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Fu
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Par
ticle
s
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T
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CC B
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C
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jid
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Ad
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Dep
ar
tm
en
t o
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C
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p
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Scie
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,
Un
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I
lo
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Nig
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I
lo
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m
ail: a
d
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p
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@
u
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.
e
d
u
.
n
g
1.
I
NT
RO
D
UCT
I
O
N
Par
ticle
s
war
m
o
p
tim
izatio
n
,
a
p
r
o
m
in
en
t
m
eta
h
eu
r
is
tic
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eth
o
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in
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ce
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e
co
llectiv
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s
in
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s
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in
s
ch
o
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,
was
in
t
r
o
d
u
ce
d
b
y
Ken
n
ed
y
an
d
E
b
er
h
a
r
t
in
1
9
9
5
[
1
]
.
T
h
e
PS
O
is
o
n
e
ex
am
p
le
o
f
a
p
o
p
u
latio
n
-
b
ased
s
to
ch
asti
c
o
p
tim
izatio
n
tech
n
iq
u
e
th
at
h
as
d
em
o
n
s
tr
ated
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em
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k
ab
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s
u
cc
ess
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r
o
s
s
d
iv
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s
e
d
o
m
ain
s
,
lik
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en
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in
ee
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in
g
d
esig
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,
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ac
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in
e
lear
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g
,
c
o
n
tr
o
l
s
y
s
tem
s
,
an
d
f
in
an
cial
m
o
d
elin
g
[
2
]
.
Par
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
is
p
o
p
u
lar
f
o
r
its
s
i
m
p
licity
,
m
in
im
al
m
ath
em
atica
l
eq
u
atio
n
s
,
an
d
its
f
lex
ib
ilit
y
in
ad
ap
tatio
n
.
Ho
wev
er
,
th
e
s
tan
d
ar
d
PS
O
f
ac
es
ch
allen
g
es,
esp
ec
ially
in
b
alan
cin
g
e
x
p
lo
r
atio
n
an
d
e
x
p
lo
itatio
n
.
Hen
c
e,
m
ain
tain
in
g
p
o
p
u
latio
n
d
iv
er
s
ity
an
d
av
o
id
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n
g
p
r
em
atu
r
e
c
o
n
v
e
r
g
en
ce
i
n
m
u
l
tim
o
d
al
co
m
p
le
x
p
r
o
b
lem
s
is
ch
allen
g
in
g
[
3
]
.
T
h
e
s
tan
d
ar
d
PS
O
alg
o
r
ith
m
o
p
er
ates
with
f
ix
e
d
p
a
r
am
e
ter
s
.
T
h
ese
p
a
r
am
eter
s
in
clu
d
e
in
er
tia
weig
h
t
(
ω
)
an
d
ac
ce
ler
atio
n
co
ef
f
icien
ts
(
₁
an
d
₂
)
.
T
h
ey
a
r
e
s
ig
n
if
ican
tly
ca
p
ab
le
o
f
in
f
lu
en
cin
g
PS
O
co
n
v
er
g
en
ce
b
eh
av
i
o
r
a
n
d
s
o
lu
tio
n
q
u
ality
.
H
o
wev
er
,
to
co
n
s
is
ten
tly
h
av
e
q
u
ality
s
o
lu
tio
n
s
,
an
o
p
tim
al
p
ar
am
eter
s
ettin
g
is
r
eq
u
ir
e
d
.
T
h
is
ch
allen
g
e
cr
ea
tes
a
g
ap
f
o
r
p
ar
am
ete
r
tu
n
in
g
in
th
e
s
ta
n
d
ar
d
PS
O
[
4
]
.
T
o
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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I
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N:
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A
co
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tive
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P
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d
a
d
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p
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fu
z
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P
S
O
fo
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p
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(
P
a
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l O
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A
d
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a
y
o
)
2721
en
h
an
ce
PS
O,
ad
ap
tiv
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v
ar
i
an
ts
h
av
e
b
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n
d
ev
el
o
p
ed
,
with
f
u
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lo
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s
h
o
win
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p
r
o
m
is
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f
o
r
d
y
n
am
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p
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eter
a
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s
tm
en
t.
Sin
ce
PS
O
p
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f
o
r
m
an
ce
is
s
en
s
itiv
e
to
p
a
r
am
eter
s
ettin
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s
,
f
u
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ca
n
b
e
u
s
ed
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o
tu
n
e
th
ese
p
ar
am
eter
s
ad
ap
tiv
ely
d
u
r
in
g
o
p
tim
izatio
n
[
5
]
.
