I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
8
,
No
.
3
,
J
u
n
e
201
8
,
p
p
.
1
2
9
7
~
1
3
0
4
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v8
i
3
.
p
p
1
2
9
7
-
1304
1297
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
co
r
e
.
co
m/
jo
u
r
n
a
ls
/in
d
ex
.
p
h
p
/
I
JE
C
E
Eco
no
m
ic and
E
m
iss
io
n Dis
pa
tch
using
Whale
O
pti
m
i
z
a
tion
Alg
o
rith
m
(
WO
A
)
F
a
s
ee
la
C.
K
.
1
,
H
.
Vennila
2
1
De
p
a
rte
m
e
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
,
M
ES
C
o
ll
e
g
e
o
f
En
g
in
e
e
rin
g
&
Tec
h
n
o
lo
g
y
,
Ke
r
a
la
,
In
d
ia
2
De
p
a
rte
m
e
n
t
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
No
o
ru
l
Isla
m
Co
ll
e
g
e
o
f
E
n
g
in
e
e
rin
g
,
Na
g
e
rc
o
il
,
T
N
,
In
d
ia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Dec
2
3
,
2
0
1
7
R
ev
i
s
ed
Feb
2
7
,
2
0
1
8
A
cc
ep
ted
Ma
r
1
6
,
2
0
1
8
T
h
is
p
a
p
e
r
w
o
rk
p
re
se
n
t
o
n
e
o
f
th
e
late
st
m
e
ta
h
e
u
risti
c
o
p
ti
m
iza
ti
o
n
a
p
p
ro
a
c
h
e
s
n
a
m
e
d
w
h
a
le
o
p
ti
m
iz
a
ti
o
n
a
lg
o
rit
h
m
a
s
a
n
e
w
a
lg
o
rit
h
m
d
e
v
e
lo
p
e
d
to
s
o
lv
e
th
e
e
c
o
n
o
m
ic
d
isp
a
tch
p
ro
b
lem
.
T
h
e
e
x
e
c
u
ti
o
n
o
f
th
e
u
ti
li
z
e
d
a
lg
o
rit
h
m
is
a
n
a
l
y
z
e
d
u
sin
g
sta
n
d
a
rd
tes
t
s
y
ste
m
o
f
IEE
E
30
b
u
s
sy
ste
m
.
T
h
e
p
ro
p
o
se
d
a
lg
o
rit
h
m
d
e
li
v
e
re
d
o
p
ti
m
u
m
o
r
n
e
a
r
o
p
ti
m
u
m
so
lu
ti
o
n
s.
F
u
e
l
c
o
st
a
n
d
e
m
issio
n
c
o
sts
a
re
c
o
n
sid
e
re
d
to
g
e
th
e
r
to
g
e
t
b
e
tt
e
r
re
su
lt
f
o
r
e
c
o
n
o
m
ic
d
isp
a
tch
.
T
h
e
a
n
a
ly
sis
sh
o
w
s
g
o
o
d
c
o
n
v
e
rg
e
n
c
e
p
ro
p
e
rty
f
o
r
W
O
A
a
n
d
p
r
o
v
id
e
s
b
e
tt
e
r
re
s
u
lt
s
in
c
o
m
p
a
riso
n
w
it
h
P
S
O.
T
h
e
a
c
h
iev
e
d
re
su
lt
s
in
t
h
is
st
u
d
y
u
sin
g
th
e
a
b
o
v
e
-
m
e
n
ti
o
n
e
d
a
lg
o
ri
th
m
h
a
v
e
b
e
e
n
c
o
m
p
a
re
d
w
it
h
o
b
tain
e
d
re
su
lt
s
u
sin
g
o
t
h
e
r
in
telli
g
e
n
t
m
e
th
o
d
s
su
c
h
a
s
p
a
rti
c
le
sw
a
r
m
Op
ti
m
iza
ti
o
n
.
Th
e
o
v
e
ra
ll
p
e
rf
o
rm
a
n
c
e
o
f
th
is
a
lg
o
rit
h
m
c
o
ll
a
tes
w
it
h
e
a
rl
y
p
ro
v
e
n
o
p
ti
m
iza
ti
o
n
m
e
th
o
d
o
lo
g
y
,
P
a
rti
c
l
e
S
w
a
r
m
Op
ti
m
iza
ti
o
n
(
P
S
O).
T
h
e
m
in
im
u
m
c
o
st
f
o
r
th
e
g
e
n
e
ra
ti
o
n
o
f
u
n
it
s
is
o
b
tai
n
e
d
f
o
r
th
e
sta
n
d
a
rd
b
u
s sy
st
e
m
.
K
ey
w
o
r
d
:
E
co
n
o
m
ic
d
is
p
atch
E
m
is
s
io
n
co
s
t
Fu
el
co
s
t
Op
ti
m
u
m
P
ar
ticle
s
w
a
n
o
p
ti
m
izatio
n
W
h
ale
o
p
ti
m
izatio
n
alg
o
r
it
h
m
Co
p
y
rig
h
t
©
2
0
1
8
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Fas
ee
la
C
.
K
.
,
Dep
ar
te
m
en
t o
f
E
lectr
ical
an
d
E
lectr
o
n
ics E
n
g
i
n
ee
r
in
g
,
ME
S C
o
lleg
e
o
f
E
n
g
i
n
ee
r
in
g
&
T
ec
h
n
o
lo
g
y
,
Ker
ala,
I
n
d
ia
.
E
m
ail:
f
aseelac
k
6
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
I
n
to
d
ay
‟
s
w
o
r
ld
,
it‟s
al
w
a
y
s
b
ee
n
a
co
n
ce
r
n
f
o
r
an
en
g
i
n
e
er
to
g
et
a
p
r
o
d
u
ct
o
u
t
at
a
v
er
y
o
p
ti
m
al
co
s
t
b
y
m
in
i
m
iz
in
g
b
o
th
t
h
e
p
r
o
d
u
ct
o
p
er
atin
g
co
s
t
a
n
d
r
a
w
m
ater
ial
i
n
p
u
t
to
th
e
p
r
o
d
u
cti
o
n
u
n
it.
E
co
n
o
m
ic
L
o
ad
Dis
p
atch
(
E
L
D)
d
ea
ls
w
it
h
t
h
e
s
a
m
e
s
it
u
atio
n
a
n
d
it
w
o
r
k
s
o
n
o
p
er
atin
g
a
co
o
r
d
i
n
ated
p
o
w
er
s
y
s
te
m
s
u
c
h
th
a
t
th
e
lo
w
es
t
o
p
er
atin
g
co
s
t
g
en
er
ato
r
s
ar
e
u
s
ed
to
t
h
e
g
r
ea
tes
t
ex
te
n
t
a
n
d
th
e
h
ig
h
est
o
p
er
atin
g
co
s
t
g
en
er
ato
r
is
u
s
ed
to
th
e
lo
w
e
s
t
ex
ten
t.
E
co
n
o
m
ic
lo
ad
d
is
p
atch
p
r
o
b
lem
is
a
co
n
s
tr
ai
n
e
d
p
r
o
b
lem
,
s
e
v
er
al
p
r
in
cip
les
an
d
s
tr
ateg
ie
s
ar
e
al
r
ea
d
y
b
ei
n
g
d
e
v
elo
p
ed
to
s
o
lv
e
th
ese
p
r
o
b
le
m
s
.
E
L
D
h
a
s
b
ec
o
m
e
an
i
m
p
o
r
tan
t
f
u
n
d
a
m
en
ta
l f
u
n
c
tio
n
i
n
o
p
er
atio
n
an
d
co
n
tr
o
l o
f
t
h
e
p
o
w
er
s
y
s
te
m
.
T
h
e
d
em
a
n
d
f
o
r
elec
tr
icit
y
is
in
cr
ea
s
i
n
g
in
a
lar
g
e
f
ac
to
r
in
to
d
a
y
‟
s
li
f
e,
w
h
ic
h
m
a
k
e
s
it
h
i
g
h
l
y
cr
u
cial
to
r
u
n
g
e
n
er
ato
r
s
at
v
er
y
m
in
i
m
al
co
s
t.
