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9
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w
o
r
k
i
n
r
e
al
ti
m
e.
Sto
c
h
ast
ic
m
et
h
o
d
s
ar
e
esp
ec
iall
y
p
r
o
d
u
cti
v
e
f
o
r
o
p
ti
m
izatio
n
p
r
o
b
le
m
s
t
h
at
h
av
e
s
u
ch
f
ea
tu
r
e
s
as
n
o
n
lin
ea
r
i
t
y
,
n
o
n
-
d
if
f
er
en
tiab
ilit
y
,
a
n
d
h
ig
h
co
m
p
u
ta
tio
n
al
co
m
p
le
x
it
y
,
f
o
r
s
to
ch
a
s
tic
a
n
d
d
y
n
a
m
ic
tas
k
s
[
1
6
-
1
8
]
.
Of
te
n
it
is
i
m
p
o
s
s
ib
le
to
m
ak
e
a
n
an
al
y
tical
e
x
p
r
ess
io
n
f
o
r
th
e
m
b
ec
au
s
e
th
e
m
o
d
el
h
as
a
co
m
p
lex
s
tr
u
ct
u
r
e
an
d
in
ter
co
n
n
ec
tio
n
s
b
et
w
ee
n
its
ele
m
en
t
s
.
I
t
also
lead
s
to
g
r
ea
t
lab
o
r
io
u
s
n
es
s
o
f
ca
lcu
l
atio
n
an
d
co
m
p
le
x
to
p
o
lo
g
y
o
f
th
e
s
ea
r
ch
s
p
ac
e.
T
h
is
r
esear
ch
co
n
s
id
er
s
t
h
e
P
SO
an
d
t
h
e
BA
f
o
r
d
y
n
a
m
ic
o
p
ti
m
izatio
n
i
n
t
h
e
d
y
n
a
m
ic
o
p
ti
m
izat
io
n
o
f
C
Us.
A
t
th
i
s
s
ta
g
e
o
f
th
e
r
esear
ch
,
th
e
ea
s
ie
s
t
o
p
tio
n
f
o
r
th
e
ex
p
er
i
m
e
n
ts
as
s
o
ciate
d
w
ith
f
ai
lu
r
es
o
f
C
Us
h
as
b
ee
n
s
elec
ted
.
I
t i
s
t
h
e
m
ai
n
a
s
p
ec
t o
f
th
e
r
eliab
ilit
y
o
f
p
o
w
er
s
y
s
te
m
s
[
1
9
]
.
T
h
e
p
u
r
p
o
s
e
o
f
th
is
s
ta
g
e
i
s
to
an
al
y
ze
t
h
e
d
y
n
a
m
ic
p
r
o
p
er
ties
o
f
t
h
ese
alg
o
r
it
h
m
s
.
W
e
s
t
r
ik
e
to
cr
ea
te
m
o
d
i
f
icatio
n
s
o
f
t
h
ese
al
g
o
r
ith
m
s
,
b
ased
o
n
th
is
an
al
y
s
i
s
f
o
r
th
ei
r
f
u
r
th
er
u
s
e
u
n
d
er
th
e
co
n
d
itio
n
s
w
h
e
n
,
ap
ar
t
f
r
o
m
t
h
e
eq
u
i
p
m
e
n
t
f
ail
u
r
es,
th
e
p
o
w
er
s
o
f
co
n
s
u
m
er
s
o
f
elec
tr
icit
y
i
n
th
e
g
r
id
ar
e
v
ar
ied
ac
co
r
d
in
g
to
th
e
ce
r
tain
d
i
s
tr
ib
u
ti
o
n
f
u
n
ct
io
n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
T
he
po
w
er
s
up
ply
s
y
s
t
e
m
co
ns
idere
d
T
h
e
p
o
w
er
s
u
p
p
l
y
s
y
s
te
m
400
V
o
f
a
p
r
o
d
u
ctio
n
p
lan
t
is
co
n
s
id
er
ed
.
T
h
e
p
o
w
er
s
u
p
p
l
y
s
y
s
te
m
r
ep
r
esen
ts
a
s
u
b
s
tatio
n
co
m
p
r
i
s
in
g
f
o
u
r
s
ec
tio
n
s
,
a
n
d
t
h
e
ar
r
an
g
e
m
e
n
t
o
f
ea
c
h
s
ec
tio
n
is
r
a
d
ial.
Fo
r
th
e
p
o
w
er
d
is
tr
ib
u
tio
n
a
m
o
n
g
co
n
s
u
m
er
s
,
p
o
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er
d
is
tr
ib
u
tio
n
p
o
in
t
s
f
ed
f
r
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m
th
e
s
u
b
s
tatio
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s
e
ctio
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b
u
s
b
ar
s
ar
e
ap
p
lied
.
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en
m
o
s
t
co
n
g
e
s
ted
d
is
tr
ib
u
t
i
o
n
p
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ts
ar
e
co
n
s
id
er
ed
i
n
ea
ch
s
ec
t
io
n
(
d
is
tr
ib
u
tio
n
ca
b
i
n
e
ts
)
,
w
h
ic
h
p
r
o
v
id
e
p
o
w
er
f
o
r
a
s
y
n
c
h
r
o
n
o
u
s
m
o
t
o
r
s
o
f
t
h
e
v
e
n
tilatio
n
s
y
s
te
m
,
elec
tr
ic
m
o
to
r
s
o
f
p
u
m
p
s
(
th
e
p
o
w
er
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f
ea
c
h
i
s
ab
o
u
t
1
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0
k
W
)
a
n
d
th
e
m
ai
n
s
u
p
p
l
y
lin
e
s
o
f
b
o
o
s
t
co
m
p
r
e
s
s
o
r
s
.
E
ac
h
m
ai
n
s
u
p
p
l
y
li
n
e
p
r
o
v
id
es
f
r
o
m
1
0
to
1
5
co
m
p
r
ess
o
r
s
w
it
h
9
k
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in
d
u
ctio
n
m
o
to
r
s
w
h
ic
h
ar
e
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u
p
p
lied
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y
a
s
t
u
b
.
2
.
2
.
T
he
M
a
t
he
m
a
t
ica
l
m
o
del
T
h
e
m
at
h
e
m
atica
l
m
o
d
el
o
f
th
e
co
n
s
id
er
ed
o
p
tim
izat
io
n
p
r
o
b
le
m
h
a
s
th
e
v
ec
to
r
o
f
C
U
p
o
w
er
s
(
Q
CU
)
as
co
n
tr
o
l
v
ar
iab
les
a
n
d
cr
it
er
ia:
a
ctiv
e
p
o
w
er
lo
s
s
es
→
m
i
n
&
s
tab
le
o
p
er
atio
n
o
f
th
e
s
y
s
te
m
→
tr
u
e.
T
h
e
s
ec
o
n
d
cr
iter
io
n
w
e
u
s
e
as
th
e
p
en
alt
y
f
u
n
ctio
n
G
(
Q
CU
)
.
A
s
a
r
es
u
lt,
t
h
e
m
at
h
e
m
atica
l
f
o
r
m
u
latio
n
ca
n
b
e
w
r
itte
n
as
f
o
llo
w
s
:
Δ
P
(
Q
CU
)
+
G
(
Q
CU
)
→
m
in
,
(
1
)
Q
CU
{
Q
1
,
Q
2
,
…,
Q
n
}
(
2
)
0
<
Q
i
<
Q
max
i
,
i
=
1
,
…,
n
(
3
)
Δ
P
(
Q
CU
)
is
th
e
to
tal
lo
s
s
es o
f
ac
tiv
e
p
o
w
er
i
n
p
o
w
er
s
u
p
p
l
y
s
y
s
te
m
.
Q
CU
is
t
h
e
C
U
p
o
w
er
v
ec
to
r
.
Q
i
is
th
e
C
U
p
o
w
er
i
n
th
e
i
-
t
h
n
o
d
e
(
if
Q
max
I
=
0
,
th
en
a
C
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i
s
n
o
t in
s
talled
i
n
th
e
i
-
t
h
n
o
d
e)
;
n
is
th
e
n
u
m
b
er
o
f
n
o
d
e
w
h
er
e
C
Us ar
e
in
s
talled
(
n
=
1
4
f
o
r
co
n
s
id
er
ed
p
o
w
er
s
u
p
p
l
y
s
y
s
te
m
)
.
Q
max
i
is
th
e
m
ax
i
m
u
m
allo
w
ab
le
C
U
p
o
w
er
v
a
lu
e
i
n
th
e
i
-
t
h
n
o
d
e.
2
.
3
.
Sw
a
r
m
inte
llig
ence
a
lg
o
rit
hm
s
T
er
m
"
P
o
p
u
latio
n
-
b
ased
"
m
e
an
s
th
at
th
e
s
e
al
g
o
r
ith
m
s
u
s
e
a
"
p
o
p
u
latio
n
"
as
a
s
y
s
te
m
o
f
p
ar
ticles
(
ag
en
t
s
)
.
As
a
r
u
le,
s
u
c
h
al
g
o
r
ith
m
s
ar
e
b
ased
o
n
n
atu
r
al
m
ec
h
a
n
i
s
m
s
,
s
u
c
h
as
e
v
o
lu
tio
n
ar
y
s
elec
tio
n
,
co
o
p
er
ate
ac
tiv
it
y
o
f
i
n
s
ec
t
s
,
etc.
T
h
e
ter
m
"
p
ar
ticle
"
m
e
an
s
a
p
o
in
t
in
t
h
e
s
ea
r
ch
s
p
a
ce
m
at
h
e
m
atica
l
l
y
,
s
o
it
is
s
o
m
e
s
o
lu
tio
n
o
f
an
o
p
tim
izatio
n
p
r
o
b
le
m
.
