
o
f
th
e
m
aj
o
r
b
ea
m
.
[
0
,
]
r
ep
r
esen
t
s
th
e
s
id
e
lo
b
es
r
eg
io
n
th
at
d
ep
en
d
s
o
n
th
e
to
tal
n
u
m
b
er
o
f
th
e
ele
m
en
ts
.
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.
10
,
No
.
2
,
A
p
r
il 2
0
2
0
:
1
4
7
7
-
1484
1480
4.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
I
n
t
h
i
s
p
ap
er
,
th
r
ee
d
if
f
er
en
t
e
x
a
m
p
le
s
ar
e
co
n
s
id
er
ed
;
2
0
-
ele
m
en
t
L
AA
w
it
h
=
30
°
,
2
6
-
ele
m
e
n
t
L
AA
w
it
h
=
45
°
an
d
3
0
-
ele
m
e
n
t
L
AA
w
it
h
=
60
°
.
I
n
all
th
e
e
x
a
m
p
le
s
,
th
e
e
x
citatio
n
c
u
r
r
en
ts
ar
e
o
p
tim
ized
to
m
i
n
i
m
ize
th
e
s
id
e
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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1483
RE
F
E
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E
NC
E
S
[1
]
S
.
M
irj
a
li
li
,
“
M
o
th
-
f
lam
e
o
p
ti
m
i
z
a
ti
o
n
a
lg
o
rit
h
m
:
A
n
o
v
e
l
n
a
tu
re
-
in
sp
ired
h
e
u
rist
ic
p
a
ra
d
ig
m
,
”
K
n
o
wled
g
e
-
Ba
se
d
S
y
ste
ms
,
v
o
l.
8
9
,
p
p
.
2
2
8
-
2
4
9
,
2
0
1
5
.
[2
]
J.
C.
S
p
a
ll
,
In
tro
d
u
c
ti
o
n
t
o
sto
c
h
a
stic
se
a
rc
h
a
n
d
o
p
ti
m
iza
ti
o
n
:
e
stim
a
ti
o
n
,
si
m
u
latio
n
,
a
n
d
c
o
n
tr
o
l,
Jo
h
n
W
il
e
y
&
S
o
n
s
,
2
0
0
3
.
[3
]
H.
Ho
o
s,
T
.
S
tü
tzle
,
S
to
c
h
a
stic l
o
c
a
l
se
a
rc
h
:
f
o
u
n
d
a
ti
o
n
s &
a
p
p
li
c
a
ti
o
n
s,
El
se
v
ier
,
2
0
0
4
.
[4
]
El
-
G
h
a
z
a
li
Talb
i,
M
e
tah
e
u
rist
ics
:
F
ro
m
De
sig
n
to
Im
p
le
m
e
n
tatio
n
,
W
il
e
y
&
S
o
n
s,
2
0
0
9
.
[5
]
D.
K.
Ch
e
n
g
,
"
Op
ti
m
iza
ti
o
n
tec
h
n
iq
u
e
s
f
o
r
a
n
ten
n
a
a
rra
y
s,"
in
Pro
c
e
e
d
i
n
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o
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th
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IEE
E
,
v
o
l.
5
9
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n
o
.
1
2
,
p
p
.
1
6
6
4
-
1
6
7
4
,
De
c
.
1
9
7
1
.
[6
]
S
.
A
n
,
S
.
Ya
n
g
a
n
d
Z
.
Re
n
,
"
In
c
o
rp
o
ra
ti
n
g
L
ig
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t
Be
a
m
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rc
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In
ter
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L
in
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r
A
n
ten
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a
A
rra
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Op
ti
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iza
ti
o
n
,
"
in
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E
T
ra
n
sa
c
ti
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n
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M
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s
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0
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7
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.
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0
0
1
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0
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.
[7
]
A
.
S
h
a
ra
q
a
,
Bio
g
e
o
g
ra
p
h
y
-
Ba
se
d
o
p
ti
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iza
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n
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e
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o
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n
d
it
s
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p
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li
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ti
o
n
i
n
e
lec
tro
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a
g
n
e
ti
c
s,
M
a
ste
r
th
e
sis
,
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rd
a
n
U
n
iv
e
rsity
f
o
r
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
2
0
1
2
.
[8
]
B.
Ba
su
,
G
.