T
h
is
i
s
th
e
m
a
in
f
ac
to
r
o
f
an
E
co
n
o
m
ic
d
is
p
atch
p
r
o
b
lem
.
W
it
h
th
e
u
n
ex
ce
p
tio
n
al
p
r
o
d
u
ctio
n
o
f
ca
r
b
o
n
e
m
i
s
s
io
n
s
in
t
h
er
m
a
l p
o
w
er
p
la
n
t,
its
n
ee
d
ed
to
o
p
ti
m
ize
t
h
e
e
m
is
s
io
n
to
g
eth
e
r
w
i
th
th
e
o
p
ti
m
izat
io
n
o
f
co
s
t
w
h
ic
h
ac
ts
as
t
w
o
v
ital
p
ar
ts
o
f
E
co
n
o
m
ic
d
is
p
atch
p
r
o
b
le
m
.
T
h
e
ec
o
n
o
m
ic
d
is
p
atc
h
s
o
lu
tio
n
p
r
o
v
id
es
th
e
b
est
m
i
n
i
m
u
m
c
o
s
t
o
f
f
u
el
a
n
d
e
m
i
s
s
io
n
.
T
h
is
i
n
d
ir
ec
tl
y
m
a
k
es
lo
w
er
co
s
t
f
o
r
elec
tr
icit
y
an
d
m
ak
e
s
elec
tr
ical
u
til
ities
m
o
r
e
co
m
p
etiti
v
e
in
t
h
e
m
ar
k
et.
As
th
e
en
er
g
y
ca
n
n
o
t
b
e
s
to
r
ed
,
it
r
eq
u
ir
es
h
i
g
h
l
y
ef
f
icie
n
t
es
ti
m
atio
n
s
ce
n
ar
io
s
in
clu
d
in
g
tr
a
n
s
m
i
s
s
io
n
an
d
d
is
tr
ib
u
tio
n
s
y
s
te
m
s
t
o
m
ak
e
t
h
e
s
a
m
e
w
o
r
k
e
f
f
ec
ti
v
el
y
.
Var
io
u
s
tech
n
o
lo
g
ie
s
h
a
v
e
b
ee
n
in
tr
o
d
u
ce
d
to
s
o
lv
e
th
e
o
p
t
i
m
izat
io
n
o
f
E
co
n
o
m
ic
L
o
ad
Dis
p
atch
p
r
o
b
lem
s
.
T
h
e
s
elec
tio
n
o
f
th
e
o
p
ti
m
izatio
n
al
g
o
r
ith
m
is
th
e
i
m
p
o
r
tan
t
p
ar
t
o
f
th
e
p
r
o
b
lem
in
v
o
lv
i
n
g
ec
o
n
o
m
ic
d
is
p
atc
h
.
T
h
e
E
DP
is
d
ev
elo
p
ed
b
ased
o
n
r
ea
l
-
v
a
lu
ed
co
d
if
icatio
n
.
I
n
m
o
d
er
n
m
et
h
o
d
o
lo
g
y
o
n
l
y
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.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
2
9
7
–
1304
1298
th
e
co
s
t
f
u
n
ctio
n
is
ev
a
lu
ated
an
d
a
g
lo
b
al
m
i
n
i
m
u
m
s
o
lu
tio
n
is
co
m
p
u
ted
,
in
d
ep
en
d
en
tl
y
o
f
th
e
co
s
t
f
u
n
ctio
n
.
T
h
e
u
s
e
o
f
d
ig
ita
l c
o
m
p
u
ter
s
f
o
r
o
b
tain
in
g
lo
ad
in
g
s
c
h
ed
u
le
s
w
er
e
in
v
e
s
tig
a
ted
an
d
u
s
ed
to
d
ay
.
Ma
n
y
d
eter
m
i
n
is
tic
o
p
ti
m
izat
io
n
ap
p
r
o
ac
h
es
w
er
e
p
r
o
p
o
s
ed
to
s
o
lv
e
t
h
e
E
L
D
p
r
o
b
lem
,
in
cl
u
d
in
g
la
m
b
d
a
iter
atio
n
m
eth
o
d
[
1
]
,
g
r
ad
ien
t
m
et
h
o
d
,
lin
ea
r
p
r
o
g
r
a
m
m
in
g
[
4
]
,
n
o
n
-
li
n
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
[
2
]
an
d
q
u
ad
r
atic
p
r
o
g
r
am
m
i
n
g
[
1
4
]
.
B
u
t
th
e
s
e
m
et
h
o
d
s
r
eq
u
ir
e
en
o
r
m
o
u
s
ef
f
o
r
ts
in
ter
m
s
o
f
co
m
p
u
tatio
n
.
Du
e
to
co
m
p
l
ex
ities
o
f
co
m
p
u
tin
g
,
th
er
e
f
o
r
e
ef
f
icien
t
alg
o
r
ith
m
to
f
i
n
d
o
p
ti
m
al
s
o
l
u
tio
n
lik
e
g
en
et
ic
alg
o
r
it
h
m
[
1
6
]
,
[
1
8
]
,
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
[
5
]
,
ev
o
lu
tio
n
ar
y
p
r
o
g
r
a
m
m
in
g
,
ar
ti
f
icia
l
b
ee
co
lo
n
y
o
p
ti
m
izat
io
n
[
9
]
,
[
1
0
]
,
an
d
b
io
g
eo
g
r
ap
h
y
b
ased
o
p
tim
izatio
n
;
b
a
cter
ial
f
o
r
ag
i
n
g
an
d
also
th
eir
v
ar
ia
n
t
s
ca
m
e
i
n
to
i
m
p
le
m
en
t.
B
io
-
i
n
s
p
ir
ed
m
e
ta
-
h
e
u
r
is
tic
alg
o
r
it
h
m
s
h
a
v
e
r
ec
e
n
tl
y
s
h
o
w
n
th
e
e
ff
ic
ien
c
y
in
d
e
a
li
n
g
w
it
h
m
a
n
y
n
o
n
li
n
e
a
r
o
p
ti
m
iza
ti
o
n
s co
n
stra
in
e
d
p
r
o
b
lem
s f
o
r
f
in
d
in
g
th
e
o
p
ti
m
a
l
so
lu
ti
o
n
.
R
ec
en
t
l
y
a
n
at
u
r
e
b
ased
o
p
ti
m
izatio
n
tec
h
n
iq
u
e
w
h
ale
o
p
ti
m
izatio
n
A
l
g
o
r
ith
m
(
W
O
A
)
i
s
d
ev
elo
p
ed
b
ased
o
n
th
e
f
la
s
h
in
g
b
e
h
av
i
o
r
o
f
W
h
ales.
W
O
A
d
ev
elo
p
ed
an
d
is
u
s
ed
to
s
o
lv
e
co
n
s
t
r
ain
ed
en
g
i
n
ee
r
in
g
p
r
o
b
lem
s
.
U
n
til
n
o
w
m
a
n
y
r
es
ea
r
ch
es
h
a
v
e
b
ee
n
ca
r
r
ied
o
u
t
to
f
in
d
t
h
e
clo
s
est
o
p
ti
m
u
m
r
e
s
u
lt
i
n
d
eter
m
in
i
n
g
th
e
p
o
w
er
g
e
n
er
atio
n
o
f
ea
ch
g
en
er
ato
r
u
s
i
n
g
W
O
A
an
d
i
t
w
a
s
i
n
f
er
r
ed
t
h
at
t
h
e
W
O
A
i
s
m
o
r
e
r
o
b
u
s
t
a
n
d
ef
f
icien
t in
d
eter
m
in
i
n
g
th
e
o
p
ti
m
al
lo
ad
s
c
h
ed
u
li
n
g
.
2.
P
RO
B
L
E
M
F
O
R
M
UL
AT
I
O
N
T
h
e
g
en
er
atin
g
u
n
i
ts
ar
e
lo
ad
ed
ec
o
n
o
m
ica
ll
y
s
u
c
h
a
w
a
y
to
r
ed
u
ce
th
e
o
p
er
atin
g
co
s
t.