Oth
er
ad
ap
tiv
e
PS
O
ap
p
r
o
ac
h
es,
s
u
ch
as
l
in
ea
r
ly
d
ec
r
ea
s
in
g
in
er
tia
weig
h
t,
r
a
n
d
o
m
i
n
er
tia
weig
h
t,
c
h
ao
tic
PS
O,
an
d
s
elf
-
ad
ap
tiv
e
PS
O,
h
av
e
also
b
ee
n
p
r
o
p
o
s
ed
;
h
o
wev
er
,
f
u
zz
y
lo
g
ic
o
f
f
er
s
a
f
lex
ib
le
r
u
le
-
b
as
ed
m
ec
h
an
is
m
f
o
r
d
y
n
a
m
ic
p
ar
am
eter
co
n
tr
o
l
in
co
m
p
lex
o
p
tim
izatio
n
en
v
ir
o
n
m
en
ts
[
6
]
.
T
h
e
co
n
ce
p
ts
p
r
o
v
id
e
a
r
u
le
-
b
ased
f
r
a
m
ewo
r
k
f
o
r
h
an
d
lin
g
u
n
ce
r
tain
ty
a
n
d
im
p
r
ec
is
io
n
i
n
a
co
m
p
lex
p
r
o
b
lem
.
Mo
r
es
o
,
its
r
u
le
-
b
ased
ap
p
r
o
ac
h
h
a
s
en
ab
led
f
lex
ib
le
lear
n
in
g
,
an
d
m
ak
es
f
u
zz
y
lo
g
ic
well
-
s
u
ited
to
m
an
ag
i
n
g
co
m
p
le
x
,
n
o
n
-
lin
ea
r
r
ela
tio
n
s
h
ip
s
in
PS
O
p
ar
am
eter
s
.
Sev
er
al
s
t
u
d
i
es h
a
v
e
e
x
p
l
o
r
e
d
ad
ap
ti
v
e
f
u
zz
y
p
a
r
t
icl
e
s
wa
r
m
o
p
ti
m
iz
ati
o
n
(
AF
-
PS
O)
im
p
l
e
m
e
n
ta
ti
o
n
s
an
d
d
e
m
o
n
s
tr
ate
d
th
e
p
e
r
f
o
r
m
an
ce
o
f
a
n
im
p
r
o
v
ed
v
ar
ia
n
t
o
n
s
p
e
ci
f
ic
p
r
o
b
le
m
s
.
Fo
r
in
s
ta
n
ce
,
s
tu
d
ies
[
7
]
–
[
9
]
in
tr
o
d
u
ce
a
s
tab
ilit
y
-
b
ased
ad
ap
tiv
e
in
er
tia
weig
h
t
f
o
r
PS
O.
T
h
e
m
eth
o
d
u
p
d
ates
th
e
in
er
tia
weig
h
t
f
o
r
ea
ch
p
ar
ticle
b
ased
o
n
its
p
er
f
o
r
m
a
n
ce
an
d
p
r
o
x
im
ity
to
th
e
o
p
ti
m
al
p
o
s
itio
n
.
E
x
p
er
im
e
n
tal
r
e
s
u
lts
s
h
o
w
im
p
r
o
v
ed
PS
O’
s
s
o
lu
tio
n
q
u
ality
a
n
d
c
o
n
v
er
g
en
ce
s
p
ee
d
in
b
o
th
s
ta
tic
an
d
d
y
n
a
m
ic
en
v
ir
o
n
m
en
t
s
.
Alth
o
u
g
h
s
ev
er
al
s
tu
d
ies
h
av
e
p
r
o
p
o
s
ed
a
d
ap
tiv
e
PS
O
v
ar
ian
ts
an
d
d
em
o
n
s
tr
ated
im
p
r
o
v
em
e
n
ts
in
s
p
ec
if
ic
o
p
tim
izatio
n
task
s
,
m
an
y
o
f
th
ese
wo
r
k
s
f
o
c
u
s
o
n
lim
ited
b
en
ch
m
ar
k
f
u
n
ctio
n
s
o
r
to
p
r
o
b
lem
d
o
m
ain
.
I
n
ad
d
itio
n
,
th
e
s
co
p
e
o
f
ev
alu
atio
n
in
th
is
s
tu
d
y
is
lim
ited
.
Fix
ed
ex
p
er
im
en
tal
c
o
n
d
itio
n
s
,
s
u
ch
as
lim
ited
p
r
o
b
lem
d
im
en
s
io
n
s
o
r
iter
atio
n
b
u
d
g
ets,
m
ay
n
o
t
f
u
lly
r
ev
ea
l
th
e
r
o
b
u
s
tn
ess
an
d
s
ca
lab
ilit
y
o
f
alg
o
r
ith
m
s
.
