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ts,
a
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s.
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atics
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NT
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th
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m
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ltime
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g
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ex
is
ts
at
least
th
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ee
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ar
ticip
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ts
in
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d
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: th
e
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d
en
t,
th
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u
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r
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th
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ex
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r
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n
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th
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Dep
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t
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ar
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ticip
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ts
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ize
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.
R
elate
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wo
r
k
in
[
1
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u
s
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teg
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m
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ar
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at
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it
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is
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n
d
t
h
at
th
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p
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o
b
lem
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in
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-
co
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d
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co
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d
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p
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s
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v
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ev
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wo
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d
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f
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I
n
th
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w
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k
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th
e
p
ar
ticle
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m
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tim
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(
PS
O)
al
g
o
r
ith
m
in
tr
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d
u
ce
d
b
y
J
.
Ke
n
n
ed
y
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d
R
.
E
b
er
h
ar
d
t is u
s
ed
[
2
]
.
I
n
th
e
PS
O
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o
r
ith
m
,
th
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p
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latio
n
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ca
lled
a
s
war
m
,
a
n
d
ea
ch
in
d
iv
id
u
al
is
ca
lled
p
ar
ticle
[
3
]
.
I
n
[
4
,
5
]
,
th
e
PS
O
alg
o
r
ith
m
s
u
cc
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f
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lly
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p
tim
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d
s
o
lv
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th
e
s
ch
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u
l
in
g
p
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o
b
lem
s
with
m
u
ltip
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co
n
s
tr
ain
ts
.
T
h
e
P
SO
alg
o
r
ith
m
h
as
ex
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llen
t
r
o
b
u
s
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ess
an
d
u
s
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u
l
in
d
if
f
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en
t
a
p
p
licatio
n
en
v
ir
o
n
m
en
ts
with
li
ttle
m
o
d
if
icatio
n
[
6
]
.
T
h
e
PS
O
alg
o
r
ith
m
also
d
eliv
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t
h
e
s
am
e
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tim
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lu
tio
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ate
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th
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alg
o
r
ith
m
s
,
s
u
ch
as
th
e
g
en
etic
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
P
a
r
ticle
s
w
a
r
m
o
p
timiz
a
tio
n
fo
r
s
o
lvin
g
th
esis
d
efen
s
e
timet
a
b
lin
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p
r
o
b
le
(
Gilb
ert C
h
r
is
to
p
h
er
)
763
alg
o
r
ith
m
[
7
]
.
PS
O
alg
o
r
ith
m
also
s
u
cc
es
s
f
u
lly
im
p
lem
en
ted
in
s
o
m
e
co
m
p
u
ter
s
cien
ce
p
r
o
b
lem
,
s
u
ch
as
k
n
ap
s
ac
k
p
r
o
b
lem
[
8
,
9
]
a
n
d
jo
b
-
s
h
o
p
p
r
o
b
lem
[1
0
,
1
1
]
a
n
d
s
o
m
e
r
ea
l
-
life
ca
s
es,
s
u
ch
as
o
p
tim
izatio
n
o
f
r
eser
v
o
ir
o
p
er
atio
n
[
1
2
]
,
task
s
ch
ed
u
lin
g
in
g
r
id
[
1
3
,
1
4
]
r
e
s
o
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r
ce
-
co
n
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tr
ain
e
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p
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ject
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ch
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lin
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[
1
5
]
,
clo
u
d
co
m
p
u
tin
g
[
7
,
1
6
,
1
7
]
,
an
d
em
p
lo
y
ee
s
ch
ed
u
lin
g
[
1
8
]
.
Sch
ed
u
lin
g
is
allo
ca
tin
g
r
eso
u
r
ce
s
in
a
s
p
ec
if
ic
tim
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to
p
r
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ce
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f
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is
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task
.