M
a
h
a
n
ti
,
“
F
ire
F
l
y
a
n
d
A
rti
f
i
c
ial
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e
s
Co
lo
n
y
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l
g
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rit
h
m
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y
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th
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e
d
a
n
d
Bro
a
d
si
d
e
L
in
e
a
r
A
rra
y
A
n
ten
n
a
,
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g
re
ss
In
El
e
c
tro
ma
g
n
e
ti
c
s R
e
se
a
rc
h
B
,
v
o
l.
3
2
,
p
p
.
1
6
9
-
1
9
0
,
2
0
1
1
.
[9
]
M
.
Ba
tain
e
h
,
J.
A
b
a
b
n
e
h
,
“
S
y
n
th
e
sis
o
f
a
p
e
rio
d
ic
li
n
e
a
r
p
h
a
se
d
a
n
ten
n
a
a
rra
y
s
u
sin
g
p
a
rti
c
le
s
w
a
r
m
o
p
ti
m
iza
ti
o
n
,
”
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e
c
tro
ma
g
n
e
ti
c
s
,
v
o
l.
2
6
,
n
o
7
,
p
p
.
5
3
1
-
5
4
1
,
2
0
0
6
.
[1
0
]
M
.
S
h
i
h
a
b
,
Y.
Na
jj
a
r,
N.
Dib
,
M
.
Kh
o
d
ier,
“
De
sig
n
o
f
n
o
n
–
u
n
if
o
rm
c
ircu
lar
a
n
ten
n
a
a
rra
y
s
u
sin
g
p
a
rti
c
le
s
w
a
r
m
o
p
ti
m
iza
ti
o
n
,
”
J
o
u
rn
a
l
o
f
El
e
c
tric
a
l
E
n
g
i
n
e
e
rin
g
,
v
o
l.
5
9
,
p
p
.
2
1
6
-
2
2
0
,
2
0
0
8
.
[1
1
]
E.
Ra
jo
-
Ig
les
ias
a
n
d
O.
Qu
e
v
e
d
o
-
T
e
ru
e
l,
"
L
in
e
a
r
a
rra
y
s
y
n
t
h
e
sis
u
sin
g
a
n
a
n
t
-
c
o
lo
n
y
-
o
p
ti
m
iz
a
ti
o
n
-
b
a
se
d
a
lg
o
rit
h
m
,
"
in
IEE
E
A
n
ten
n
a
s
a
n
d
Pro
p
a
g
a
t
io
n
M
a
g
a
zin
e
,
v
o
l.
4
9
,
n
o
.
2
,
p
p
.
7
0
-
7
9
,
A
p
ril
2
0
0
7
.
[1
2
]
N.
Dib
,
S
.
G
o
u
d
o
s,
H.
M
u
h
se
n
,
“
A
p
p
li
c
a
ti
o
n
o
f
T
a
g
u
c
h
i'
s
Op
ti
m
iz
a
t
io
n
M
e
th
o
d
a
n
d
S
e
lf
-
A
d
a
p
ti
v
e
Diffe
re
n
ti
a
l
Ev
o
lu
ti
o
n
to
th
e
S
y
n
th
e
sis
o
f
L
in
e
a
r
A
n
ten
n
a
A
rra
y
s,”
Pro
g
re
ss
in
El
e
c
tro
ma
g
n
e
t
ics
Res
e
a
rc
h
,
v
o
l.
1
0
2
,
p
p
.
1
5
9
-
1
8
0
,
2
0
1
0
.
[1
3
]
S
.
Ka
rimk
a
sh
i
a
n
d
A
.
A
.
Kish
k
,
"
In
v
a
siv
e
W
e
e
d
Op
ti
m
iza
ti
o
n
a
n
d
it
s
F
e
a
tu
re
s
in
El
e
c
tro
m
a
g
n
e
ti
c
s,"
in
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
An
ten
n
a
s a
n
d
Pr
o
p
a
g
a
ti
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n
,
v
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l
.
5
8
,
n
o
.
4
,
p
p
.
1
2
6
9
-
1
2
7
8
,
A
p
ril
2
0
1
0
.
[1
4
]
F
.
G
u
n
e
s,
F
.
T
o
k
a
n
,
“
P
a
tt
e
rn
se
a
rc
h
o
p
ti
m
iza
ti
o
n
w
it
h
a
p
p
li
c
a
ti
o
n
s
o
n
sy
n
th
e
sis
o
f
li
n
e
a
r
a
n
ten
n
a
a
rra
y
s,”
Exp
e
rt
S
y
ste
ms
wit
h
Ap
p
li
c
a
ti
o
n
s
,
v
o
l
.