T
h
er
ef
o
r
e,
a
th
o
r
o
u
g
h
co
m
p
ar
ativ
e
ass
ess
m
en
t
o
f
ad
ap
tiv
e
f
u
zz
y
PS
O
an
d
s
tan
d
ar
d
PS
O
ac
r
o
s
s
v
ar
io
u
s
b
en
c
h
m
ar
k
f
u
n
ctio
n
s
a
n
d
a
wid
er
r
an
g
e
o
f
e
x
p
er
im
e
n
tal
s
ettin
g
s
is
ess
en
tial.
T
h
er
ef
o
r
e,
th
is
s
tu
d
y
ad
d
r
es
s
es
th
ese
r
e
s
ea
r
ch
g
ap
s
b
y
o
f
f
er
in
g
a
co
m
p
r
e
h
en
s
iv
e
co
m
p
ar
ativ
e
an
aly
s
is
b
etwe
en
PS
O
an
d
AF
-
PSO
.
Ou
r
in
v
esti
g
atio
n
in
clu
d
es
s
ix
well
-
k
n
o
wn
s
in
g
le
-
o
b
jectiv
e
b
en
ch
m
a
r
k
f
u
n
ctio
n
s
(
Sp
h
er
e,
R
o
s
en
b
r
o
ck
,
R
astrig
in
,
Ack
le
y
,
Gr
iew
an
k
,
a
n
d
Sch
wef
el)
.
Als
o
,
t
wo
m
u
lti
-
o
b
jectiv
e
b
en
ch
m
ar
k
p
r
o
b
lem
s
(
Z
DT
1
-
3
an
d
DT
L
Z
1
-
2
)
an
d
th
e
Par
eto
f
r
o
n
t
f
ea
tu
r
e.
T
h
e
AF
-
PSO
d
y
n
am
ically
ev
alu
ates
s
war
m
d
iv
e
r
s
ity
an
d
co
n
v
er
g
en
ce
s
p
ee
d
.
I
t
also
all
o
ws
an
in
tellig
en
t
tr
ad
e
-
o
f
f
b
e
twee
n
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
d
u
r
in
g
t
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
T
h
e
ex
p
er
im
en
tal
m
et
h
o
d
u
s
es
s
tatis
t
ical
p
r
o
to
co
ls
with
m
u
ltip
le
in
d
e
p
en
d
e
n
t
d
im
en
s
io
n
s
an
d
alg
o
r
ith
m
ic
iter
atio
n
s
.
B
esid
es,
th
e
alg
o
r
ith
m
'
s
p
er
f
o
r
m
an
ce
is
co
m
p
ar
ed
ac
r
o
s
s
b
en
ch
m
ar
k
m
etr
ics,
in
clu
d
in
g
s
o
lu
tio
n
q
u
ality
,
co
n
v
er
g
e
n
ce
s
p
ee
d
,
s
u
cc
ess
r
at
e,
an
d
th
e
Par
eto
f
r
o
n
t.
I
m
p
o
r
tan
tly
,
d
im
en
s
io
n
s
(
2
to
5
0
)
a
n
d
iter
atio
n
b
u
d
g
et
s
(
5
0
to
5
0
0
)
p
r
o
v
id
e
i
n
s
ig
h
ts
in
to
th
e
s
ca
lab
ilit
y
an
d
co
m
p
u
tatio
n
al
ef
f
icien
cy
o
f
b
o
t
h
s
tan
d
ar
d
PS
O
an
d
its
v
ar
ian
t,
AF
-
PSO
.
Ho
wev
er
,
th
e
p
r
im
a
r
y
co
n
tr
i
b
u
tio
n
s
o
f
th
is
s
tu
d
y
ar
e
in
f
o
l
d
s
:
B
ey
o
n
d
p
r
o
v
i
d
in
g
a
co
m
p
r
eh
en
s
iv
e
em
p
ir
ical
v
alid
atio
n
o
f
AF
-
P
SO
ag
ain
s
t
s
tan
d
ar
d
PS
O
ac
r
o
s
s
a
wid
e
r
an
g
e
o
f
s
in
g
le
o
b
jectiv
e
b
en
ch
m
a
r
k
o
p
tim
izatio
n
f
u
n
ctio
n
s
,
it
also
r
ev
ea
ls
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
alg
o
r
ith
m
s
o
n
m
u
lti
-
o
b
jectiv
e.
T
h
is
ex
p
er
im
en
t
p
r
o
v
id
es
g
u
i
d
an
ce
o
n
alg
o
r
ith
m
s
elec
tio
n
ac
r
o
s
s
d
if
f
er
en
t
ap
p
licatio
n
co
n
tex
ts
.