T
h
e
s
ch
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lin
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p
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o
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lem
is
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c
o
m
p
lex
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m
b
i
n
ato
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ial
p
r
o
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lem
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ec
a
u
s
e
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er
e
is
m
o
r
e
th
an
o
n
e
s
o
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tio
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d
is
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ca
lly
o
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tim
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Sch
ed
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lin
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lem
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if
ied
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a
n
o
n
-
d
eter
m
in
is
tic
p
o
ly
n
o
m
ial
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tim
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ar
d
(
NP
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Har
d
)
p
r
o
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lem
.
I
n
s
ch
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u
lin
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lem
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th
e
r
e
ar
e
two
ty
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o
f
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n
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tr
ain
t:
a
h
a
r
d
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n
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tr
ain
t
an
d
s
o
f
t
c
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n
s
tr
ain
t.
T
h
e
h
ar
d
c
o
n
s
tr
ain
t
is
a
co
n
s
tr
ain
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t
h
at
ca
n
n
o
t
b
e
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a
n
d
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o
f
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co
n
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tr
ain
t
i
s
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s
tr
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th
at
ca
n
b
e
v
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o
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Ho
wev
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v
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m
u
s
t b
e
m
in
im
ized
t
o
g
et
th
e
o
p
tim
al
s
o
lu
tio
n
[
1
9
]
.
T
h
is
p
ap
er
d
ef
in
es
th
e
p
r
o
b
lem
f
o
r
m
u
latio
n
th
at
ap
p
lies
to
th
e
De
p
ar
tm
en
t
o
f
I
n
f
o
r
m
atics
at
Un
iv
er
s
itas
Mu
ltime
d
ia
N
u
s
an
tar
a.
T
h
e
f
itn
ess
f
u
n
ctio
n
s
ta
ilo
r
ed
to
th
e
p
r
o
b
lem
f
o
r
m
u
la
tio
n
ar
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d
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o
p
ed
with
b
o
th
h
a
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d
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d
s
o
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tr
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ts
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T
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e
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tr
y
to
o
p
t
im
ize
th
e
tim
etab
lin
g
p
r
o
ce
s
s
b
y
m
in
im
izin
g
th
e
s
o
f
t
co
n
s
tr
ain
ts
v
io
latio
n
s
.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
is
im
p
le
m
en
te
d
in
a
web
-
b
ased
p
latf
o
r
m
u
s
in
g
th
e
Py
th
o
n
p
r
o
g
r
a
m
m
in
g
la
n
g
u
a
g
e
an
d
t
h
e
Flas
k
f
r
am
ewo
r
k
.
T
h
e
ap
p
licatio
n
is
test
ed
u
s
in
g
r
ea
l
-
wo
r
ld
in
s
tan
ce
s
an
d
ev
alu
ated
u
s
in
g
t
h
e
e
n
d
-
u
s
er
c
o
m
p
u
tin
g
s
atis
f
ac
tio
n
(
E
UC
S)
with
a
6
-
p
o
in
t
L
ik
er
t
s
ca
le
[
2
0
,
2
1
]
.
T
h
e
r
est
o
f
t
hi
s
p
ap
er
is
o
r
g
a
n
ized
as
f
o
llo
ws;
s
ec
tio
n
2
b
r
ief
ly
d
escr
ib
es
th
e
r
esear
ch
m
eth
o
d
u
s
ed
f
o
r
t
h
is
s
tu
d
y
,
in
clu
d
in
g
p
r
o
b
lem
f
o
r
m
u
latio
n
,
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
an
d
th
e
d
esig
n
a
n
d
im
p
lem
en
tati
o
n
wo
r
k
.
Sectio
n
3
d
escr
ib
es
th
e
r
esu
lts
o
f
th
e
s
tu
d
y
a
n
d
th
e
p
e
r
f
o
r
m
an
ce
ev
al
u
atio
n
.