C
o
n
s
id
er
in
g
th
e
v
al
v
e
p
o
in
t e
f
f
ec
t th
e
ec
o
n
o
m
ic
d
is
p
atch
f
o
r
m
u
lated
th
e
o
b
j
ec
tiv
e
f
u
n
c
tio
n
as
g
i
v
en
b
el
o
w
(
1
)
W
h
er
e
ar
e
th
e
f
u
el
co
s
t c
o
ef
f
icie
n
ts
o
f
g
e
n
er
ato
r
is
th
e
p
o
w
er
g
en
er
ated
b
y
u
n
i
t
, MW
is
th
e
f
u
el
co
s
t f
u
n
c
tio
n
o
f
u
n
i
t
T
h
e
to
tal
f
u
el
co
s
t
f
o
r
th
e
en
tir
e
s
y
s
te
m
o
f
N
g
e
n
er
ato
r
s
ca
n
t
h
en
b
e
ca
lcu
lated
as,
(
2
)
T
h
e
n
e
w
o
b
j
ec
tiv
e
f
u
n
c
tio
n
b
y
co
n
s
id
er
in
g
v
al
v
e
p
o
in
t lo
ad
i
n
g
alo
n
g
w
i
th
to
tal
f
u
el
co
s
t b
ec
o
m
e
s
,
(
3
)
W
h
er
e
an
d
ar
e
th
e
f
u
e
l c
o
s
t c
o
ef
f
ici
en
ts
o
f
g
e
n
er
ato
r
is
th
e
p
o
w
er
g
en
er
ated
b
y
u
n
i
t
, MW
i is th
e
n
u
m
b
er
o
f
g
e
n
er
ati
n
g
u
n
its
is
th
e
m
i
n
i
m
u
m
g
en
er
atio
n
li
m
it o
f
u
n
it
, MW
is
th
e
f
u
el
co
s
t f
u
n
c
tio
n
o
f
u
n
i
t
is
th
e
to
tal
f
u
el
co
s
t,
$
/h
r
.
I
n
o
r
d
er
to
m
in
i
m
ize
th
e
p
o
llu
tan
ts
,
e
m
i
s
s
io
n
is
co
n
s
ier
ed
alo
n
g
w
it
h
ec
o
n
o
m
ic
d
is
p
atc
h
.
T
h
e
g
en
er
ato
r
ca
n
b
e
m
o
d
elled
as
h
av
in
g
a
q
u
ad
r
atic
r
elatio
n
b
et
w
ee
n
t
h
e
a
m
o
u
n
t
o
f
p
o
ll
u
tan
ts
r
elea
s
ed
an
d
th
e
p
o
w
er
g
en
er
ated
.
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
E
co
n
o
mic
a
n
d
E
mis
s
io
n
Dis
p
a
tch
u
s
in
g
W
h
a
le
Op
timiz
a
tio
n
A
lg
o
r
ith
m
(
W
OA
)
(
F
a
s
ee
la
C
.
K
.
)
1299
T
h
e
m
at
h
e
m
atica
l
f
o
r
m
u
latio
n
f
o
r
g
en
er
ato
r
is
g
iv
e
n
b
y
,
(
4
)
W
h
er
e
ar
e
th
e
e
m
i
s
s
io
n
co
ef
f
icien
ts
o
f
g
e
n
er
ato
r
is
th
e
p
o
w
er
g
en
er
ated
b
y
u
n
i
t
, MW
is
th
e
f
u
el
co
s
t f
u
n
c
tio
n
o
f
u
n
i
t
T
h
e
to
tal
em
i
s
s
io
n
f
o
r
th
e
e
n
ti
r
e
s
y
s
te
m
o
f
N
g
e
n
er
ato
r
s
ca
n
th
en
b
e
ca
lc
u
lated
as,
(
5
)
T
h
e
n
e
w
E
m
is
s
io
n
f
u
n
ct
io
n
b
ec
o
m
e
s
,
(
6
)
W
h
er
e
an
d
ar
e
th
e
e
m
is
s
io
n
co
e
f
f
ici
en
ts
o
f
g
e
n
er
ato
r
is
th
e
p
o
w
er
g
e
n
er
ated
b
y
u
n
it
, MW
is
th
e
n
u
m
b
er
o
f
g
e
n
er
ati
n
g
u
n
its
is
th
e
e
m
is
s
io
n
f
u
n
ct
ieo
n
o
f
u
n
it
,
is
th
e
to
tal
e
m
is
s
io
n
,
to
n
/h
r
.
T
h
e
p
o
w
er
b
alan
ce
eq
u
atio
n
[
3
]
is
g
i
v
en
b
y
(
7
)
A
d
d
in
g
lo
s
s
f
ac
to
r
to
th
e
eq
u
a
tio
n
.
(
8
)
P
o
w
er
lo
s
s
P
L
is
ca
lc
u
lated
as
(
9
)
T
h
e
ac
tu
al
p
o
w
er
g
e
n
er
atio
n
f
o
r
th
e
g
en
er
ato
r
w
ill b
e
b
et
w
e
en
its
m
a
x
i
m
u
m
a
n
d
m
i
n
i
m
u
m
li
m
it
s
w
h
ich
i
s
r
ep
r
esen
ted
as
(
1
0
)
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.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
2
9
7
–
1304
1
300
3.
WH
AL
E
O
P
T
I
M
I
Z
A
T
I
O
N
AL
G
O
RI
T
H
M
I
n
T
h
e
m
a
th
e
m
atica
l
m
o
d
el
o
f
w
h
ale
o
p
ti
m
izat
io
n
[
6
]
is
d
etailed
in
th
i
s
s
ec
tio
n
.
3
.
1
.
I
ns
pira
t
io
n
T
h
e
h
u
n
ti
n
g
m
et
h
o
d
o
f
h
u
m
p
b
ac
k
w
h
ales,
k
n
o
w
n
a
s
b
u
b
b
le
-
n
e
t
-
f
ee
d
i
n
g
,
is
u
s
ed
as
an
in
s
p
ir
atio
n
to
cr
ea
te
th
i
s
al
g
o
r
ith
m
.
T
h
is
m
eth
o
d
h
i
g
h
li
g
h
t
s
t
h
e
in
te
lli
g
e
n
ce
a
n
d
co
r
p
o
r
atio
n
o
f
h
u
m
p
b
ac
k
w
h
ale
s
.
T
h
e
h
u
m
p
b
ac
k
w
h
ale
s
h
u
n
t
i
n
g
r
o
u
p
s
(
th
e
g
r
o
u
p
s
ize
m
a
y
b
e
as
h
ig
h
a
s
a
d
o
ze
n
o
f
h
u
m
p
b
ac
k
w
h
ales)
a
n
d
th
e
b
i
g
w
h
ale
w
h
ic
h
is
th
e
lead
er
f
i
n
d
s
th
e
g
r
o
u
p
o
f
f
i
s
h
e
s
w
h
ich
is
to
b
e
h
u
n
ted
.
T
h
e
g
r
o
u
p
g
o
es
u
n
d
er
n
ea
th
th
e
w
ater
a
n
d
th
e
lead
w
h
ale,
w
h
o
is
th
e
b
u
b
b
le
b
lo
w
er
,
p
r
o
d
u
ce
s
s
p
ir
al
b
ig
b
u
b
b
les
to
w
ar
d
s
th
e
s
u
r
f
ac
e
o
f
th
e
w
ater
.
T
h
ese
s
p
ir
al
b
u
b
b
les
(
s
h
ap
e
o
f
„
9
‟
)
in
ter
r
u
p
t
t
h
e
f
is
h
es
to
s
w
i
m
th
r
o
u
g
h
th
e
s
a
m
e
an
d
th
e
y
g
o
t
s
tr
u
c
k
u
p
w
i
th
i
n
t
h
e
s
p
ir
al
b
u
b
b
le.