T
h
e
m
ain
f
ea
t
u
r
e
o
f
th
e
p
o
p
u
latio
n
-
b
ased
alg
o
r
ith
m
s
is
th
eir
s
el
f
-
o
r
g
an
iza
tio
n
.
I
t
en
s
u
r
es
th
e
alg
o
r
it
h
m
s
’
ab
ilit
y
to
ex
p
lo
r
e
th
e
s
ea
r
ch
s
p
ac
e
r
eg
ar
d
less
o
f
t
h
e
s
p
ac
e’
s
d
i
m
en
s
io
n
a
n
d
to
p
o
lo
g
y
.
I
t
p
r
o
v
id
es
th
e
f
le
x
ib
ili
t
y
a
n
d
p
o
s
s
ib
ilit
y
to
f
i
n
d
q
u
asi
-
o
p
ti
m
al
s
o
lu
tio
n
s
q
u
ic
k
l
y
.
P
o
p
u
latio
n
-
b
ased
o
p
ti
m
izat
io
n
alg
o
r
it
h
m
s
ca
n
b
e
s
p
lit
i
n
to
E
v
o
lu
tio
n
ar
y
al
g
o
r
ith
m
s
an
d
S
w
ar
m
I
n
telli
g
en
ce
(
SI)
alg
o
r
ith
m
s
.
T
h
e
ev
o
lu
tio
n
ar
y
p
r
o
ce
s
s
is
b
ased
o
n
cr
ea
ti
n
g
n
e
w
p
o
p
u
latio
n
s
at
ev
er
y
n
e
w
s
tep
co
n
ce
r
n
in
g
th
e
ex
p
er
ien
ce
o
b
tain
ed
b
y
th
e
p
r
ev
io
u
s
p
o
p
u
latio
n
s
.
Ge
n
et
ic
alg
o
r
ith
m
,
Gen
etic
p
r
o
g
r
a
m
m
in
g
,
Di
f
f
er
e
n
tial
ev
o
lu
tio
n
ar
e
ex
a
m
p
les
o
f
E
v
o
lu
tio
n
ar
y
al
g
o
r
ith
m
s
.
I
t
u
s
es
p
r
in
cip
les
o
f
n
at
u
r
al
s
e
lec
tio
n
as
i
n
h
er
itan
ce
,
m
u
tatio
n
,
cr
o
s
s
o
v
er
.
T
h
e
w
o
r
d
"
s
w
ar
m
"
ca
n
b
e
d
e
f
i
n
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as
a
g
r
o
u
p
o
f
o
b
j
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ts
o
r
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ag
g
r
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o
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m
o
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g
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S
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m
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m
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t
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an
d
th
e
in
d
ir
ec
t in
f
o
r
m
atio
n
ex
c
h
an
g
e.
T
h
e
ap
p
licatio
n
o
f
th
e
s
y
s
te
m
ap
p
r
o
ac
h
allo
w
s
is
o
la
t
in
g
ele
m
e
n
ts
o
f
an
o
b
j
ec
t
an
d
r
elatio
n
s
h
ip
s
b
et
w
ee
n
th
e
m
.
A
l
s
o
,
it
n
ee
d
s
to
d
ef
in
e
co
n
n
ec
tio
n
s
b
et
w
e
en
an
o
b
j
ec
t
an
d
th
e
o
u
ts
id
e
w
o
r
ld
(
in
p
u
t
s
an
d
o
u
tp
u
ts
)
.
A
n
SI
alg
o
r
it
h
m
i
s
s
p
ec
if
ied
ce
r
tain
d
ata
s
tr
u
ct
u
r
es
an
d
o
p
er
atio
n
s
o
n
th
e
m
lik
e
m
a
n
y
o
th
er
alg
o
r
i
th
m
s
.
SI
u
s
e
s
t
h
e
s
w
ar
m
,
i.e
.
,
a
p
o
p
u
latio
n
o
f
p
ar
ticle
s
as
a
b
ase
d
ata
s
tr
u
ct
u
r
e
a
n
d
r
u
le
s
o
f
m
o
v
i
n
g
p
ar
ticle
s
as
an
alg
o
r
it
h
m
o
f
tr
an
s
f
o
r
m
atio
n
t
h
i
s
d
ata.
A
d
is
ti
n
cti
v
e
f
ea
t
u
r
e
o
f
th
e
SI
al
g
o
r
it
h
m
s
i
s
u
s
in
g
s
o
m
e
m
ea
n
s
o
f
in
d
ir
ec
t
in
f
o
r
m
at
i
o
n
ex
ch
a
n
g
e
b
et
w
ee
n
p
ar
ticle
s
.
T
h
e
P
ar
ticle
S
w
ar
m
Op
ti
m
izatio
n
u
s
e
s
th
e
b
est
-
f
o
u
n
d
p
o
s
itio
n
i
n
th
e
s
ea
r
ch
s
p
ac
e;
th
e
An
t
C
o
lo
n
y
Op
ti
m
izatio
n
u
s
es
a
p
h
er
o
m
o
n
e
g
r
ap
h
;
th
e
Mo
n
k
e
y
Sear
c
h
alg
o
r
it
h
m
u
s
es
a
w
ei
g
h
ted
ce
n
ter
o
f
p
ar
ticle
s
’
p
o
s
itio
n
s
,
etc.
Fin
a
ll
y
,
it
is
n
ec
ess
ar
y
to
d
is
tin
g
u
is
h
th
e
m
ea
n
s
o
f
d
ata
ex
ch
a
n
g
e
o
f
an
SI
alg
o
r
it
h
m
a
n
d
th
e
o
u
ts
id
e
w
o
r
ld
(
o
p
tim
izatio
n
tas
k
)
.
T
h
u
s
,
w
e
ca
n
f
o
r
m
u
late
b
asic
p
ar
ts
o
f
SI
alg
o
r
ith
m
:
-
Set o
f
p
ar
ticles
S
.
-
Me
an
o
f
i
n
d
ir
ec
t in
ter
ac
tio
n
M
.
-
A
l
g
o
r
ith
m
o
f
p
ar
ticle
s
’
m
o
v
in
g
,
an
d
in
ter
ac
t
io
n
w
it
h
o
p
ti
m
i
za
tio
n
p
r
o
b
lem
A
.
-
P
ar
am
eter
s
o
f
h
e
u
r
is
tic
r
u
les
P
.
-
I
n
p
u
t to
o
b
tain
d
ata
f
r
o
m
t
h
e
o
p
ti
m
izatio
n
p
r
o
b
lem
b
ein
g
s
o
l
v
ed
I
.
-
Ou
tp
u
t to
s
en
d
s
o
l
u
tio
n
s
o
f
t
h
e
o
p
tim
iza
tio
n
p
r
o
b
le
m
an
d
th
e
f
in
a
l a
lg
o
r
it
h
m
r
e
s
u
lt
O
.
2
.
4
.
P
a
rt
icle
Sw
a
rm
O
pti
m
i
za
t
io
n
T
h
e
P
ar
ticle
S
w
ar
m
Op
ti
m
iza
tio
n
al
g
o
r
ith
m
w
a
s
f
ir
s
t
p
r
o
p
o
s
ed
b
y
J
.
Ke
n
n
ed
y
a
n
d
R
.
E
b
er
h
ar
t
in
1
9
9
5
[
20
]
.
T
h
en
it
w
a
s
i
m
p
r
o
v
ed
b
y
Ke
n
n
ed
y
,
E
b
er
h
ar
t,
an
d
Sh
i
[
21
]
.
P
SO
is
b
ased
o
n
a
b
ir
d
f
lo
ck
s
’
b
eh
av
io
r
.
E
v
er
y
b
ir
d
(
p
ar
t
icle)
co
o
r
d
in
ates
o
w
n
m
o
v
e
m
e
n
t
s
w
it
h
t
h
e
m
o
v
e
m
en
ts
o
f
w
h
o
le
f
lo
ck
s
.
I
n
th
e
P
SO
alg
o
r
ith
m
,
e
v
er
y
p
ar
ticle
is
d
en
o
ted
b
y
a
p
o
s
itio
n
v
ec
to
r
,
a
v
elo
cit
y
v
ec
to
r
,
an
d
a
v
a
lu
e
o
f
t
h
e
cr
iter
io
n
.
T
h
e
v
ec
to
r
s
o
f
p
o
s
itio
n
an
d
v
e
lo
cit
y
o
f
all
p
ar
ticles
ar
e
u
p
d
ated
ac
c
o
r
d
in
g
to
th
e
r
u
les
tak
i
n
g
i
n
to
ac
co
u
n
t
t
h
e
b
est
p
o
s
itio
n
o
f
a
p
ar
ticle,
a
n
d
th
e
b
e
s
t
p
o
s
itio
n
o
f
th
e
w
h
o
le
s
w
ar
m
.
A
ls
o
,
t
h
e
al
g
o
r
it
h
m
u
s
es
i
n
er
tia
w
e
ig
h
t
s
o
f
t
h
e
p
ar
ticles,
v
elo
citie
s
r
es
tr
ictio
n
a
n
d
th
e
s
to
c
h
asti
c
d
ev
iatio
n
s
.
A
cc
o
r
d
in
g
to
t
h
e
s
c
h
e
m
e
o
f
t
h
e
s
w
ar
m
alg
o
r
ith
m
d
escr
ip
tio
n
[
1
5
]
,
th
e
P
SO
alg
o
r
ith
m
m
a
y
b
e
r
ep
r
esen
ted
b
y
a
tu
p
le
{
S
,
M,
A
,
P
,
I
,
O
}.
a.