Sectio
n
4
c
o
n
clu
d
es
th
is
p
ap
er
with
s
o
m
e
d
is
cu
s
s
io
n
s
o
n
f
u
tu
r
e
w
o
r
k
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
P
r
o
blem
f
o
rm
ula
t
io
n
I
n
Un
iv
er
s
itas
Mu
ltime
d
ia
Nu
s
an
tar
a
(
UM
N)
,
th
esis
d
ef
en
s
e
s
ess
io
n
s
ar
e
allo
ca
ted
in
to
t
wo
wee
k
s
p
er
b
atch
.
W
ith
in
a
s
in
g
le
b
at
ch
,
th
e
n
u
m
b
er
o
f
s
tu
d
en
ts
g
o
in
g
f
o
r
th
eir
th
esis
d
ef
en
s
e
is
n
o
t
lim
ited
b
y
th
e
d
ep
ar
tm
en
t.
I
n
th
e
Dep
ar
tm
e
n
t
o
f
I
n
f
o
r
m
atics
at
UM
N,
1
8
d
ep
ar
tm
e
n
t
m
em
b
er
s
ar
e
al
l
elig
ib
le
to
b
e
th
e
co
m
m
ittee
o
f
th
e
s
ess
io
n
s
.
Se
s
s
io
n
s
ar
e
m
ee
tin
g
s
wh
er
e
s
tu
d
en
ts
d
ef
en
d
th
eir
th
esis
in
f
r
o
n
t
o
f
a
co
m
m
ittee,
an
d
s
o
m
e
s
ess
io
n
s
m
ig
h
t
o
v
er
lap
in
tim
e
[
1
]
.
Facu
lt
y
m
em
b
er
s
ar
e
ch
ar
ac
ter
ized
b
y
th
eir
ac
ad
em
ic
lev
el
an
d
r
esear
ch
ar
ea
s
.
Stu
d
en
ts
ar
e
allo
wed
to
h
av
e
at
least
o
n
e
s
u
p
er
v
is
o
r
an
d
at
m
o
s
t
two
s
u
p
er
v
is
o
r
s
.
T
h
e
d
ep
ar
tm
en
t
ass
ig
n
s
th
e
ex
am
i
n
er
an
d
m
o
d
er
ato
r
o
f
th
e
s
ess
io
n
s
.
An
o
th
er
co
n
s
id
er
atio
n
is
th
e
q
u
o
ta
f
o
r
ea
c
h
d
ep
ar
tm
en
t
m
em
b
e
r
to
b
ec
o
m
e
a
co
m
m
ittee
o
f
a
s
ess
io
n
.
Dep
ar
tm
en
t
m
em
b
er
th
at
h
o
l
d
s
a
p
o
s
itio
n
in
th
e
u
n
iv
er
s
ity
is
lim
ited
to
a
lo
wer
n
u
m
b
e
r
o
f
s
ess
io
n
s
.
As
cu
s
to
m
ar
y
,
co
n
s
tr
ain
ts
ar
e
d
iv
id
ed
in
to
h
ar
d
an
d
s
o
f
t
o
n
e
s
.
T
h
e
f
o
r
m
er
m
u
s
t
alwa
y
s
b
e
s
atis
f
ied
,
wh
er
ea
s
th
e
latter
co
m
p
o
s
e
th
e
o
b
jectiv
e
f
u
n
ctio
n
th
at
is
o
p
tim
ized
(
m
in
im
ized
)
d
u
r
i
n
g
e
ac
h
iter
atio
n
in
th
e
PS
O.
T
h
er
e
is
o
n
ly
o
n
e
h
ar
d
co
n
s
tr
ain
t
th
at
ap
p
lies
to
all,
wh
ich
is
th
e
tim
e
a
v
ailab
ilit
y
o
f
ea
ch
p
ar
ticip
an
t.
T
h
er
e
m
u
s
t n
o
t e
x
is
t o
v
er
lap
p
i
n
g
s
ess
io
n
s
f
o
r
an
y
o
f
t
h
e
p
ar
ti
cip
an
ts
.