No
w
t
h
e
g
r
o
u
p
o
f
w
h
a
les
co
m
es
o
u
t
o
f
th
e
w
ater
w
it
h
th
e
o
p
en
m
o
u
th
to
w
ar
d
s
th
e
w
ater
s
u
r
f
ac
e,
w
it
h
i
n
t
h
e
s
p
ir
al
b
u
b
b
le,
m
o
v
i
n
g
s
y
n
c
h
r
o
n
o
u
s
l
y
a
n
d
h
u
n
ti
n
g
all
t
h
e
f
i
s
h
es
w
it
h
i
n
t
h
e
s
p
ir
a
l
b
u
b
b
le.
A
ll
th
e
w
h
a
les
f
o
r
m
s
ex
ac
tl
y
t
h
e
s
a
m
e
p
o
s
itio
n
w
i
th
r
esp
ec
t
to
th
e
lead
w
h
ale
w
h
ile
h
u
n
ti
n
g
.
T
h
e
b
u
b
b
le
-
n
et
f
ee
d
i
n
g
is
a
u
n
iq
u
e
b
eh
av
io
r
t
h
at
ca
n
o
n
l
y
b
e
o
b
s
er
v
ed
in
h
u
m
p
b
ac
k
w
h
ale
s
.
I
n
t
h
is
w
o
r
k
,
th
e
s
p
ir
al
b
u
b
b
le
-
n
et
f
ee
d
in
g
m
an
eu
v
er
is
m
at
h
e
m
atica
ll
y
m
o
d
e
led
in
o
r
d
er
to
p
er
f
o
r
m
o
p
ti
m
i
za
tio
n
.
3
.
2
.
M
a
t
he
m
a
t
ica
l
m
o
del a
nd
o
p
t
i
m
iza
t
io
n a
lg
o
rit
h
m
I
n
th
i
s
s
ec
tio
n
,
th
e
m
at
h
e
m
a
ti
ca
l
m
o
d
el
o
f
co
m
p
lete
b
eh
a
v
i
o
r
o
f
h
u
m
p
b
ac
k
-
w
h
ale
-
h
u
n
t
in
g
is
d
o
n
e
w
h
ic
h
in
cl
u
d
es
s
ea
r
ch
f
o
r
s
m
all
f
is
h
es,
en
cir
cli
n
g
p
r
ey
a
n
d
s
p
ir
al
b
u
b
b
le
-
n
et
f
ee
d
in
g
m
a
n
eu
v
er
.
T
h
e
W
h
ale
Op
ti
m
izatio
n
A
l
g
o
r
ith
m
(
W
OA
)
is
t
h
e
n
p
r
o
p
o
s
ed
.
3
.
2
.
1
.
E
ncircli
ng
prey
Hu
m
p
b
ac
k
w
h
ales
ca
n
r
ec
o
g
n
ize
t
h
e
lo
ca
tio
n
o
f
p
r
e
y
a
n
d
en
cir
cle
t
h
e
m
.
Si
n
ce
t
h
e
p
o
s
it
io
n
o
f
th
e
o
p
tim
a
l
d
esi
g
n
i
n
t
h
e
s
ea
r
ch
s
p
ac
e
is
n
o
t
k
n
o
w
n
a
p
r
io
r
i,
th
e
W
OA
al
g
o
r
ith
m
a
s
s
u
m
es
th
at
th
e
cu
r
r
e
n
t
b
es
t
ca
n
d
id
ate
s
o
lu
tio
n
is
t
h
e
tar
g
et
p
r
ey
o
r
is
clo
s
e
to
t
h
e
o
p
ti
m
u
m
.
Af
ter
th
e
b
est
s
ea
r
ch
ag
en
t
i
s
d
ef
in
ed
,
th
e
o
th
er
s
ea
r
ch
ag
e
n
t
s
w
il
l
h
en
c
e
tr
y
to
u
p
d
ate
t
h
eir
p
o
s
itio
n
s
to
w
ar
d
s
th
e
b
est
s
ea
r
ch
a
g
e
n
t.
T
h
is
b
eh
a
v
io
r
is
r
ep
r
esen
ted
b
y
t
h
e
f
o
llo
w
i
n
g
e
q
u
atio
n
s
:
(
1
1
)
(
1
2
)
w
h
er
e
„
t
‟
in
d
icate
s
th
e
c
u
r
r
en
t iter
ati
o
n
,
„
A‟
an
d
„
C
‟
ar
e
co
ef
f
ic
ien
t
v
e
cto
r
s
,
X
∗
is
t
h
e
p
o
s
itio
n
v
ec
to
r
o
f
th
e
b
est s
o
lu
tio
n
o
b
tai
n
ed
s
o
f
ar
,
X
is
th
e
p
o
s
itio
n
v
ec
to
r
,
|
|
is
t
h
e
ab
s
o
lu
te
v
al
u
e,
an
d
·
is
an
e
le
m
e
n
t
-
by
-
ele
m
en
t
m
u
ltip
licat
io
n
.
X
∗
s
h
o
u
ld
b
e
u
p
d
ated
in
ea
ch
iter
atio
n
if
t
h
er
e
is
a
b
etter
s
o
l
u
tio
n
.
T
h
e
v
ec
to
r
s
A
a
n
d
C
ar
e
ca
lcu
lated
as f
o
llo
w
s
:
(
1
3
)
(
1
4
)
w
h
er
e
is
li
n
ea
r
l
y
d
ec
r
ea
s
ed
f
r
o
m
2
to
0
o
v
er
th
e
co
u
r
s
e
o
f
ite
r
atio
n
s
(
in
b
o
th
e
x
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
p
h
ases
)
an
d
is
a
r
an
d
o
m
v
ec
to
r
in
[
0
,
1
]
.
Fig
u
r
e
1
(
a)
ill
u
s
tr
ate
s
t
h
e
r
ati
o
n
ale
b
eh
i
n
d
E
q
u
at
io
n
(
1
2
)
f
o
r
a
2
D
p
r
o
b
le
m
.
T
h
e
p
o
s
itio
n
(
X,
Y)
o
f
a
s
ea
r
ch
a
g
en
t
ca
n
b
e
u
p
d
ated
ac
co
r
d
in
g
to
th
e
p
o
s
it
io
n
o
f
t
h
e
cu
r
r
e
n
t
b
est
r
ec
o
r
d
(
X
∗
,Y
∗
)
.
Dif
f
er
en
t
p
lace
s
ar
o
u
n
d
th
e
b
est
ag
e
n
t
ca
n
b
e
ac
h
iev
ed
w
it
h
r
esp
ec
t
to
th
e
cu
r
r
en
t
p
o
s
itio
n
b
y
ad
j
u
s
ti
n
g
t
h
e
v
alu
e
o
f
A
an
d
C
v
ec
to
r
s
.
T
h
e
p
o
s
s
ib
le
u
p
d
atin
g
p
o
s
itio
n
o
f
a
s
ea
r
c
h
ag
e
n
t i
n
3
D
s
p
ac
e
i
s
also
d
ep
icted
in
F
ig
u
r
e
1
(
b
)
.
I
t sh
o
u
ld
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
E
co
n
o
mic
a
n
d
E
mis
s
io
n
Dis
p
a
tch
u
s
in
g
W
h
a
le
Op
timiz
a
tio
n
A
lg
o
r
ith
m
(
W
OA
)
(
F
a
s
ee
la
C
.
K
.
)
1301
b
e
n
o
ted
th
at
b
y
d
ef
i
n
i
n
g
t
h
e
r
an
d
o
m
v
ec
to
r
(
)
it
is
p
o
s
s
i
b
le
to
r
ea
ch
an
y
p
o
s
itio
n
i
n
th
e
s
ea
r
ch
s
p
ac
e
lo
ca
ted
b
et
w
ee
n
th
e
k
ey
-
p
o
i
n
ts
s
h
o
w
n
i
n
Fig
u
r
e
2
.
T
h
er
ef
o
r
e,
E
q
u
atio
n
(
1
2
)
allo
w
s
an
y
s
ea
r
ch
a
g
e
n
t
to
u
p
d
ate
its
p
o
s
itio
n
ab
o
u
t t
h
e
c
u
r
r
en
t b
est s
o
l
u
tio
n
a
n
d
s
i
m
u
l
ates e
n
cir
cli
n
g
th
e
p
r
e
y
.