A
s
et
o
f
p
ar
ticle
s
S
=
{
S
1
,
S
2
,
…,
S
|
S
|
},
|
S
|
i
s
n
u
m
b
er
o
f
p
ar
ticle
s
.
A
t
j
-
t
h
iter
atio
n
i
-
t
h
p
ar
ticle
is
ch
ar
ac
ter
ized
b
y
t
h
e
s
tate
S
ij
=
{
X
ij
,V
ij
,
X
best
ij
},
w
h
er
e
X
ij
=
{
X
1
ij
,
X
2
ij
,
…,
X
n
ij
}
is
th
e
v
ar
iab
le
p
ar
a
m
eter
v
ec
to
r
(
p
ar
ticle
p
o
s
itio
n
)
,
V
ij
=
{
V
1
ij
,
V
2
ij
,
…,
V
n
ij
}
is
th
e
v
e
lo
cit
y
v
e
cto
r
,
X
best
ij
=
{
b
1
ij
,
b
2
ij
,
…,
b
n
ij
}
ar
e
th
e
b
est
(
b
y
v
alu
e)
f
itn
e
s
s
-
f
u
n
ctio
n
s
o
f
t
h
e
p
ar
ticle
p
o
s
itio
n
a
m
o
n
g
all
th
e
p
o
s
itio
n
s
it
to
o
k
d
u
r
in
g
th
e
alg
o
r
ith
m
o
p
er
atio
n
f
r
o
m
th
e
1
st
to
th
e
j
-
t
h
iter
atio
n
s
,
l is t
h
e
n
u
m
b
er
o
f
v
ar
iab
le
p
ar
am
e
ter
s
.
b.
A
m
ea
n
o
f
in
d
ir
ec
t
e
x
ch
a
n
g
e
is
v
ec
to
r
M
=
X
j
best
is
t
h
e
b
est
v
al
u
e
o
f
t
h
e
v
ar
iab
le
p
ar
a
m
eter
s
v
ec
to
r
d
er
iv
ed
a
m
o
n
g
all
p
ar
ticle
s
f
r
o
m
th
e
1
st
to
th
e
j
-
t
h
iter
atio
n
s
o
f
th
e
al
g
o
r
ith
m
.
c.
A
l
g
o
r
ith
m
A
d
escr
ib
es t
h
e
s
te
p
s
o
f
th
e
P
SO a
lg
o
r
it
h
m
.
1)
Gen
er
atio
n
o
f
i
n
it
ial
p
o
p
u
latio
n
(
iter
atio
n
n
u
m
b
er
j
= 1
):
X
i
1
←r
an
d
o
m
(
0
,
1
)
,
i
=1
,
…,
|
S
|
;
V
i
1
←
r
an
d
o
m
(
-
β
,
β
)
,
i
=1
,
…
|
S
|
;
X
i
1
bes
t
←
X
ij
,
i
=
1
,
…
|
S
|
.
w
h
er
e
r
an
d
o
m
(
0
,
1
)
is
th
e
v
ec
to
r
o
f
r
an
d
o
m
n
u
m
b
er
s
w
i
th
d
i
m
e
n
s
io
n
al
it
y
l
(
d
i
m
en
s
io
n
al
it
y
o
f
s
o
lu
tio
n
s
ea
r
ch
s
p
ac
e)
u
n
i
f
o
r
m
l
y
d
i
s
tr
i
b
u
ted
f
r
o
m
0
to
1
.
2)
C
alcu
latio
n
o
f
f
it
n
es
s
-
f
u
n
ctio
n
s
.
T
h
e
cr
iter
io
n
ca
lc
u
latio
n
tak
e
s
p
la
ce
i
n
t
h
e
m
at
h
e
m
atica
l
m
o
d
el
o
f
th
e
p
r
o
b
lem
w
h
er
e
X
ij
v
ec
to
r
s
ar
e
en
ter
ed
f
r
o
m
alg
o
r
it
h
m
s
,
an
d
th
e
r
esu
lt
s
ar
e
r
etu
r
n
ed
to
th
e
alg
o
r
ith
m
th
r
o
u
g
h
t
h
e
i
n
ter
f
ac
e
{
I
,
O
}.
if
f
(
X
ij
)
<
f
(
X
ij
be
st
)
th
en
X
ij
best
←
X
ij
,
i
=
1
,
…,
|
S
|
;
if
f
(
X
ij
)
<
f
(
M
)
th
en
M
←
X
ij
,
i
=
1
,
…,
|
S
|.
3)
P
ar
ticle
s
’
m
o
v
e
m
en
t
w
i
th
r
esp
ec
t to
th
e
to
ler
an
ce
r
eg
io
n
a
n
d
to
th
e
v
elo
cit
y
li
m
itatio
n
:
V
ij+
1
←
V
ij
ω+
α
1
(
X
ij
best
–
X
ij
)
r
an
d
o
m
(
0
,
1
)
+
α
2
(
M
–
X
ij
)
r
an
d
o
m
(
0
,
1
)
,
i
=
1
,
…,
|
S
|,
(
4
)
if
V
ij
+1
>
β
t
h
en
V
ij
+1
←
β
,
i
=
1
,
…,
|
S
|
,
if
V
ij
+1
<
–
β
th
e
n
V
ij
+1
←
–
β
,
i
=
1
,
…,
|
S
|.
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.
9
,
No
.
5
,
Octo
b
er
2
0
1
9
:
3
9
6
7
-
3974
3970
w
h
er
e
α
1
,
α
2
,
ω
,
V
max
ar
e
alg
o
r
i
th
m
p
ar
a
m
eter
s
.
X
ij
+1
←
X
ij
+V
ij
+1
,
i
=1
,
…,
|
S
|.
if
X
ij
+
1
>1
th
e
n
X
ij
+1
←
1
,
i
=1
,
…,
|
S
|;
if
X
ij
+1
<
0
th
e
n
X
ij
+1
←
0
,
i
=1
,
…,
|
S
|.
4)
I
f
at
t
h
e
j
-
th
iter
atio
n
a
s
to
p
-
c
o
n
d
itio
n
i
s
s
atis
f
ied
,
t
h
en
t
h
e
v
alu
e
M
i
s
tr
a
n
s
m
itted
to
o
u
tp
u
t
O
o
r
th
e
tr
an
s
itio
n
to
iter
atio
n
3
.
2
tak
es
p
lace
.
d.
Vec
to
r
P
=
{α
1
,
α
2
,
ω
,
β
}
c
o
m
p
r
i
s
es
t
h
e
co
e
f
f
icie
n
ts
o
f
alg
o
r
ith
m
A
w
h
ic
h
i
n
f
lu
e
n
ce
s
t
h
e
p
ar
ticles
’
m
o
v
e
m
e
n
t
in
t
h
e
s
ea
r
ch
s
p
ac
e
.
C
o
ef
f
ic
ien
t
s
α
1
an
d
α
2
d
e
f
in
e
th
e
d
eg
r
ee
o
f
ac
co
u
n
ti
n
g
t
h
e
in
d
iv
id
u
a
l
an
d
g
r
o
u
p
e
x
p
er
ien
ce
o
f
t
h
e
p
ar
t
icles,
r
esp
ec
ti
v
el
y
.
C
o
ef
f
icie
n
t
ω
ch
ar
ac
ter
izes
i
n
er
tial
p
r
o
p
er
ties
o
f
t
h
e
p
ar
ticles,
an
d
co
ef
f
ic
ien
t
β
d
ef
in
es li
m
i
tatio
n
s
f
o
r
th
e
m
a
x
i
m
u
m
v
elo
cit
y
.
2
.
5
.
B
ee
s
Alg
o
rit
h
m
T
h
e
B
ee
s
A
lg
o
r
ith
m
(
n
o
t
th
e
A
r
ti
f
icia
l
B
ee
C
o
lo
n
y
Op
ti
m
izatio
n
[
2
2
]
)
w
a
s
r
esear
ch
ed
an
d
d
ev
elo
p
ed
b
y
D.
T
.
P
h
am
e
t
a
l.
in
2
0
0
5
[
23
]
.
I
t
is
b
ased
o
n
th
e
s
i
m
u
latio
n
o
f
t
h
e
b
eh
a
v
io
r
o
f
b
ee
s
in
t
h
eir
s
ea
r
ch
i
n
g
f
o
r
n
ec
tar
a
n
d
th
e
i
n
d
ir
ec
t
ex
c
h
an
g
e
o
f
in
f
o
r
m
at
i
o
n
b
et
w
ee
n
b
ee
s
.
B
ee
s
w
ar
m
s
en
d
s
s
ev
er
al
s
co
u
t
s
in
r
a
n
d
o
m
d
ir
ec
tio
n
s
to
s
ea
r
ch
f
o
r
n
ec
tar
.
R
et
u
r
n
in
g
,
s
co
u
t
s
r
ep
o
r
t o
n
th
e
ar
ea
s
f
o
u
n
d
i
n
t
h
e
f
ield
w
it
h
f
lo
w
er
s
co
n
tain
i
n
g
n
ec
tar
,
an
d
o
n
th
e
m
f
l
y
o
u
t
t
h
e
o
th
er
b
ee
s
[
1
5
]
.
I
n
th
is
ca
s
e,
th
e
m
o
r
e
o
n
th
e
s
ite
o
f
n
ec
tar
,
th
e
m
o
r
e
b
ee
s
g
o
to
it.
Ho
w
ev
er
,
th
e
b
ee
s
ca
n
r
an
d
o
m
l
y
d
ev
i
ate
f
r
o
m
t
h
e
ch
o
s
e
n
d
ir
ec
tio
n
.