T
h
e
s
o
f
t c
o
n
s
tr
ain
ts
ar
e:
−
Qu
o
ta:
T
h
e
m
ax
im
u
m
n
u
m
b
e
r
o
f
s
ess
io
n
s
th
at
ar
e
allo
ca
ted
f
o
r
ea
ch
o
f
th
e
d
e
p
ar
tm
e
n
t m
e
m
b
er
s
.
−
Aca
d
em
ic
L
ev
el:
T
h
e
ac
a
d
em
i
c
lev
el
o
f
th
e
d
ep
ar
tm
e
n
t m
em
b
er
th
at
is
r
eg
u
lated
b
y
t
h
e
g
o
v
er
n
m
en
t.
−
E
x
p
er
ien
ce
: T
h
e
p
r
e
v
io
u
s
ex
p
er
ien
ce
in
m
o
d
er
atin
g
th
e
s
ess
io
n
s
.
−
R
esear
ch
Ar
ea
: T
h
e
co
n
f
o
r
m
it
y
o
f
th
e
ex
am
in
e
r
'
s
r
esear
ch
ar
ea
s
with
th
e
th
esis
.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
is
o
b
ta
in
ed
b
y
s
u
m
m
in
g
u
p
th
e
v
io
la
tio
n
s
o
f
all
s
o
f
t
co
n
s
tr
ain
ts
.
I
n
p
r
ac
tical
ca
s
es,
th
e
s
ep
ar
atio
n
i
n
h
a
r
d
a
n
d
s
o
f
t
co
n
s
tr
ain
ts
ca
n
b
e
m
o
d
if
ied
b
y
th
e
u
s
er
,
wh
o
co
u
ld
r
e
lax
s
o
m
e
o
f
th
e
h
ar
d
co
n
s
tr
ain
ts
b
y
tu
r
n
i
n
g
th
em
i
n
to
s
o
f
t
o
n
es
an
d
ass
ig
n
in
g
t
h
e
m
a
weig
h
t.
I
t
is
also
p
o
s
s
ib
le
to
ad
d
weig
h
t
f
o
r
ea
ch
o
f
th
e
s
o
f
t
co
n
s
tr
ain
ts
ch
o
s
en
b
y
th
e
u
s
er
s
.
Fo
r
th
e
s
ak
e
o
f
s
im
p
licity
,
th
is
wo
r
k
s
tick
s
to
th
e
class
if
icat
io
n
p
r
o
v
id
e
d
ab
o
v
e.
2
.
2
.
P
a
rt
icle
s
wa
rm
o
ptim
iz
a
t
io
n
I
n
g
en
er
al,
th
e
p
a
r
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
alg
o
r
ith
m
co
n
s
is
ts
o
f
th
r
ee
s
tep
s
: f
ir
s
t,
to
in
itialize
ea
ch
p
ar
ticle’
s
p
o
s
itio
n
an
d
v
e
lo
city
,
s
ec
o
n
d
is
to
u
p
d
ate
th
e
v
elo
city
,
an
d
th
ir
d
is
to
u
p
d
ate
th
e
p
o
s
itio
n
.
T
h
ese
th
r
ee
s
tep
s
ar
e
r
ep
ea
ted
u
n
til
t
h
e
s
to
p
co
n
d
itio
n
i
s
m
et,
o
r
th
e
m
ax
im
u
m
iter
atio
n
is
r
ea
ch
e
d
.
T
h
e
in
itial
p
o
s
itio
n
an
d
v
elo
city
o
f
ea
c
h
p
ar
ticle
ar
e
g
en
er
ated
r
a
n
d
o
m
l
y
u
s
in
g
(
1
)
an
d
(
2
)
wh
er
e
x
r
ep
r
esen
ts
p
o
s
itio
n
an
d
v
r
ep
r
esen
ts
v
elo
city
[
2
2
-
2
5
]
.