T
h
e
s
a
m
e
co
n
ce
p
t
ca
n
b
e
ex
te
n
d
ed
to
a
s
ea
r
ch
s
p
ac
e
w
it
h
n
d
im
e
n
s
io
n
s
,
an
d
th
e
s
ea
r
c
h
a
g
e
n
t
s
w
ill
m
o
v
e
i
n
h
y
p
er
-
cu
b
es
ar
o
u
n
d
th
e
b
est
s
o
lu
tio
n
o
b
tain
ed
s
o
f
ar
.
A
s
m
en
t
io
n
ed
in
th
e
p
r
ev
io
u
s
s
ec
tio
n
,
th
e
h
u
m
p
b
ac
k
w
h
a
les
al
s
o
attac
k
t
h
e
p
r
e
y
w
it
h
t
h
e
b
u
b
b
le
-
n
e
t
s
tr
ateg
y
.
T
h
is
m
et
h
o
d
is
m
at
h
e
m
a
ticall
y
f
o
r
m
u
lated
in
t
h
e
n
e
x
t
s
ec
tio
n
.
Fig
u
r
e
1
.
B
u
b
b
le
-
n
et
s
ea
r
c
h
m
ec
h
an
i
s
m
i
m
p
le
m
e
n
ted
in
W
O
A
(
X
∗
i
s
th
e
b
est
s
o
lu
tio
n
o
b
tain
ed
s
o
f
ar
)
:
(
a)
s
h
r
in
k
i
n
g
e
n
cir
cli
n
g
m
ec
h
a
n
is
m
a
n
d
(
b
)
s
p
ir
al
u
p
d
atin
g
p
o
s
itio
n
3
.
2
.
2
.
B
ub
ble
-
net
a
t
t
a
ck
i
ng
m
et
ho
d (
ex
plo
it
a
t
io
n pha
s
e)
T
o
m
ath
e
m
atica
l
l
y
m
o
d
el
th
e
b
u
b
b
le
-
n
et
b
e
h
av
io
r
o
f
h
u
m
p
b
ac
k
w
h
ale
s
,
t
w
o
ap
p
r
o
ac
h
es
ar
e
d
esig
n
ed
as f
o
llo
w
s
:
Sh
r
i
n
k
i
n
g
en
c
ir
clin
g
m
ec
h
a
n
i
s
m
:
T
h
i
s
b
eh
a
v
io
r
is
ac
h
ie
v
e
d
b
y
d
ec
r
ea
s
i
n
g
t
h
e
v
al
u
e
o
f
in
t
h
e
E
q
u
a
tio
n
(
1
3
)
.
No
te
th
at
th
e
f
l
u
ctu
a
tio
n
r
an
g
e
o
f
is
also
d
ec
r
ea
s
ed
b
y
.
I
n
o
th
er
w
o
r
d
s
is
a
r
an
d
o
m
v
alu
e
i
n
th
e
i
n
ter
v
a
l
[
−a
,
a]
w
h
er
e
a
is
d
ec
r
ea
s
ed
f
r
o
m
2
to
0
o
v
er
th
e
co
u
r
s
e
o
f
iter
atio
n
s
.
Set
tin
g
r
an
d
o
m
v
alu
e
s
f
o
r
i
n
[
−1
,
1
]
,
th
e
n
e
w
p
o
s
itio
n
o
f
a
s
e
ar
ch
ag
e
n
t
ca
n
b
e
d
ef
in
ed
a
n
y
w
h
er
e
i
n
b
et
wee
n
t
h
e
o
r
ig
i
n
al
p
o
s
itio
n
o
f
th
e
ag
e
n
t
an
d
th
e
p
o
s
itio
n
o
f
th
e
cu
r
r
en
t
b
est
a
g
en
t.
Fi
g
.
3
(
a)
s
h
o
w
s
t
h
e
p
o
s
s
ib
le
p
o
s
itio
n
s
f
r
o
m
(
X,
Y)
to
w
ar
d
s
(
X
∗
,Y
∗
)
th
at
ca
n
b
e
ac
h
iev
ed
b
y
0
≤
A
≤
1
in
a
2
D
s
p
ac
e.
Sp
ir
al
u
p
d
atin
g
p
o
s
itio
n
:
As
ca
n
b
e
s
ee
n
i
n
Fig
u
r
e
3
(
b
)
,
th
is
ap
p
r
o
ac
h
f
ir
s
t
ca
lcu
lates
t
h
e
d
is
ta
n
ce
b
et
w
ee
n
th
e
w
h
ale
lo
ca
ted
at
(
X,
Y)
an
d
p
r
ey
lo
ca
ted
at
(
X
∗
,Y
∗
)
.
A
s
p
ir
al
eq
u
atio
n
is
t
h
en
cr
ea
ted
b
etw
ee
n
th
e
p
o
s
itio
n
o
f
w
h
ale
a
n
d
p
r
ey
to
m
i
m
ic
t
h
e
h
eli
x
-
s
h
ap
ed
m
o
v
e
m
e
n
t o
f
h
u
m
p
b
ac
k
w
h
ale
s
a
s
f
o
llo
w
s
:
(
1
5
)
W
h
er
e
an
d
in
d
icate
s
t
h
e
d
is
ta
n
ce
o
f
t
h
e
i
t
h
w
h
ale
to
t
h
e
p
r
e
y
(
b
est
s
o
l
u
tio
n
o
b
tai
n
ed
s
o
f
ar
)
,
b
i
s
a
co
n
s
ta
n
t
f
o
r
d
e
f
i
n
in
g
th
e
s
h
ap
e
o
f
t
h
e
lo
g
ar
ith
m
ic
s
p
ir
al,
l
i
s
a
r
an
d
o
m
n
u
m
b
er
in
[
−1
,
1
]
,
an
d
„
.
‟
is
an
ele
m
en
t
-
by
-
ele
m
e
n
t
m
u
ltip
licatio
n
.
T
h
e
h
u
m
p
b
ac
k
wh
ales
s
w
i
m
ar
o
u
n
d
th
e
p
r
e
y
w
it
h
i
n
a
s
h
r
i
n
k
i
n
g
cir
cle
an
d
alo
n
g
a
s
p
ir
al
-
s
h
ap
ed
p
ath
s
i
m
u
lta
n
eo
u
s
l
y
.
T
o
m
o
d
el
th
is
s
i
m
u
l
tan
eo
u
s
b
e
h
av
i
o
r
,
w
e
as
s
u
m
e
t
h
at
th
er
e
is
a
p
r
o
b
ab
ilit
y
o
f
5
0
% t
o
ch
o
o
s
e
b
et
w
ee
n
eit
h
er
th
e
s
h
r
in
k
i
n
g
en
c
ir
clin
g
m
ec
h
a
n
i
s
m
o
r
th
e
s
p
ir
al
m
o
d
el
to
u
p
d
ate
th
e
p
o
s
itio
n
o
f
w
h
al
es d
u
r
in
g
o
p
ti
m
izat
io
n
.
T
h
e
m
ath
e
m
atica
l
m
o
d
el
i
s
as f
o
llo
ws:
(
1
6
)
w
h
er
e
p
is
a
r
an
d
o
m
n
u
m
b
er
i
n
[
0
,
1
]
.
I
n
ad
d
itio
n
to
th
e
b
u
b
b
le
-
n
et
m
et
h
o
d
,
th
e
h
u
m
p
b
ac
k
w
h
ale
s
s
ea
r
ch
f
o
r
p
r
ey
r
an
d
o
m
l
y
.
T
h
e
m
at
h
e
m
at
ical
m
o
d
el
o
f
th
e
s
ea
r
ch
is
g
i
v
en
in
t
h
e
n
e
x
t
s
ec
tio
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.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
2
9
7
–
1304
1302
3
.
2
.
3
.