Af
ter
t
h
e
r
etu
r
n
o
f
all
th
e
b
ee
s
i
n
th
e
h
i
v
e,
in
f
o
r
m
atio
n
ex
c
h
an
g
e
a
n
d
s
en
d
i
n
g
o
f
b
ee
s
ag
ain
.
A
cc
o
r
d
in
g
to
t
h
e
d
escr
ip
tio
n
s
ch
e
m
e
o
f
s
w
ar
m
al
g
o
r
ith
m
s
[
1
5
]
,
th
e
B
A
m
a
y
b
e
r
ep
r
es
en
ted
b
y
a
tu
p
le
{
S
,
M,
A
,
P
,
I
,
O
}.
a.
A
s
e
t
o
f
p
ar
ticle
s
(
b
ee
s
)
S
=
{
S
1
,
S
2
,
…,
S
|
S
|
}.
A
t
t
h
e
j
-
th
ite
r
atio
n
th
e
i
-
t
h
p
ar
ticle
is
ch
ar
ac
ter
ized
b
y
th
e
s
tate
S
ij
={
X
ij
},
w
h
er
e
X
ij
=
X
1
ij
,
X
2
ij
,
…,
X
n
ij
is
th
e
v
ar
iab
le
p
ar
a
m
eter
s
v
ec
to
r
(
th
e
p
ar
ticle
p
o
s
itio
n
)
,
l
is
th
e
d
i
m
en
s
io
n
al
it
y
o
f
th
e
s
o
lu
tio
n
s
ea
r
ch
s
p
ac
e.
b.
Me
an
s
o
f
i
n
d
ir
ec
t e
x
c
h
an
g
e
M
is
a
lis
t o
f
t
h
e
b
est a
n
d
p
er
s
p
ec
tiv
e
p
o
s
itio
n
s
f
o
u
n
d
in
th
e
j
-
t
h
iter
atio
n
,
M
=
{
N
b
ij
,
N
g
kj
},
i
=
1
,
.
.
.
,
n
b
,
k
=
1
,
.
.
.
,
n
g
.
c.
A
l
g
o
r
ith
m
A
d
escr
ib
es t
h
e
s
te
p
s
o
f
th
e
BA
.
1)
Gen
er
atio
n
o
f
i
n
it
ial
p
o
p
u
latio
n
(
j
= 1
)
is
f
u
l
f
il
led
o
n
l
y
f
o
r
a
s
u
b
s
et
o
f
s
co
u
t p
ar
ticles
:
X
i
1
←
r
an
d
o
m
(
0
,
1
)
,
i
=
1
,
…,
n
s
w
h
er
e
n
s
is
th
e
n
u
m
b
er
o
f
s
co
u
t
p
ar
ticle
s
.
Oth
er
p
ar
ticle
s
ar
e
co
n
s
id
er
ed
as
in
ac
tiv
e
th
is
ti
m
e
(
o
n
l
y
a
t
th
e
f
ir
s
t iter
atio
n
)
.
2)
C
alcu
latio
n
o
f
f
it
n
es
s
-
f
u
n
ctio
n
s
.
T
h
e
cr
iter
io
n
ca
lc
u
latio
n
t
ak
es
p
lace
in
th
e
m
ath
e
m
at
ic
al
m
o
d
el
o
f
t
h
e
p
r
o
b
lem
w
h
er
e
X
ij
v
ec
to
r
s
ar
e
en
ter
ed
f
r
o
m
al
g
o
r
ith
m
s
,
a
n
d
th
e
r
es
u
lts
ar
e
r
etu
r
n
ed
t
o
th
e
al
g
o
r
ith
m
th
r
o
u
g
h
t
h
e
i
n
ter
f
ac
e
{
I
,
O
}.
if
f
(
X
ij
)
<
f
(
X
ij
best
)
th
en
X
ij
best
←
X
ij
,
i
=
1
,
…,
|
S
|
.
3)
P
ar
ticle
s
’
m
o
v
e
m
e
n
t.
Am
o
n
g
all
p
ar
ticle
s
,
n
b
p
ar
ticle
s
w
i
th
th
e
b
est
v
alu
e
s
o
f
tar
g
e
t
f
u
n
ct
io
n
ar
e
c
h
o
s
en
,
an
d
th
e
n
,
i
n
t
h
e
r
est
o
f
t
h
e
s
et,
n
g
p
ar
ticle
s
w
it
h
th
e
b
est
v
alu
e
s
ar
e
c
h
o
s
en
.
T
h
e
li
s
ts
o
f
th
e
b
e
s
t
an
d
p
er
s
p
ec
tiv
e
p
o
s
itio
n
s
M
=
{
N
b
ij
,
N
g
kj
}
ar
e
g
e
n
er
ated
u
s
in
g
t
h
e
ch
o
s
e
n
p
ar
ticle
s
.
Her
e
w
it
h
,
t
h
e
d
is
ta
n
c
e
b
et
w
ee
n
an
y
t
w
o
p
o
s
itio
n
s
in
M
o
v
er
ea
ch
co
o
r
d
in
ate
in
t
h
e
s
o
lu
tio
n
s
ea
r
ch
s
p
ac
e
m
u
s
t
b
e
n
o
t
less
t
h
a
n
th
e
v
al
u
es
o
f
p
ar
a
m
eter
rx
.
W
o
r
k
er
p
ar
ticle
s
ar
e
s
en
t
to
th
e
v
ici
n
it
y
o
f
th
e
s
e
p
o
s
itio
n
s
c
b
p
ar
ticle
s
ar
e
s
en
t
to
th
e
v
ici
n
it
y
o
f
ea
c
h
b
est
p
o
s
it
io
n
a
n
d
c
g
p
ar
ticle
s
ar
e
s
e
n
t
to
th
e
v
ici
n
it
y
o
f
ea
c
h
p
er
s
p
ec
tiv
e
p
o
s
itio
n
.
T
h
u
s
,
th
e
p
o
s
itio
n
s
o
f
all
w
o
r
k
er
p
ar
ticle
s
ar
e
d
eter
m
in
ed
as
f
o
llo
w
s
:
X
(
i
-
1)
cb
+
k
j
←
N
b
ij
-
1
+
r
an
d
o
m
(
–
1
,
1
)
∙
r
a
d
,
i
=
1
,
…,
n
b
,
k
=
1
,
…,
c
b
;
X
nbcb
+
(
i
-
1)
c
b
+
k
j
←
N
b
ij
-
1
+
r
an
d
o
m
(
–
1
,
1
)
∙
r
ad
,
i
=
1
,
…,
n
g
,
k
=
1
,
…,
c
g
.
w
h
er
e
n
s
, n
b
, n
g
, с
b
,
c
g
,
r
a
d
ar
e
th
e
p
ar
a
m
eter
s
o
f
t
h
e
al
g
o
r
ith
m
.
I
n
th
is
ca
s
e,
s
co
u
t
p
ar
ticle
s
a
r
e
s
en
t
to
r
a
n
d
o
m
p
o
s
itio
n
s
t
h
e
co
o
r
d
in
ates
o
f
w
h
ich
ar
e
r
an
d
o
m
v
a
lu
e
s
u
n
i
f
o
r
m
l
y
d
is
tr
ib
u
ted
in
t
h
e
to
ler
an
ce
r
an
g
e
:
X
nb∙
cb+
ng∙
cg+
i
j
←
r
an
d
o
m
(
–
1
,
1
)
∙
r
a
d
,
i
=
1
,
…,
n
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:
2088
-
8708
S
w
a
r
m
a
lg
o
r
ith
ms in
d
yn
a
mic
o
p
timiz
a
tio
n
p
r
o
b
lem
o
f rea
cti
ve
p
o
w
er c
o
mp
en
s
a
tio
n
u
n
its
....
(
V
.
Z.
Ma
n
u
s
o
v)
3971
Fin
all
y
,
t
h
e
li
m
itatio
n
o
f
s
ea
r
c
h
s
p
ac
e
is
ch
ec
k
ed
:
if
X
ij+
1
>
1
th
e
n
X
ij
+1
←
1
,
i
=
1
,
…,
|
S
|
if
X
ij
+1
<
0
th
e
n
X
ij
+1
←
0
,
i
=
1
,
…,
|
S
|
4)
I
f
at
th
e
j
-
th
iter
atio
n
a
s
to
p
-
co
n
d
itio
n
i
s
s
a
tis
f
ied
,
t
h
en
t
h
e
v
al
u
e
i
s
tr
a
n
s
m
itted
to
o
u
tp
u
t
O
,
o
r
t
h
e
tr
an
s
itio
n
to
iter
atio
n
3
.
2
tak
es
p
lace
.
d.
A
l
g
o
r
ith
m
p
ar
a
m
eter
s
u
s
ed
in
th
is
d
escr
ip
tio
n
f
o
r
m
v
ec
to
r
P
=
{
n
s
,
n
b
,
n
g
,
с
b
,
c
g
,
r
a
d
,
r
x
}.
T
h
e
co
ef
f
icien
t
r
a
d
d
ef
in
e
s
p
ar
ticle
s
ca
tter
i
n
g
in
s
e
n
d
in
g
to
th
e
b
est
a
n
d
p
r
o
s
p
ec
tiv
e
p
o
s
itio
n
s
.
T
h
e
co
e
f
f
icien
t
rx
d
e
f
i
n
es
th
e
m
in
i
m
u
m
p
o
s
s
ib
le
d
is
ta
n
c
es
b
et
w
ee
n
t
h
ese
p
o
s
itio
n
s
.
T
h
e
v
al
u
e
f
o
r
th
e
ex
p
r
es
s
io
n
n
s
+
n
b
с
b
+
n
g
с
g
is
eq
u
al
to
t
h
e
to
tal
n
u
m
b
er
o
f
t
h
e
s
w
ar
m
p
ar
ticle
s
(|
S
|
=
n
s
+
n
b
с
b
+
n
g
с
g
)
.