0
=
+
(
−
)
(
1
)
0
=
+
(
−
)
(
2
)
T
h
e
v
elo
city
is
u
p
d
ated
b
y
u
s
i
n
g
(
3
)
.
T
h
e
in
er
tia
f
ac
to
r
(
w)
,
co
g
n
itiv
e
lear
n
i
n
g
r
ate
(
c
1
an
d
c
2
)
,
an
d
r
an
d
o
m
n
u
m
b
er
s
(
r
1
an
d
r
2
)
a
r
e
th
e
p
ar
am
eter
s
th
at
in
f
lu
en
ce
t
h
e
u
p
d
ate
o
f
th
e
v
el
o
city
[
2
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
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p
u
t E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
3
,
J
u
n
e
2
0
2
1
:
7
62
-
7
69
764
+
1
=
∗
+
1
+
1
∗
(
−
)
+
2
∗
2
∗
(
−
)
(
3
)
T
h
e
f
in
al
s
tep
in
ea
c
h
iter
atio
n
is
to
u
p
d
ate
ea
ch
p
ar
ticle'
s
p
o
s
itio
n
u
s
in
g
(
4
)
[
2
2
]
.
+
1
=
+
+
1
(
4
)
2
.
3
.
Desig
n a
nd
im
plem
ent
a
t
io
n
Fig
u
r
e
1
s
h
o
ws
th
e
ap
p
licatio
n
wo
r
k
f
lo
w.
First,
th
e
u
s
er
m
u
s
t
in
p
u
t
th
e
d
ata
t
h
at
is
u
s
ed
to
th
e
ap
p
licatio
n
.
Af
ter
th
e
d
ata
en
ter
ed
,
th
e
ap
p
licatio
n
will
s
tar
t
s
ch
ed
u
lin
g
u
s
in
g
th
e
PS
O
al
g
o
r
ith
m
,
b
e
g
in
n
in
g
with
th
e
s
ch
ed
u
le
f
o
r
th
e
th
esis
d
ef
en
s
e,
th
en
th
e
ex
am
in
er
,
an
d
th
e
m
o
d
er
at
o
r
o
f
th
e
th
esis
d
ef
en
s
e.
Af
ter
th
e
s
ch
ed
u
lin
g
p
r
o
ce
s
s
is
d
o
n
e,
th
e
o
p
tim
ized
s
ch
ed
u
le
will
b
e
s
h
o
wn
b
y
th
e
ap
p
licatio
n
.
T
h
er
e
ar
e
th
r
ee
f
itn
ess
f
u
n
ctio
n
s
d
e
v
elo
p
ed
an
d
u
s
ed
in
th
is
r
esear
ch
.
T
h
e
f
ir
s
t
f
itn
e
s
s
f
u
n
ctio
n
(
f
Su
p
e
r
v
is
o
r
)
d
ef
i
n
ed
b
y
(
5
)
is
to
s
et
th
e
in
itial
s
ch
ed
u
le
c
o
n
s
is
tin
g
o
f
th
e
s
tu
d
e
n
t
an
d
th
e
s
u
p
er
v
is
o
r
.
T
h
e
s
ec
o
n
d
f
itn
ess
f
u
n
ctio
n
(
f
E
x
a
m
in
er
)
d
ef
in
ed
b
y
(
6
)
is
f
o
r
s
ch
e
d
u
li
n
g
th
e
ex
am
i
n
er
.
T
h
e
t
h
ir
d
f
itn
ess
f
u
n
ctio
n
(
f
Mo
d
er
ato
r
)
d
e
f
in
ed
b
y
(
7
)
is
f
o
r
s
ch
ed
u
lin
g
th
e
m
o
d
e
r
ato
r
.
T
h
e
g
o
al
is
to
f
in
d
th
e
g
l
o
b
al
m
in
i
m
u
m
f
o
r
ea
ch
o
f
th
ese
f
itn
ess
f
u
n
ctio
n
s
.