Sea
rc
h f
o
r
prey
(
ex
plo
ra
t
io
n
ph
a
s
e)
T
h
e
s
a
m
e
ap
p
r
o
ac
h
b
ased
o
n
th
e
v
ar
iatio
n
o
f
th
e
A
v
e
cto
r
ca
n
b
e
u
tili
ze
d
to
s
ea
r
ch
f
o
r
p
r
e
y
(
ex
p
lo
r
atio
n
)
.
I
n
f
ac
t,
h
u
m
p
b
a
ck
w
h
ales
s
ea
r
ch
r
an
d
o
m
l
y
ac
co
r
d
in
g
to
t
h
e
p
o
s
itio
n
o
f
ea
c
h
o
th
er
.
T
h
er
ef
o
r
e,
w
e
u
s
e
A
w
it
h
th
e
r
a
n
d
o
m
v
al
u
es
g
r
ea
ter
th
a
n
1
o
r
less
t
h
an
-
1
to
f
o
r
ce
s
ea
r
ch
a
g
en
t
to
m
o
v
e
f
ar
a
w
a
y
f
r
o
m
a
r
ef
er
en
ce
w
h
ale.
I
n
co
n
tr
a
s
t
to
th
e
e
x
p
lo
itatio
n
p
h
ase,
we
u
p
d
a
te
t
h
e
p
o
s
itio
n
o
f
a
s
ea
r
ch
ag
e
n
t
in
th
e
ex
p
lo
r
atio
n
p
h
ase
ac
co
r
d
in
g
t
o
a
r
an
d
o
m
l
y
c
h
o
s
en
s
ea
r
c
h
ag
en
t
i
n
s
tead
o
f
th
e
b
est
s
ea
r
ch
ag
en
t
f
o
u
n
d
s
o
f
ar
.
T
h
is
m
ec
h
an
is
m
an
d
|
A
|
>
1
e
m
p
h
a
s
ize
ex
p
lo
r
atio
n
a
n
d
allo
w
t
h
e
W
O
A
al
g
o
r
ith
m
to
p
er
f
o
r
m
a
g
lo
b
al
s
ea
r
c
h
.
T
h
e
m
at
h
e
m
atica
l
m
o
d
el
is
as
f
o
llo
w
s
:
(
1
7
)
(
1
8
)
4.
WAO
F
O
R
E
CO
NO
M
I
C
DIS
P
AT
CH
CA
SE
Me
r
it
o
r
d
er
d
is
p
atch
ca
s
e
is
i
m
p
le
m
e
n
ted
u
s
in
g
W
O
A
alg
o
r
ith
m
.
T
h
e
E
co
n
o
m
ic
d
is
p
atch
ca
s
e
i
m
p
le
m
en
ta
tio
n
is
d
o
n
e
o
n
s
ta
n
d
ar
d
I
E
E
E
3
0
b
u
s
s
y
s
te
m
.
As
it‟s
a
s
tan
d
ar
d
test
s
y
s
te
m
,
v
ar
io
u
s
p
ar
a
m
eter
s
h
ad
alr
ea
d
y
b
ee
n
r
ec
o
r
d
ed
.
T
h
is
s
y
s
te
m
w
as
u
s
ed
i
n
m
a
n
y
co
m
p
ar
ab
le
s
t
u
d
ies i
n
t
h
e
m
er
it
o
r
d
er
d
is
p
atch
.
4
.
1
.
I
E
E
E
-
30
bu
s
s
y
s
t
e
m
-
re
s
ults
T
h
is
s
y
s
te
m
h
a
s
6
g
en
er
ato
r
b
u
s
es
at
b
u
s
1
,
2
,
5
,
8
,
1
1
an
d
1
3
.
E
ac
h
o
f
th
es
e
g
e
n
er
ato
r
s
h
as
t
h
eir
o
w
n
f
u
e
l
an
d
e
m
is
s
io
n
co
ef
f
i
cien
ts
.
T
h
ese
ar
e
r
ep
r
esen
ted
in
p
er
u
n
it
v
al
u
es
to
s
i
m
p
li
f
y
ca
lc
u
latio
n
s
.
T
h
e
b
ase
co
n
s
id
er
ed
is
1
0
0
MV
A
.
T
h
e
to
tal
d
em
a
n
d
co
n
s
id
er
ed
is
2
.
3
8
p
.
u
.
T
h
e
v
ar
io
u
s
g
e
n
er
a
tio
n
p
ar
a
m
eter
s
ar
e
tab
u
lated
as f
o
llo
w
s
,
Ge
n
er
ato
r
co
s
t c
o
ef
f
icie
n
ts
f
o
r
I
E
E
E
3
0
b
u
s
s
y
s
te
m
is
p
r
o
v
id
ed
in
T
ab
le
1
g
iv
e
n
b
elo
w
.
T
ab
le
1.
Sam
p
le
C
o
s
t Co
ef
f
ici
en
ts
U
n
i
t
e
F
1
10
2
0
0
1
0
0
15
6
.
2
8
3
0
.
0
5
0
.
5
2
10
1
5
0
1
2
0
10
8
.
9
7
6
0
.
0
5
0
.
6
3
20
1
8
0
40
10
1
4
.
7
8
4
0
.
0
5
1
4
10
1
0
0
60
5
2
0
.
9
4
4
0
.
0
5
1
.
2
5
20
1
8
0
40
5
2
5
.
1
3
3
0
.
0
5
1
6
10
1
5
0
1
0
0
5
1
8
.
4
8
0
.
0
5
0
.
6
Gen
er
ato
r
e
m
is
s
io
n
co
e
f
f
ic
ien
ts
f
o
r
I
E
E
E
-
30
-
b
u
s
s
y
s
te
m
is
p
r
o
v
id
ed
in
T
ab
le
2
g
iv
en
b
elo
w
.
T
ab
le
2
.
Sam
p
le
E
m
is
s
io
n
C
o
ef
f
icien
ts
U
n
i
t
1
4
.
0
9
1
-
5
.
5
5
4
6
.
4
9
2
.
0
0
E
-
03
2
.
8
5
7
2
2
.
5
4
3
-
6
.
0
4
7
5
.
6
3
8
5
.
0
0
E
-
04
3
.
3
3
3
3
4
.
2
5
8
-
5
.
0
9
4
4
.
5
8
6
1
.
0
0
E
-
06
8
4
5
.
3
2
6
-
3
.
3
5
3
.
3
8
2
.
0
0
E
-
03
2
5
4
.
2
5
8
-
5
.
0
9
4
4
.
5
8
6
1
.
0
0
E
-
06
8
6
6
.
1
3
1
-
5
.
5
5
5
5
.
1
5
1
1
.
0
0
E
-
05
6
.
6
6
7
IEEE
-
3
0
b
u
s
s
y
s
te
m
s
i
m
u
la
ti
o
n
an
d
i
m
p
le
m
en
ta
tio
n
o
f
E
co
n
o
m
ic
d
is
p
atc
h
u
s
i
n
g
W
O
A
i
s
d
o
n
e
u
s
i
n
g
M
A
T
L
A
B
p
r
o
g
r
am
.
T
h
e
f
o
llo
w
in
g
ar
e
th
e
r
es
u
lt
s
o
b
tain
ed
in
co
m
p
ar
is
o
n
w
it
h
P
SO
alg
o
r
ith
m
.
T
h
e
v
alu
e
is
f
o
u
n
d
at
ea
c
h
s
t
ep
an
d
r
es
u
lts
ar
e
ca
lc
u
lated
v
ar
y
i
n
g
t
h
e
n
o
o
f
iter
atio
n
s
a
n
d
g
r
ap
h
is
p
lo
tted
f
o
r
th
e
s
a
m
e.
T
h
e
b
est r
esu
lt i
s
o
b
tain
ed
at
1
0
6
th
iter
a
tio
n
s
a
n
d
th
er
e
is
n
o
d
if
f
er
e
n
c
e
af
ter
w
ar
d
s
.
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
E
co
n
o
mic
a
n
d
E
mis
s
io
n
Dis
p
a
tch
u
s
in
g
W
h
a
le
Op
timiz
a
tio
n
A
lg
o
r
ith
m
(
W
OA
)
(
F
a
s
ee
la
C
.