T
h
e
s
elec
tio
n
o
f
t
h
e
al
g
o
r
ith
m
p
ar
am
eter
s
h
ea
v
il
y
a
f
f
ec
t
s
th
e
q
u
alit
y
o
f
d
er
iv
ed
s
o
lu
tio
n
s
,
s
o
to
in
cr
ea
s
e
th
e
alg
o
r
ith
m
e
f
f
icien
c
y
,
it
i
s
n
ec
es
s
ar
y
to
ad
ap
t p
ar
am
eter
s
.
2
.
6
.
Appl
ica
t
io
n o
f
t
he
s
w
a
r
m
a
lg
o
rit
h
m
s
t
o
t
he
pro
ble
m
co
n
s
idered
I
n
t
h
e
d
escr
ip
tio
n
s
o
f
t
h
e
SI
al
g
o
r
ith
m
s
,
w
e
u
s
ed
co
n
s
tr
ai
n
ts
o
f
t
h
e
s
ea
r
c
h
s
p
ac
e
f
r
o
m
0
to
1
f
o
r
ea
ch
d
i
m
en
s
io
n
.
T
h
e
in
tr
o
d
u
ce
d
r
estrictio
n
s
ar
e
n
ec
es
s
ar
y
f
o
r
s
u
cc
ess
f
u
l
ad
ap
tatio
n
a
n
d
s
i
m
p
l
e
in
te
g
r
atio
n
o
f
t
h
e
alg
o
r
ith
m
f
o
r
s
o
lv
i
n
g
v
ar
io
u
s
o
p
ti
m
izatio
n
p
r
o
b
le
m
s
.
T
h
e
f
ac
t
i
s
th
a
t
s
o
m
e
SI
p
ar
a
m
ete
r
s
h
a
v
e
a
d
if
f
er
en
t
ef
f
ec
t d
ep
en
d
in
g
o
n
th
e
s
ca
le
o
f
o
p
ti
m
izatio
n
v
ar
iab
les.
T
h
er
ef
o
r
e,
if
a
r
an
g
e
o
f
s
ea
r
c
h
s
p
a
ce
is
f
r
o
m
0
to
1
0
0
,
th
e
d
is
ta
n
ce
f
r
o
m
t
h
e
m
id
d
le
(
5
0
)
to
th
e
h
ig
h
ed
g
e
(
1
0
0
)
w
i
l
l
b
e
7
5
.
W
h
ile
if
t
h
is
d
i
s
tan
ce
is
s
ca
led
f
r
o
m
0
to
1
,
th
en
t
h
e
s
a
m
e
d
i
s
tan
ce
w
ill
b
e
eq
u
al
to
0
.
7
5
an
d
th
e
d
eg
r
ee
o
f
attr
ac
tio
n
b
et
w
ee
n
t
h
e
p
ar
ticle
s
w
o
u
ld
b
e
co
m
p
lete
l
y
d
if
f
er
en
t.
At
t
h
e
s
a
m
e
ti
m
e,
i
n
f
ac
t,
t
h
i
s
d
is
ta
n
c
e
is
eq
u
iv
ale
n
t
to
th
e
o
p
ti
m
iz
atio
n
tas
k
.
An
o
t
h
er
ex
a
m
p
le
is
th
e
r
ad
i
u
s
o
f
t
h
e
s
ca
tti
n
g
ar
ea
(
r
ad
)
in
BA
.
Ob
v
io
u
s
l
y
,
th
e
lar
g
er
t
h
e
d
is
tan
ce
b
et
w
ee
n
t
h
e
b
o
u
n
d
ar
ies
o
f
a
s
ea
r
ch
s
p
ac
e
d
ir
ec
tio
n
,
th
e
g
r
ea
ter
s
h
o
u
ld
b
e
th
e
r
ad
iu
s
o
f
t
h
e
s
ca
tter
in
g
ar
e
a.
Mo
r
eo
v
er
,
f
o
r
a
n
o
n
-
h
o
m
o
g
e
n
eo
u
s
s
ea
r
ch
s
p
a
ce
,
it
w
o
u
ld
b
e
n
ec
ess
ar
y
to
in
tr
o
d
u
ce
d
if
f
er
en
t
v
alu
e
s
o
f
th
e
p
ar
a
m
eter
s
f
o
r
d
if
f
er
e
n
t d
ir
ec
tio
n
s
.
As
a
r
es
u
lt,
t
h
e
al
g
o
r
ith
m
p
ar
am
eter
s
w
o
u
ld
h
a
v
e
to
b
e
c
h
an
g
ed
ev
e
n
i
n
t
h
e
ca
s
e
o
f
a
s
i
m
p
le
ch
a
n
g
e
o
f
m
ea
s
u
r
e
m
en
t
u
n
it
s
in
t
h
e
task
(
h
o
u
r
s
to
s
ec
o
n
d
s
,
k
ilo
m
eter
s
to
f
ee
t,
etc.
)
.
T
o
av
o
id
th
is
,
w
e
s
u
g
g
e
s
t
s
p
ec
if
y
in
g
t
h
e
s
ea
r
ch
s
p
ac
e
b
o
u
n
d
ed
f
r
o
m
0
.
0
to
1
.
0
in
all
d
ir
ec
tio
n
s
i
n
to
th
e
al
g
o
r
it
h
m
.
An
d
m
ap
p
i
n
g
o
f
t
h
e
p
ar
ticle
'
s
co
o
r
d
in
ate
f
r
o
m
t
h
e
alg
o
r
ith
m
in
to
t
h
e
v
al
u
es
o
f
th
e
o
p
ti
m
ized
v
ar
iab
le
s
b
ein
g
s
o
lv
ed
.
I
n
th
e
s
i
m
p
le
s
t v
ar
ia
n
t,
t
h
is
ca
n
b
e
d
o
n
e
as f
o
llo
w
s
.
W
e
d
en
o
te
th
e
co
o
r
d
in
ate
v
ec
to
r
o
f
a
p
ar
ticle
X
={
X
1
,
X
2
,
…,
X
L
},
an
d
t
h
e
v
ec
to
r
o
f
o
p
ti
m
ized
v
ar
iab
les
Q
=
{
Q
1
,
Q
2
,
…,
Q
L
}.
Un
d
er
t
h
e
r
estrict
io
n
s
α
i
≤
q
i
≤
b
i
,
w
e
o
b
tain
a
m
ap
p
in
g
:
q
i
=
x
i
(
b
i
–
α
i
)
+
α
i
,
i
=
1
,
…,
L
.
Fo
r
th
e
in
ter
ac
tio
n
o
f
t
h
e
alg
o
r
ith
m
s
w
ith
t
h
e
m
o
d
el
o
f
th
e
o
p
tim
izat
io
n
p
r
o
b
lem
d
is
c
u
s
s
ed
,
it
is
n
ec
es
s
ar
y
to
s
et
u
p
a
co
r
r
esp
o
n
d
en
ce
b
et
w
ee
n
p
o
s
itio
n
X
o
f
a
p
ar
ticle
an
d
th
e
v
ec
to
r
s
p
ec
if
y
in
g
t
h
e
C
U
ar
r
an
g
e
m
en
t
in
t
h
e
g
r
id
(
Q
C
U
).
Vec
to
r
X
is
u
s
ed
n
o
t a
s
th
e
C
U
p
o
w
er
v
ec
to
r
,
b
u
t a
s
t
h
e
co
ef
f
icien
ts
’
v
ec
to
r
,
s
o
th
e
p
o
w
er
o
f
ea
c
h
C
U
w
as
d
e
ter
m
i
n
ed
as
t
h
e
p
r
o
d
u
ct
o
f
t
h
e
X
v
ec
to
r
ele
m
e
n
t
a
n
d
t
h
e
ca
lcu
lated
m
a
x
i
m
u
m
allo
w
ab
le
p
o
w
er
o
f
t
h
e
C
U
in
th
e
co
r
r
esp
o
n
d
in
g
n
o
d
e
:
Q
i
=
X
i
Q
maxi
2
.
7
.
T
he
ex
peri
m
e
nt
T
h
e
ex
p
er
im
e
n
t
s
i
m
u
lated
th
e
f
ail
u
r
e
ca
s
es
o
f
o
n
e
o
f
t
h
e
C
U
in
t
h
e
g
r
id
.
I
t
is
n
ec
e
s
s
ar
y
to
c
ar
r
y
o
u
t
a
r
ea
l
-
ti
m
e
au
to
m
atic
ad
j
u
s
t
m
e
n
t
o
f
p
o
w
er
s
o
f
a
n
o
th
er
C
U
to
p
r
eser
v
e
th
e
s
tab
il
it
y
o
f
th
e
s
y
s
te
m
a
n
d
m
in
i
m
iz
e
th
e
ac
ti
v
e
p
o
w
er
lo
s
s
e
s
.
T
h
er
e
ar
e
t
w
o
alter
n
ati
v
e
w
a
y
s
in
t
h
e
ca
s
e
o
f
u
s
i
n
g
t
h
e
p
o
p
u
latio
n
-
b
a
s
ed
alg
o
r
ith
m
s
f
o
r
d
y
n
a
m
icall
y
ch
a
n
g
i
n
g
o
p
ti
m
izatio
n
p
r
o
b
lem
s
.
a.
"
R
estar
t
"
.
I
f
co
n
d
itio
n
s
o
f
an
o
p
tim
izatio
n
tas
k
ch
a
n
g
e,
th
en
an
o
p
tim
iza
tio
n
alg
o
r
it
h
m
r
u
n
s
ag
ain
to
s
o
lv
e
th
is
n
e
w
tas
k
.