=
1
+
2
(
5
)
−
1
∈
{
0
,
1
}
∶
1
ℎ
ℎ
,
0
ℎ
−
2
∈
{
0
,
1
}
∶
1
ℎ
,
0
ℎ
=
1
+
2
+
3
+
4
+
5
+
6
(
6
)
−
1
∈
{
0
,
1
}
∶
1
ℎ
ℎ
,
0
ℎ
−
2
∈
{
0
,
1
}
∶
1
ℎ
′
,
0
ℎ
−
3
∈
{
0
,
1
}
∶
1
ℎ
ℎ
,
0
ℎ
−
4
∈
{
0
,
1
}
∶
1
ℎ
ℎ
,
0
ℎ
−
5
∈
{
0
,
1
}
∶
1
ℎ
′
,
0
ℎ
−
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u
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r
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mb
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.
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.
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9
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h
e
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r
eq
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ate
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le
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6
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ased
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.
RE
F
E
R
E
NC
E
S
[1
]
M
.
Ba
tt
istu
t
ta,
S
.
Ce
sc
h
ia,
F
.
D
e
Ce
sc
o
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L
.
D
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G
a
sp
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ro
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a
n
d
A
.
S
c
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rf,
“
M
o
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g
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s
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lv
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e
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e
sis
d
e
fe
n
se
ti
m
e
tab
li
n
g
p
r
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b
lem
∗
,
”
J
.
O
p
e
r
.
R
e
s
.
S
o
c
.
,
v
o
l.
7
0
,
n
o
.
7
,
2
0
1
9
.
[2
]
B.
Ch
o
p
a
r
d
a
n
d
M
.
T
o
m
a
ss
in
i,
“
P
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
,
”
in
:
A
n
In
tro
d
u
c
ti
o
n
t
o
M
e
ta
h
e
u
ristics
f
o
r
Op
ti
miza
ti
o
n
.
Na
tu
ra
l
Co
m
p
u
ti
n
g
S
e
rie
s.
S
p
rin
g
e
r,
Ch
a
m
,
2
0
1
8
.
[3
]
X.
C
a
i
a
n
d
Z
.
Cu
i
,
“
Hu
n
g
ry
p
a
rti
c
le sw
a
rm
o
p
ti
m
iza
ti
o
n
,
”
ICIC
Ex
p
re
ss
L
e
tt
.
,
v
o
l
.
4
,
n
o
.
3
,
2
0
1
0
.
[4
]
S
.
M
.
El
sa
y
e
d
,
R.
A
.
S
a
rk
e
r,
a
n
d
E
.
M
e
z
u
ra
-
M
o
n
tes
,
“
S
e
lf
-
a
d
a
p
ti
v
e
m
i
x
o
f
p
a
rti
c
le
sw
a
rm
m
e
t
h
o
d
o
l
o
g
ies
fo
r
c
o
n
stra
in
e
d
o
p
ti
m
iza
ti
o
n
,
”
In
f
o
rm
a
ti
o
n
S
c
ien
c
e
s,
2
0
1
4.
[5
]
K.
E
.
P
a
rso
p
o
u
lo
s
a
n
d
M
.
N
.
Vra
h
a
ti
s,
“
P
a
rti
c
le
S
wa
rm
Op
ti
m
iza
ti
o
n
M
e
t
h
o
d
f
o
r
C
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n
stra
in
e
d
Op
ti
m
iza
ti
o
n
P
ro
b
lem
s,”
Fro
n
ti
e
rs
in
Arti
fi
c
i
a
l
In
telli
g
e
n
c
e
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
v
o
l
.
76
,
p
p
.
2
1
4
-
2
2
0
,
2
0
0
2
.
[6
]
D.
Wan
g
,
D
.
Tan
,
a
n
d
L
.
Li
u
,
“
P
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rm
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[7
]
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1
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3
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4
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5
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1
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2
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3
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4
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,
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6
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.
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d
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D.
Ch
a
tz
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.
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.
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