3
7
,
p
p
.
4
6
98
-
4
7
0
5
,
2
0
1
0
.
[1
5
]
A
.
S
h
a
ra
q
a
a
n
d
N.
Dib
,
"
De
sig
n
o
f
li
n
e
a
r
a
n
d
c
ircu
lar an
ten
n
a
a
rra
y
s u
sin
g
b
i
o
g
e
o
g
ra
p
h
y
b
a
se
d
o
p
ti
m
iz
a
ti
o
n
,
"
2
0
1
1
IEE
E
J
o
rd
a
n
C
o
n
fer
e
n
c
e
o
n
Ap
p
li
e
d
El
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trica
l
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g
in
e
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rin
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a
n
d
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mp
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ti
n
g
T
e
c
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lo
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(
AE
ECT
)
,
Am
m
a
n
,
2
0
1
1
,
p
p
.
1
-
6.
[1
6
]
A
.
Am
a
ireh
,
N.
Dib
,
A
.
A
l
-
Zo
u
b
i
,
“
T
h
e
Op
ti
m
a
l
S
y
n
th
e
sis
o
f
Co
n
c
e
n
tri
c
El
li
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ti
c
a
l
A
n
ten
n
a
A
rr
a
y
s,
”
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ter
n
a
ti
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n
a
l
J
o
u
rn
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l
o
f
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tro
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9
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0
7
2
1
7
.
2
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9
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1
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6
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0
2
8
[1
7
]
L
.
P
a
p
p
u
la,
D.
G
h
o
sh
,
“
L
in
e
a
r
a
n
ten
n
a
a
rra
y
s
y
n
th
e
sis
u
sin
g
c
a
t
s
w
a
r
m
o
p
ti
m
iza
ti
o
n
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
tro
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ics
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n
d
C
o
mm
u
n
ica
ti
o
n
s
,
v
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l.
6
8
,
p
p
.
5
4
0
-
5
4
9
,
2
0
1
4
.
[1
8
]
S
.
M
irj
a
li
li
,
“
T
h
e
A
n
t
L
io
n
Op
t
imiz
e
r,
”
Ad
v
a
n
c
e
s in
En
g
in
e
e
rin
g
S
o
ft
wa
re
,
v
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l
.
8
3
,
p
p
.
8
0
-
9
8
,
2
0
1
5
.
[1
9
]
S
.
S
a
re
m
i,
S
.
M
irj
a
li
li
,
A
.
L
e
w
i
s,
“
G
ra
ss
h
o
p
p
e
r
Op
ti
m
isa
ti
o
n
A
l
g
o
rit
h
m
:
T
h
e
o
ry
a
n
d
a
p
p
li
c
a
ti
o
n
,
”
Ad
v
a
n
c
e
s
in
En
g
i
n
e
e
rin
g
S
o
ft
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re
,
v
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l
.
1
0
5
,
p
p
.
3
0
-
4
7
,
2
0
1
7
.
[2
0
]
A
.
Am
a
ireh
,
A
.
A
l
-
Zo
u
b
i,
N.
Dib
,
“
A
Ne
w
H
y
b
rid
Op
ti
m
iz
a
ti
o
n
Tec
h
n
iq
u
e
Ba
se
d
o
n
A
n
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a
n
d
G
ra
ss
h
o
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r
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ti
m
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n
A
lg
o
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h
m
s,”
S
u
b
mitt
e
d
to
Ro
y
a
l
S
o
c
iety
O
p
e
n
S
c
ien
c
e
,
2
0
1
9
.
[2
1
]
A
.
Am
a
ireh
,
Co
m
p
a
ra
ti
v
e
S
tu
d
y
o
f
Ne
w
Op
ti
m
iza
ti
o
n
M
e
th
o
d
s
a
n
d
T
h
e
ir
A
p
p
li
c
a
ti
o
n
s
in
El
e
c
tr
o
m
a
g
n
e
ti
c
s,
Irb
id
:
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ste
r T
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sis
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rm
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0
1
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2
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3
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ter
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0
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IEE
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ti
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sto
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stic a
lg
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h
m
s a
n
d
o
p
ti
m
iza
ti
o
n
tec
h
n
i
q
u
e
s.
Evaluation Warning : The document was created with Spire.PDF for Python.
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Un
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Evaluation Warning : The document was created with Spire.PDF for Python.