So
d
y
n
a
m
icall
y
ch
an
g
i
n
g
tas
k
i
s
co
n
s
id
er
ed
as
a
s
et
o
f
d
if
f
er
en
t
s
tat
ic
o
p
tim
izatio
n
tas
k
s
.
On
th
e
o
n
e
h
a
n
d
,
it
ig
n
o
r
es
th
e
alg
o
r
ith
m
e
x
p
er
ien
ce
f
o
u
n
d
b
y
s
o
l
v
i
n
g
t
h
e
tas
k
b
ef
o
r
e
co
n
d
itio
n
ch
an
g
i
n
g
.
On
t
h
e
o
th
er
h
a
n
d
,
th
e
al
g
o
r
ith
m
ca
n
g
et
o
u
t o
f
a
lo
ca
l e
x
tr
e
m
u
m.
b.
"
W
ith
o
u
t
r
estar
t
"
.
I
f
co
n
d
itio
n
s
o
f
an
o
p
ti
m
izat
io
n
tas
k
c
h
a
n
g
e,
an
o
p
ti
m
izat
io
n
al
g
o
r
ith
m
d
o
es
n
o
t
s
tar
t
f
r
o
m
s
cr
atc
h
,
b
u
t
f
r
o
m
t
h
e
cu
r
r
en
t
s
tate.
I
n
th
i
s
in
s
tan
ce
,
t
h
er
e
is
a
ch
an
ce
to
q
u
ick
l
y
f
i
n
d
an
ef
f
ec
ti
v
e
s
o
lu
tio
n
to
th
e
tas
k
m
o
d
i
f
ied
,
b
u
t th
er
e
is
a
r
is
k
o
f
g
etti
n
g
s
t
u
ck
i
n
a
cu
r
r
en
t lo
ca
l e
x
tr
e
m
u
m
.
Du
r
in
g
t
h
e
ex
p
er
i
m
e
n
t
s
,
ea
ch
o
f
th
e
al
g
o
r
ith
m
s
co
n
s
id
er
ed
(
P
SO,
BA
)
s
o
lv
ed
th
e
o
p
ti
m
iz
atio
n
tas
k
(1
-
3
)
s
ep
ar
ately
.
T
h
e
ex
p
er
i
m
en
ts
f
o
r
ea
ch
alg
o
r
it
h
m
w
er
e
p
er
f
o
r
m
ed
as f
o
llo
w
:
a.
Fin
d
t
h
e
q
u
asi
-
o
p
ti
m
a
l so
lu
tio
n
X
*
o
f
tas
k
(
1
)
w
h
en
all
t
h
e
C
Us ar
e
o
p
er
atio
n
al.
b.
Fo
r
ea
ch
n
o
d
e
i o
f
th
e
g
r
id
t
h
a
t h
as t
h
e
C
U
(
i
=
1
,
…,
n
).
1)
Ass
u
m
e
th
at
t
h
e
C
U
i
r
ef
u
s
ed
a
n
d
ce
ased
to
f
u
n
ctio
n
co
m
p
let
el
y
.
2)
C
o
n
ti
n
u
e
th
e
p
r
o
ce
s
s
o
f
o
p
ti
m
izatio
n
o
f
t
h
e
alg
o
r
it
h
m
f
o
r
th
e
tas
k
m
o
d
i
f
ied
,
i.e
.
u
s
e
t
h
e
r
estar
t.
T
h
e
p
r
o
ce
s
s
is
s
to
p
p
ed
af
ter
2
0
0
0
0
0
iter
atio
n
s
af
ter
th
e
co
n
d
itio
n
s
o
f
t
h
e
tas
k
h
a
v
e
b
ee
n
c
h
an
g
ed
.
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.
9
,
No
.
5
,
Octo
b
er
2
0
1
9
:
3
9
6
7
-
3974
3972
3)
Star
t
t
h
e
o
p
ti
m
izatio
n
al
g
o
r
ith
m
f
r
o
m
s
cr
atch
w
it
h
o
u
t
ta
k
i
n
g
i
n
to
ac
co
u
n
t
th
e
p
r
ev
io
u
s
s
o
lu
tio
n
,
i.e
.
u
s
e
th
e
w
a
y
w
i
th
o
u
t
r
estar
t.
T
h
e
p
r
o
ce
s
s
is
s
to
p
p
ed
af
ter
2
0
0
0
0
0
iter
atio
n
s
af
ter
th
e
co
n
d
it
io
n
s
o
f
t
h
e
tas
k
h
av
e
b
ee
n
c
h
a
n
g
ed
.
As
t
h
e
s
o
lu
t
io
n
o
f
t
h
e
o
p
ti
m
i
za
tio
n
p
r
o
b
le
m
i
s
e
x
p
ec
ted
i
n
r
ea
l
ti
m
e,
it
i
s
i
m
p
o
r
tan
t
h
o
w
q
u
ic
k
l
y
a
q
u
asi
-
o
p
ti
m
al
s
o
lu
tio
n
to
th
e
task
w
ill
b
e
f
o
u
n
d
a
f
ter
c
h
an
g
i
n
g
t
h
e
co
n
d
itio
n
s
o
f
t
h
e
tas
k
.
T
h
er
ef
o
r
e,
th
e
r
es
u
lt
s
w
er
e
r
ec
o
r
d
ed
at
1
0
0
,
5
0
0
,
1
0
0
0
an
d
2
0
0
0
iter
atio
n
s
.
T
h
e
f
in
al
r
esu
lts
o
b
tai
n
ed
af
ter
2
0
0
0
0
0
iter
atio
n
s
ar
e
r
ath
er
o
f
t
h
e
th
e
o
r
etica
l v
alu
e.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
ex
p
er
im
e
n
tal
to
tal
r
es
u
lt
s
av
er
ag
ed
o
v
er
all
t
h
e
C
U
s
ar
e
s
h
o
w
n
i
n
T
ab
le
1
an
d
2
.
T
h
e
f
ir
s
t
co
lu
m
n
d
ef
i
n
es
t
h
e
alg
o
r
it
h
m
u
s
ed
,
th
e
s
ec
o
n
d
o
n
e
s
h
o
w
s
t
h
e
w
a
y
u
s
ed
f
o
r
ac
co
u
n
t
in
g
c
h
an
g
i
n
g
co
n
d
itio
n
s
o
f
th
e
p
r
o
b
le
m
(
w
it
h
r
estar
t
o
r
w
it
h
o
u
t
it).
T
h
e
f
o
llo
w
i
n
g
co
lu
m
n
s
s
h
o
w
t
h
e
v
alu
e
s
o
f
th
e
o
p
ti
m
izatio
n
cr
iter
io
n
(
ac
tiv
e
p
o
w
er
lo
s
s
e
s
,
k
W
)
af
ter
th
e
g
iv
e
n
n
u
m
b
er
o
f
alg
o
r
ith
m
iter
atio
n
s
.
I
ter
at
io
n
s
w
er
e
co
u
n
ted
af
ter
c
h
an
g
i
n
g
t
h
e
co
n
d
itio
n
s
o
f
t
h
e
p
r
o
b
le
m
,
t
h
at
is
,
f
r
o
m
s
t
ep
2
.
1
.
T
ab
le
1
s
h
o
w
s
th
e
d
ev
i
atio
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izatio
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u
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4.
CO
NCLU
SI
O
N
T
h
e
p
r
o
b
lem
o
f
co
n
tr
o
l
o
f
r
ea
ctiv
e
p
o
w
er
s
o
u
r
ce
s
i
n
a
p
o
w
er
s
u
p
p
l
y
s
y
s
te
m
i
s
co
n
s
i
d
er
ed
as
a
d
y
n
a
m
ic
o
p
ti
m
izatio
n
p
r
o
b
lem
s
o
l
v
ed
i
n
r
ea
l
-
t
i
m
e.
T
h
e
cr
iter
io
n
o
f
t
h
e
ta
s
k
is
to
r
ed
u
c
e
th
e
lo
s
s
o
f
ac
tiv
e
p
o
w
er
in
t
h
e
p
o
w
er
s
u
p
p
l
y
s
y
s
te
m
.
A
s
t
h
e
co
n
tr
o
lled
v
ar
iab
les,
t
h
e
ca
p
ac
ities
o
f
th
e
r
ea
cti
v
e
p
o
w
er
co
m
p
e
n
s
at
io
n
u
n
its
ar
e
c
h
o
s
en
.
T
h
e
co
m
p
en
s
a
tio
n
u
n
it
s
’
f
ail
u
r
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e
s
i
m
u
lated
,
lead
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g
t
o
d
y
n
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m
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h
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g
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in
t
h
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d
itio
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s
o
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t
h
e
p
r
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b
le
m
.
T
o
s
o
lv
e
th
e
o
p
ti
m
iza
tio
n
p
r
o
b
lem
,
SI
alg
o
r
ith
m
s
w
er
e
u
s
ed
:
t
h
e
P
ar
ticle
S
w
ar
m
Op
ti
m
iza
tio
n
al
g
o
r
ith
m
a
n
d
th
e
B
ee
s
A
l
g
o
r
ith
m
.
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h
en
s
o
lv
i
n
g
d
y
n
a
m
ic
o
p
ti
m
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n
tas
k
s
,
th
e
s
e
alg
o
r
ith
m
s
ca
n
w
o
r
k
eit
h
er
p
er
m
an
e
n
tl
y
,
r
ea
ctin
g
to
all
ch
a
n
g
e
s
in
t
h
e
co
n
d
itio
n
s
o
f
t
h
e
p
r
o
b
lem
,
o
r
r
estar
t
ea
ch
ti
m
e
th
e
co
n
d
it
io
n
s
c
h
an
g
e.