K
.
)
1303
T
ab
le
3
.
E
c
o
n
o
m
ic
Dis
p
atc
h
r
esu
lt
s
u
s
i
n
g
W
O
A
PSO
W
O
A
0
.
0
9
9
4
4
1
0
.
0
9
8
7
6
2
0
.
3
6
2
4
8
0
.
3
7
4
6
8
0
.
4
8
3
4
9
0
.
4
7
0
1
1
0
.
8
7
3
5
9
0
.
9
2
7
7
3
0
.
6
6
4
2
8
0
.
7
0
2
2
1
0
.
3
9
0
0
4
0
.
3
8
9
9
1
T
o
t
a
l
F
u
e
l
C
o
st
(
)
I
N
R
/
h
r
4
0
7
5
2
.
1
9
4
0
2
3
2
.
2
6
T
o
t
a
l
Emi
ssi
o
n
(
)
t
o
n
/
h
r
0
.
2
1
3
9
2
1
0
.
2
1
3
8
4
1
T
o
t
a
l
Emi
ssi
o
n
C
o
st
(
)
I
N
R
/
h
r
6
6
1
.
0
1
8
6
6
0
.
7
6
8
7
T
o
t
a
l
C
o
st
(
)
I
N
R
/
h
r
4
1
4
1
3
.
2
0
4
0
8
9
3
.
0
3
5.
I
NF
E
R
E
NC
E
S
W
h
ale
o
p
ti
m
izatio
n
p
r
o
v
id
es
ex
ce
lle
n
t
r
es
u
lt
s
f
o
r
a
n
ec
o
n
o
m
ic
d
i
s
p
atch
p
r
o
b
lem
i
n
ter
m
s
o
f
co
s
t
o
p
tim
izatio
n
a
n
d
ea
s
y
co
n
v
er
g
en
ce
.
On
co
m
p
ar
is
o
n
,
t
h
e
c
o
s
t
o
p
ti
m
izatio
n
o
f
W
O
A
is
b
etter
th
an
P
SO
b
y
1
.3
%.
T
h
er
e
ca
n
b
e
f
u
r
t
h
er
i
m
p
r
o
v
e
m
e
n
t
s
o
n
t
h
e
alg
o
r
ith
m
co
n
s
id
er
i
n
g
t
h
e
f
o
llo
w
i
n
g
f
ac
to
r
s
.
U
s
e
co
m
b
i
n
atio
n
al
al
g
o
r
ith
m
s
–
S
u
g
g
es
ted
alg
o
r
it
h
m
s
ar
e
AL
O
an
d
W
O
A
e
x
tr
ac
ti
n
g
e
f
f
ec
tiv
e
f
ea
t
u
r
es
o
f
b
o
th
.
I
n
clu
d
e
o
th
er
c
h
ar
ac
ter
is
tic
s
o
f
h
u
m
p
b
ac
k
w
h
a
les
o
n
t
h
i
s
alg
o
r
ith
m
.
T
h
is
i
n
cl
u
d
es
ef
f
e
cts
o
f
„
d
o
u
b
le
lo
o
p
‟
b
u
b
b
les in
to
th
is
al
g
o
r
ith
m
.
RE
F
E
R
E
NC
E
S
[
1
]
S
u
sh
e
e
l
Ku
m
a
r
De
wa
n
g
a
n
,
A
c
h
a
la
Ja
in
,
Dr.
A.
P
.
H
u
d
d
a
r,
“
A
T
ra
d
it
io
n
a
l
A
p
p
r
o
a
c
h
t
o
S
o
lv
e
Eco
n
o
m
ic
L
o
a
d
Disp
a
tch
P
ro
b
lem
Co
n
sid
e
ri
n
g
th
e
G
e
n
e
r
a
to
r
Co
n
stra
i
n
ts”
,
IOS
R
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
El
e
c
tro
n
ics
En
g
i
n
e
e
rin
g
(IOSR
-
JEEE
),
v
o
l.
1
0
,
n
o
.
2
V
e
r.
III
(M
a
r
-
A
p
r.
2
0
1
5
)
,
p
p
.
27
-
32
[
2
]
D.
L
.
T
r
a
v
e
r
s,
R.
Ka
y
e
,
“
D
y
n
a
m
ic
d
isp
a
tch
b
y
c
o
n
stru
c
ti
v
e
d
y
n
a
m
i
c
p
ro
g
ra
m
m
in
g
”
,
IEE
E
T
ra
n
s.
o
n
Po
we
r
S
y
ste
ms
,
v
o
l.
1
3
,
n
o
.
1
,
p
p
.
72
-
7
8
,
F
e
b
1
9
9
8
.
[
3
]
A.
J.
W
o
o
d
a
n
d
B.
F
.
W
o
ll
e
n
b
e
rg
,
P
o
w
e
r
G
e
n
e
ra
ti
o
n
,
Op
e
ra
ti
o
n
a
n
d
Co
n
tr
o
l
,
Ne
w
Yo
rk
:
W
il
e
y
,
(1
9
9
6
)
.
[
4
]
B.
S
to
tt
,
Ho
b
s
o
n
,
Eri
c
,
“
P
o
w
e
r
S
y
st
e
m
S
e
c
u
rit
y
Co
n
tro
l
Ca
lcu
latio
n
s
Us
in
g
L
in
e
a
r
P
ro
g
ra
m
m
in
g
”,
Pa
rt
I,
IEE
E
T
ra
n
s.
o
n
Po
we
r A
p
p
a
ra
t
u
s a
n
d
S
y
ste
ms
,
v
o
l.
P
A
S
-
9
7
,
n
o
.
5
,
p
p
.
1
7
1
3
-
1
7
2
0
,
S
e
p
t
1
9
7
8
.
[
5
]
K.
S
.
Ku
m
a
r,
V
.
T
a
m
il
se
lv
a
n
,
N.
M
u
ra
li
,
R.
Ra
jara
m
,
N.
S
.
S
u
n
d
a
ra
m
,
a
n
d
T
.
Ja
y
a
b
a
ra
th
i,
“
Eco
n
o
m
ic
lo
a
d
d
isp
a
tch
w
it
h
e
m
is
sio
n
c
o
n
stra
in
t
s
u
sin
g
v
a
rio
u
s
P
S
O
a
lg
o
rit
h
m
s
”
,
W
S
EA
S
T
ra
n
sa
c
ti
o
n
s
o
n
P
o
we
r
S
y
ste
ms
,
v
o
l.
3
,
n
o
.
9
,
p
p
.
5
9
8
-
6
0
7
,
2
0
0
8
.
[
6
]
M
irj
a
li
li
S
.
,
A
.
L
e
w
is
,
“
T
h
e
W
h
a
le Op
ti
m
iza
ti
o
n
A
lg
o
rit
h
m
”
,
Ad
v
a
n
c
e
s in
E
n
g
i
n
e
e
rin
g
S
o
ft
wa
re
,
2
0
1
6
,
p
p
.
51
-
67
[
7
]
M
.
A
b
id
o
,
“
E
n
v
iro
n
m
e
n
tal/E
c
o
n
o
m
ic
P
o
w
e
r
Disp
a
tch
u
sin
g
M
u
lt
i
o
b
jec
ti
v
e
Ev
o
lu
ti
o
n
a
ry
A
lg
o
rit
h
m
s
”
,
IEE
E
T
ra
n
s.
o
n
Po
we
r
S
y
ste
ms
,
v
o
l.
1
8
,
n
o
.
4
,
p
p
.
1
5
2
9
-
1
5
3
7
,
No
v
2
0
0
3
[
8
]
S
.
He
m
a
m
a
li
n
i,
S
P
.
S
im
o
n
,
“
A
rti
f
icia
l
b
e
e
c
o
lo
n
y
a
lg
o
rit
h
m
f
o
r
e
c
o
n
o
m
ic
lo
a
d
d
is
p
a
tch
p
r
o
b
lem
w
i
th
n
o
n
-
sm
o
o
t
h
c
o
st f
u
n
c
ti
o
n
s
”,
El
e
c
tric P
o
we
r C
o
mp
o
n
e
n
ts
a
n
d
S
y
ste
ms
,
v
o
l.