T
h
ese
t
w
o
o
p
tio
n
s
ca
n
b
e
d
esig
n
ated
"
w
ith
o
u
t r
estar
ti
n
g
"
an
d
"
w
i
t
h
r
estar
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.
T
h
e
ex
p
er
im
e
n
t
s
s
h
o
w
ed
th
a
t
f
o
r
th
e
P
ar
ticle
S
w
ar
m
Op
ti
m
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n
alg
o
r
it
h
m
i
t
is
n
ec
e
s
s
a
r
y
to
d
o
a
r
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t,
o
th
er
w
is
e,
th
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alg
o
r
it
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m
d
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n
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co
m
e
o
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t
o
f
t
h
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l
o
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l
ex
tr
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m
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m
s
in
ce
t
h
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p
ar
ti
cles
o
f
th
e
P
ar
ticle
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w
ar
m
Op
ti
m
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o
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te
n
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llect
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ce
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tai
n
a
m
o
u
n
t
i
ter
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s
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n
t
h
e
v
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it
y
o
f
o
n
e
e
x
tr
e
m
u
m
,
r
ef
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T
ab
les
1
,
2
.
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h
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p
er
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t
r
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lt
s
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o
w
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th
e
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s
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lg
o
r
it
h
m
,
w
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ile
t
h
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o
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tio
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h
o
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t
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estar
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as
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h
tl
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o
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b
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1
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W
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r
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tly
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n
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tic
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g
o
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h
m
,
th
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ticle
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o
f
t
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e
B
ee
s
A
lg
o
r
ith
m
ar
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al
w
a
y
s
d
is
p
er
s
ed
o
v
er
s
e
v
er
al
e
x
tr
e
m
e
s
,
w
h
ic
h
allo
w
s
b
o
t
h
to
ta
k
e
in
to
ac
co
u
n
t
b
ef
o
r
e
f
o
u
n
d
s
o
l
u
tio
n
s
an
d
to
f
in
d
n
e
w
o
n
es.
As
th
e
f
u
tu
r
e
w
o
r
k
,
it
is
p
lan
n
ed
to
in
v
est
ig
ate
t
h
e
SI
alg
o
r
it
h
m
s
f
o
r
p
r
o
b
lem
o
f
th
e
o
p
ti
m
a
l
co
n
tr
o
l
i
n
m
icr
o
g
r
id
s
.
Fo
r
m
icr
o
g
r
id
s
t
h
e
o
p
er
atio
n
al
co
n
tr
o
l
a
n
d
r
ea
ctiv
e
p
o
w
er
s
h
ar
in
g
ar
e
r
ea
ll
y
ac
t
u
al
an
d
i
m
p
o
r
ta
n
t [
2
4
,
2
5
]
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
r
esear
ch
w
a
s
ca
r
r
ied
o
u
t
u
n
d
er
t
h
e
State
Ass
ig
n
m
en
t
o
f
t
h
e
Mi
n
is
tr
y
o
f
E
d
u
ca
tio
n
a
n
d
Scie
n
ce
o
f
th
e
R
u
s
s
ia
n
Fed
er
atio
n
,
P
r
o
j
ec
t 8
.
6
8
0
9
.
2
0
1
7
/8
.
9
.
RE
F
E
R
E
NC
E
S
[1
]
J.
W
.
Dix
o
n
a
n
d
L.
A
.
M
o
ra
n
,
“
R
e
a
c
ti
v
e
p
o
we
r
c
o
m
p
e
n
sa
ti
o
n
tec
h
n
o
l
o
g
ies
,
”
Acc
e
ss
S
c
ien
c
e
,
2
0
1
8
.
[2
]
A
.
A
u
g
u
stin
e
,
e
t
a
l.
,
“
V
o
lt
a
g
e
re
g
u
latio
n
o
f
S
TAT
COM
u
sin
g
f
u
z
z
y
s
e
l
f
tu
n
in
g
P
I
c
o
n
tr
o
ll
e
r,
”
in
Circ
u
it
,
Po
we
r
a
n
d
Co
m
p
u
ti
n
g
T
e
c
h
n
o
lo
g
ies
(
ICCPCT
),
Na
g
e
rc
o
il
,
p
p
.
1
-
7
,
2
0
1
6
.
[3
]
F
.
C.
L
u
a
n
d
Y.
Y.
Hs
u
,
“
F
u
z
z
y
d
y
n
a
m
i
c
p
ro
g
ra
m
m
in
g
a
p
p
ro
a
c
h
to
re
a
c
ti
v
e
p
o
w
e
r/
v
o
lt
a
g
e
c
o
n
tro
l
in
a
d
istri
b
u
ti
o
n
su
b
sta
ti
o
n
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r
S
y
ste
ms
,
v
o
l.
1
2
,
p
p
.
6
8
1
-
6
8
8
,
1
9
9
7
.
[4
]
C
.
A
.
Ra
jan
a
n
d
M
.
R.
M
o
h
a
n
,
“
A
n
Ev
o
lu
ti
o
n
a
ry
P
r
o
g
ra
m
m
in
g
B
a
se
d
T
a
b
u
S
e
a
rc
h
M
e
th
o
d
f
o
r
S
o
lv
in
g
th
e
Un
it
Co
m
m
it
m
e
n
t
P
ro
b
lem
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r
S
y
ste
ms
,
v
o
l.
1
9
,
p
p
.
5
7
7
-
5
8
5
,
2
0
1
4
.
[5
]
A.
H.
M
a
n
taw
y
,
e
t
a
l.
,
“
In
teg
ra
ti
n
g
G
e
n
e
ti
c
A
lg
o
rit
h
m
s,
Tab
u
S
e
a
rc
h
,
a
n
d
S
im
u
late
d
A
n
n
e
a
li
n
g
f
o
r
th
e
Un
it
Co
m
m
it
m
e
n
t
P
ro
b
lem
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r
S
y
ste
ms
,
v
o
l.
1
4
,
p
p
.
8
2
9
-
8
3
6
,
1
9
9
9
.
[6
]
D.
De
rv
a
n
i
a
n
d
J.
P
.
R
o
se
ly
n
,
“
Ge
n
e
ti
c
a
lg
o
rit
h
m
b
a
se
d
re
a
c
ti
v
e
p
o
w
e
r
d
isp
a
tch
f
o
r
v
o
lt
a
g
e
sta
b
il
it
y
i
m
p
ro
v
e
m
e
n
t,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
Po
we
r
&
En
e
rg
y
S
y
ste
ms
,
v
o
l.
3
2
,
p
p
.
1
1
5
1
-
1
1
5
6
,
2
0
1
0
.
[7
]
V
.
Z.
M
a
n
u
s
o
v
,
e
t
a
l
.
,
“
Im
p
lem
e
n
tatio
n
o
f
P
o
p
u
lat
io
n
A
lg
o
rit
h
m
s
to
M
i
n
im
ize
P
o
w
e
r
L
o
ss
e
s
a
n
d
Ca
b
le
Cro
ss
-
S
e
c
ti
o
n
in
P
o
w
e
r
S
u
p
p
ly
S
y
st
e
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
i
n
e
e
rin
g
,
v
o
l.
6
,
p
p
.
2
9
5
5
-
2
9
6
1
,
2
0
1
6
.
[8
]
M
d
.
I
.
A
z
im
a
n
d
M
d
.
F
.
Ra
h
m
a
n
,
“
Ge
n
e
ti
c
A
lg
o
rit
h
m
Ba
s
e
d
Re
a
c
t
iv
e
P
o
w
e
r
M
a
n
a
g
e
m
e
n
t
b
y
S
V
C,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
t
e
r E
n
g
i
n
e
e
rin
g
,
v
o
l.
4
,
p
p
.
2
0
0
-
2
0
6
,
2
0
1
4
.
[9
]
H.
Yo
sh
i
d
a
,
e
t
a
l
.
,
“
P
a
rti
c
le
sw
a
rm
o
p
ti
m
iza
ti
o
n
f
o
r
re
a
c
ti
v
e
p
o
w
e
r
a
n
d
v
o
lt
a
g
e
c
o
n
tr
o
l
c
o
n
sid
e
ri
n
g
v
o
lt
a
g
e
se
c
u
rit
y
a
ss
e
ss
m
e
n
t,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
P
o
we
r S
y
ste
m,
v
o
l.
1
5
,
p
p
.
1
2
3
2
-
1
2
3
9
,
2
0
0
0
.
[1
0
]
J.
J.
Ja
m
ian
,
e
t
a
l.
,
“
A
Ne
w
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
T
e
c
h
n
iq
u
e
in
Op
ti
m
izin
g
S
ize
o
f
Distrib
u
te
d
G
e
n
e
ra
ti
o
n
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l.
1
,
p
p
.
1
3
7
-
1
4
6
,
2
0
1
2
.
[1
1
]
M.
N.
Da
z
a
h
ra
,
“
Op
ti
m
a
l
L
o
c
a
ti
o
n
o
f
S
V
C
u
si
n
g
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
a
n
d
V
o
lt
a
g
e
S
ta
b
il
it
y
In
d
e
x
e
s,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l.
6
,
p
p
.
2
5
8
1
-
2
5
8
8
,
2
0
1
6
.
[1
2
]
V.
Z.
M
a
n
u
so
v
,
e
t
a
l.
,
“
F
iref
ly
a
l
g
o
rit
h
m
to
o
p
ti
m
a
l
d
istri
b
u
ti
o
n
o
f
re
a
c
ti
v
e
p
o
w
e
r
c
o
m
p
e
n
sa
ti
o
n
u
n
it
s,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
t
ric
a
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l.
8
,
p
p
.
1
7
5
8
-
1
7
6
5
,
2
0
1
8
.
[1
3
]
M
.
L
a
k
sh
m
i
a
n
d
A
.
R
.
Ku
m
a
r,
“
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
w
e
r
Disp
a
tc
h
u
sin
g
Cro
w
se
a
rc
h
A
lg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
t
e
r E
n
g
i
n
e
e
rin
g
,
v
o
l.
8
,
p
p
.
1
4
2
3
-
1
4
3
1
,
2
0
1
8
.
[1
4
]
S
.
S
.
Re
d
d
y
,
“
Op
ti
m
a
l
Re
a
c
ti
v
e
P
o
w
e
r
S
c
h
e
d
u
li
n
g
Us
i
n
g
Cu
c
k
o
o
S
e
a
rc
h
A
lg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l.
7
,
p
p
.
2
3
4
9
-
2
3
5
6
,
2
0
1
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
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,
Octo
b
er
2
0
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3974
[1
5
]
V.
Z.
M
a
n
u
so
v
,
e
t
a
l.
,
“
S
w
a
rm
in
telli
g
e
n
c
e
a
lg
o
rit
h
m
s
f
o
r
th
e
p
ro
b
lem
o
f
th
e
o
p
t
im
a
l
p
lac
e
m
e
n
t
a
n
d
o
p
e
ra
ti
o
n
c
o
n
tro
l
o
f
re
a
c
ti
v
e
p
o
w
e
r
so
u
rc
e
s
in
t
o
p
o
w
e
r
g
rid
s,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
De
sig
n
&
Na
tu
re
a
n
d
Eco
d
y
n
a
mic
s
,
v
o
l
.
1
2
,
p
p
.
1
0
1
-
1
1
2
,
2
0
1
7
.
[1
6
]
A
.
P
.
E
n
g
e
lb
re
c
h
t,
“
F
u
n
d
a
m
e
n
tal
s o
f
Co
m
p
u
tatio
n
a
l
S
w
a
r
m
In
telli
g
e
n
c
e
,
”
Jo
h
n
W
il
e
y
&
S
o
n
s
,
p
p
.
6
7
2
,
2
0
0
5
.
[1
7
]
L
.
L
iu
,
e
t
a
l.
,
“
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
w
it
h
Co
m
p
o
site
P
a
rti
c
les
in
Dy
n
a
m
ic
En
v
iro
n
m
e
n
ts
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
y
ste
ms
,
M
a
n
,
a
n
d
Cy
b
e
rn
e
ti
c
s
,
Pa
rt
B
,
v
o
l.
4
0
,
p
p
.
1
6
3
4
-
1
6
4
8
,
2
0
1
0
.
[1
8
]
T
.
Blac
k
we
ll
a
n
d
J.
Bra
n
k
e
,
“
M
u
lt
isw
a
r
m
s,
e
x
c
lu
sio
n
,
a
n
d
a
n
t
i
-
c
o
n
v
e
r
g
e
n
c
e
in
d
y
n
a
m
ic
e
n
v
ir
o
n
m
e
n
ts
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Evo
l
u
ti
o
n
a
ry
Co
mp
u
ta
ti
o
n
,
v
o
l
.
1
0
,
p
p
.
4
5
9
-
4
7
2
,
2
0
0
6
.
[1
9
]
K.
W
a
n
g
,
e
t
a
l.
,
“
A
P
ra
c
ti
c
a
l
S
tr
u
c
tu
re
a
n
d
Co
n
tr
o
l
f
o
r
Re
a
c
ti
v
e
P
o
w
e
r
S
h
a
rin
g
in
M
icro
g
ri
d
,
”
IE
EE
T
ra
n
sa
c
ti
o
n
s
o
n
S
ma
rt Gri
d
,
v
o
l.
1
0
,
p
p
.
1
8
8
0
-
1
8
8
8
,
2
0
1
7
.
[2
0
]
J.
Ke
n
n
e
d
y
a
n
d
R.
Eb
e
rh
a
rt,
“
P
a
rti
c
le s
w
a
r
m
o
p
ti
m
iza
ti
o
n
,
”
in
IEE
E
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ne
u
ra
l
Ne
two
rk
s,
v
o
l.
4
,
p
p
.
1
9
4
2
-
1
9
4
8
,
1
9
9
5
.
[2
1
]
R.
C.
Eb
e
rh
a
rt
a
n
d
Y.
S
h
i,
“
P
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
:
d
e
v
e
lo
p
m
e
n
ts,
a
p
p
l
ica
ti
o
n
s
a
n
d
re
so
u
rc
e
s,
”
in
C
o
n
g
re
ss
o
n
Ev
o
lu
t
io
n
a
ry
Co
m
p
u
t
a
ti
o
n
,
2
7
-
3
0
M
a
y
2
0
0
1
;
S
e
o
u
l,
S
o
u
t
h
Ko
re
a
.
IEE
E,
p
p
.
8
1
-
8
6
,
2
0
0
1
.
[2
2
]
D.
Ka
ra
b
o
g
a
,
“
A
n
id
e
a
b
a
se
d
o
n
h
o
n
e
y
b
e
e
sw
a
r
m
f
o
r
n
u
e
rica
l
o
p
t
im
iz
a
ti
o
n
,
”
Erciy
e
s
Un
iv
e
rsit
y
,
K
a
y
se
ri,
T
u
rk
e
y
,
p
p
.
10
,
2
0
0
5
.
[2
3
]
D.
T
.
P
h
a
m
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t
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l.
,
“
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lex
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M
a
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n
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.
[2
4
]
J.
He
,
e
t
a
l.
,
“
A
n
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IEE
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ra
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s P
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[2
5
]
W
.
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,
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t
a
l.
,
“
Re
a
c
ti
v
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P
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sp
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ts
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ty
As
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s,
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ra
n
sa
c
ti
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s
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Po
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r S
y
ste
ms
,
v
o
l.
2
6
,
p
p
.
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2
,
2
0
1
1
.
B
I
O
G
RAP
H
I
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S
O
F
AUTH
O
RS
Va
d
i
m
Z
i
n
o
v
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M
a
n
u
so
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r
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c
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th
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B.
S
.
a
n
d
th
e
P
h
D
d
e
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re
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s
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le
c
tri
c
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in
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rin
g
f
ro
m
No
v
o
sib
irsk
El
e
c
tri
c
T
e
c
h
n
ica
l
In
stit
u
te,
No
v
o
si
b
irsk
,
Ru
ss
ia
i
n
1
9
6
3
a
n
d
1
9
8
6
,
re
s
p
e
c
ti
v
e
l
y
.
He
is
a
P
r
o
f
e
ss
o
r
o
f
th
e
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p
a
rt
m
e
n
t
o
f
In
d
u
strial
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o
w
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r
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u
p
p
ly
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y
ste
m
s
in
No
v
o
sib
irsk
S
t
a
te
T
e
c
h
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ica
l
Un
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e
rsit
y
,
Ru
ss
ia.
His
c
u
rre
n
t
re
se
a
rc
h
a
r
e
a
is
a
rti
f
icia
l
in
telli
g
e
n
c
e
tec
h
n
o
lo
g
ies
a
n
d
p
ro
b
a
b
il
isti
c
m
e
th
o
d
s i
n
e
lec
tri
c
p
o
w
e
r
s
y
ste
m
s.
Pa
v
e
l
V
i
k
to
r
o
v
i
c
h
M
a
tr
e
n
in
r
e
c
e
iv
e
d
th
e
B.
S
.
a
n
d
M
.
S
.
d
e
g
re
e
s
in
f
o
r
m
a
ti
o
n
tec
h
n
o
lo
g
ies
f
ro
m
No
v
o
sib
irsk
S
tate
T
e
c
h
n
ica
l
Un
iv
e
rsit
y
,
No
v
o
sib
irsk
,
Ru
ss
ia
in
2
0
1
2
a
n
d
2
0
1
4
,
re
sp
e
c
ti
v
e
l
y
.
He
is
a
n
a
ss
istan
t
in
No
v
o
sib
irsk
S
tate
Tec
h
n
ica
l
Un
iv
e
rsit
y
.
His
m
a
in
in
tere
sts
a
re
re
late
d
t
o
sto
c
h
a
stic
o
p
ti
m
iza
ti
o
n
m
e
th
o
d
s,
d
e
sig
n
a
n
d
d
e
v
e
lo
p
m
e
n
t
in
f
o
rm
a
ti
o
n
sy
ste
m
s,
a
n
d
a
rti
f
icia
l
in
telli
g
e
n
c
e
tec
h
n
o
lo
g
ies
in
e
lec
tr
ic p
o
w
e
r
s
y
ste
m
s.
Na
sr
u
ll
o
K
h
a
sa
n
z
o
d
a
re
c
e
iv
e
d
th
e
sp
e
c
ialty
d
e
g
re
e
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
f
ro
m
Tajik
T
e
c
h
n
ica
l
Un
iv
e
rsit
y
n
a
m
e
d
a
f
ter
a
c
a
d
e
m
i
c
ian
M
.
S
.
Os
im
i,
Tajik
istan
in
2
0
1
3
.
He
is
a
P
h
D
st
u
d
e
n
t
i
n
No
v
o
sib
irsk
S
tate
T
e
c
h
n
ica
l
Un
iv
e
rsit
y
.
His
m
a
in
in
tere
sts
a
re
re
n
e
w
a
b
le
e
n
e
rg
y
a
n
d
a
p
p
ly
in
g
A
rti
f
icia
l
In
telli
g
e
n
c
e
to
o
p
ti
m
a
l
c
o
n
tro
l
o
f
re
n
e
w
a
b
le res
o
u
rc
e
s.
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