3
8
,
n
o
.
7
,
p
p
.
7
8
6
-
8
0
3
,
M
a
y
(2
0
1
0
).
[
9
]
V
e
n
n
il
a
.
H,
R
u
b
a
n
De
v
a
P
ra
k
a
s
h
.
T
.
,
“
A
so
lu
ti
o
n
f
o
r
e
n
v
ir
o
n
m
e
n
tal
c
o
n
stra
i
n
e
d
Eco
n
o
m
isc
Disp
a
tch
P
ro
b
lem
s
u
sin
g
Ho
n
e
y
Be
e
A
lg
o
rit
h
m
”
,
In
ter
n
a
ti
o
n
a
l
jo
u
rn
a
l
o
f
Co
m
p
u
ter
A
p
p
li
c
a
ti
o
n
,
v
o
l
.
4
7
,
n
o
.
2
2
,
p
p
.
1
3
-
1
7
,
(2
0
1
2
)
.
[
1
0
]
V
e
n
n
il
a
,
H.,
R
u
b
a
n
De
v
a
P
ra
k
a
sh
,
T
.
,
(2
0
1
2
)
,
“
P
a
rti
c
le
S
w
a
m
Op
ti
m
isa
ti
o
n
T
e
c
h
n
iq
u
e
f
o
r
so
l
v
in
g
Eco
n
o
m
ic
Disp
a
tch
P
ro
b
lem
s”
,
El
se
v
ier
J
o
u
rn
a
l
of
Pr
o
c
e
d
ia
En
g
in
e
e
rin
g
,
n
o
.
4
8
,
p
p
.
2
0
0
9
-
2
0
2
1
.
[
1
1
]
X
in
-
S
h
e
Ya
n
g
,
S
.
De
b
,
C
u
c
k
o
o
S
e
a
rc
h
v
ia
L
é
v
y
f
li
g
h
ts,
Na
tu
re
&
Bio
lo
g
ica
ll
y
In
sp
ired
C
o
m
p
u
ti
n
g
,
Na
BIC
2
0
0
9
,
W
o
rld
Co
n
g
re
ss
,
pp.
2
1
0
-
2
1
4
.
[
1
2
]
F
.
Re
id
,
L
.
Ha
sd
o
rf
f
,
“
Eco
n
o
m
ic
Disp
a
tch
Us
in
g
Qu
a
d
ra
ti
c
P
ro
g
ra
m
m
in
g
”
,
IEE
E
T
ra
n
s.
o
n
P
o
we
r
Ap
p
a
ra
t
u
s
a
n
d
S
y
ste
ms
,
v
o
l.
P
A
S
-
9
2
,
n
o
.
6,
p
p
.
2
0
1
5
-
2
0
2
3
,
N
o
v
.
1
9
7
3
.
[
1
3
]
K.
S.
L
e
e
a
n
d
Z.
W.
G
e
e
m
,
“
A
Ne
w
S
tru
c
tu
ra
l
Op
ti
m
iza
ti
o
n
M
e
th
o
d
Ba
se
d
o
n
Ha
rm
o
n
y
S
e
a
rc
h
A
lg
o
rit
h
m
Co
m
p
u
ters
a
n
d
str
u
c
tu
re
s
”
,
v
o
l.
8
2
,
p
p
.
7
8
1
-
7
9
8
,
2
0
0
4
.
[
1
4
]
G
a
in
g
,
“
P
a
rti
c
le
sw
a
r
m
o
p
ti
m
i
z
a
t
io
n
to
so
lv
in
g
th
e
e
c
o
n
o
m
ic
d
isp
a
tch
c
o
n
sid
e
ri
n
g
th
e
g
e
n
e
ra
to
r
c
o
n
stra
in
ts
”
,
IEE
E
T
ra
n
s.
Po
we
r S
y
st.
,
v
o
l.
1
8
,
p
p
.
1
1
87
-
1
1
9
5
,
A
u
g
.
2
0
0
3
.
[
1
5
]
Ch
a
o
-
L
u
n
g
Ch
ian
g
,
“
Im
p
ro
v
e
d
G
e
n
e
ti
c
a
lg
o
rit
h
m
f
o
r
p
o
w
e
r
e
c
o
n
o
m
ic
d
isp
a
tch
o
f
u
n
it
s
w
it
h
v
a
l
v
e
p
o
in
t
e
ff
e
c
ts
a
n
d
m
u
lt
ip
le f
u
e
ls
”
,
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
p
o
we
r sy
ste
ms
,
v
o
l
2
0
,
n
o
.
4
,
N
o
v
2
0
0
5
,
p
p
.
1
6
9
0
-
1
6
9
9
.
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.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
2
9
7
–
1304
1304
[
1
6
]
E.
L
in
,
G.
L.
V
iv
ian
i
,
(
1
9
8
4
),
“
Hie
ra
rc
h
ica
l
Eco
n
o
m
ic
Disp
a
tch
f
o
r
p
iec
e
w
ise
q
u
a
d
ra
ti
c
c
o
st
f
u
n
c
ti
o
n
s
”
,
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
p
o
we
r a
p
p
a
r
a
tu
s
a
n
d
sy
ste
ms
,
v
o
l.
P
A
S
-
1
0
3
,
n
o
.
6
,
J
u
n
e
,
p
p
.
1
1
7
0
-
1
1
7
5
.
[
1
7
]
P.
S
u
b
b
a
ra
ja,
R.
Re
n
g
a
ra
j,
S.
S
a
li
v
a
h
a
n
a
n
,
“
En
h
a
n
c
e
m
e
n
t
o
f
se
lf
a
d
a
p
ti
v
e
re
a
l
c
o
d
e
d
g
e
n
e
ti
c
a
lg
o
rit
h
m
u
sin
g
T
a
g
u
c
h
i
m
e
th
o
d
f
o
r
e
c
o
n
o
m
ic d
is
p
a
tch
p
r
o
b
lem
”
,
Ap
p
l
ied
S
o
ft
Co
mp
u
ti
n
g
,
p
p
.
1
-
1
0
.
[
1
8
]
G
iri
sh
Ku
m
a
r,
R
a
m
e
s
h
w
a
r
S
in
g
h
,
“
Eco
n
o
m
ic
Disp
a
tch
o
f
P
o
w
e
r
S
y
ste
m
Op
ti
m
i
z
a
ti
o
n
w
it
h
P
o
w
e
r
G
e
n
e
ra
ti
o
n
S
c
h
e
d
u
le
Us
in
g
Ev
o
lu
ti
o
n
a
ry
T
e
c
h
n
iq
u
e
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Ad
v
a
n
c
e
d
Res
e
a
rc
h
in
El
e
c
trica
l
El
e
c
tro
n
ics
a
n
d
In
str
u
me
n
ta
t
io
n
En
g
i
n
e
e
rin
g
,
v
o
l.
3
,
n
o
.
7
,
J
u
ly
2
0
1
4
.
[
1
9
]
A
n
u
ra
g
G
u
p
ta,
K.
K.
S
w
a
rn
k
a
r,
K.
W
a
d
h
w
a
n
i,
“
Co
m
b
in
e
d
Eco
n
o
m
ic
E
m
issio
n
Disp
a
tch
P
r
o
b
le
m
u
sin
g
P
a
rti
c
l
e
S
w
a
r
m
Op
ti
m
iza
ti
o
n
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
C
o
mp
u
ter
Ap
p
li
c
a
t
io
n
s
(
0
9
7
5
-
88
8
7
)
,
v
o
l.
4
9
,
n
o.
6
,
J
u
ly
2
0
1
2
.
[
2
0
]
Ha
d
i
S
a
a
d
a
t,
P
o
w
e
r
S
y
ste
m
A
n
a
l
y
sis,
b
y
M
c
G
re
w
Hill
.
In
c
;1
9
9
9
.
Evaluation Warning : The document was created with Spire.PDF for Python.