T
h
e
r
em
a
in
d
er
o
f
th
is
ar
ticle
d
is
cu
s
s
es
r
elev
an
t
liter
atu
r
e
o
n
PS
O
an
d
its
v
ar
ian
ts
,
with
an
em
p
h
asis
o
n
f
u
zz
y
a
d
ap
t
atio
n
m
ec
h
an
is
m
s
.
Als
o
,
s
ec
tio
n
th
r
ee
d
etails
th
e
m
eth
o
d
o
lo
g
y
,
wh
ich
r
ev
ea
ls
s
tan
d
ar
d
PS
O,
AF
-
PS
O,
Fu
zz
y
lo
g
ic
co
n
tr
o
ller
,
b
en
ch
m
ar
k
f
u
n
ctio
n
s
,
an
d
ex
p
er
im
en
tal
p
r
o
to
c
o
ls
.
Sectio
n
f
o
u
r
p
r
esen
ts
co
m
p
r
e
h
en
s
iv
e
r
e
s
u
lts
an
d
s
tati
s
tical
an
aly
s
is
.
L
astl
y
,
s
ec
tio
n
f
iv
e
d
is
cu
s
s
es
th
e
im
p
licatio
n
s
an
d
r
esu
lts
,
with
r
ec
o
m
m
en
d
atio
n
s
f
o
r
t
h
e
alg
o
r
ith
m
ac
r
o
s
s
d
if
f
er
en
t f
ield
s
o
f
s
tu
d
y
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
P
SO
is
am
o
n
g
th
e
m
o
s
t
wid
ely
u
s
ed
p
o
p
u
latio
n
-
b
ased
m
eta
h
eu
r
is
tics
b
ec
au
s
e
o
f
its
s
im
p
le
v
elo
city
-
p
o
s
itio
n
u
p
d
ate
r
u
le
an
d
s
tr
o
n
g
p
e
r
f
o
r
m
an
ce
in
co
n
tin
u
o
u
s
o
p
tim
izatio
n
.
R
ec
en
t
r
e
v
iews
in
d
icate
th
at
ca
n
o
n
ical
PS
O
is
s
en
s
itiv
e
to
co
n
tr
o
l
p
ar
am
ete
r
s
,
lead
in
g
to
p
r
em
atu
r
e
co
n
v
e
r
g
en
c
e
an
d
s
tag
n
atio
n
,
p
ar
ticu
lar
ly
in
m
u
ltimo
d
al
a
n
d
h
ig
h
-
d
im
en
s
io
n
al
s
ea
r
ch
s
p
ac
es
[
1
0
]
–
[
1
3
]
.
A
m
ajo
r
lim
itatio
n
o
f
s
tan
d
a
r
d
PS
O
is
th
e
u
s
e
o
f
f
ix
ed
p
ar
am
eter
s
[
1
4
]
,
[
1
5
]
,
wh
ich
ca
n
d
r
iv
e
p
ar
ticles
to
o
q
u
ick
ly
to
war
d
p
r
o
m
is
in
g
r
eg
io
n
s
o
f
th
e
s
ea
r
ch
s
p
ac
e,
af
ter
wh
ich
d
iv
er
s
ity
d
ec
r
ea
s
es,
an
d
th
e
s
war
m
m
ay
b
ec
o
m
e
tr
ap
p
ed
i
n
lo
ca
l
o
p
tim
a.
Fo
r
th
is
r
ea
s
o
n
,
p
ar
am
eter
co
n
tr
o
l
h
as
b
ec
o
m
e
a
ce
n
tr
al
r
esear
ch
d
ir
ec
tio
n
.
Dete
r
m
in
is
tic
s
tr
ateg
ies,
s
u
ch
as
lin
ea
r
ly
o
r
n
o
n
lin
ea
r
ly
v
a
r
y
in
g
in
e
r
tia
weig
h
t,
ar
e
ea
s
y
to
im
p
lem
en
t
b
u
t
d
o
n
o
t
r
esp
o
n
d
to
r
ea
l
-
tim
e
s
ea
r
ch
co
n
d
itio
n
s
[
1
6
]
,
[
1
7
]
.
Ho
wev
er
,
th
e
s
elf
-
ad
ap
tiv
e
s
tr
ateg
ies
ad
ju
s
t
th
eir
p
ar
am
eter
s
b
ased
o
n
th
e
s
tate
o
f
th
e
s
war
m
,
s
u
c
h
as
d
iv
er
s
ity
an
d
c
o
n
v
er
g
en
c
e
r
ate.
T
h
is
m
ak
es
s
elf
-
ad
ap
tiv
e
s
tr
ateg
ies in
PS
O
m
o
r
e
ef
f
ec
tiv
e
f
o
r
o
p
tim
izin
g
a
r
an
g
e
o
f
d
if
f
er
e
n
t p
r
o
b
lem
s
[
1
0
]
,
[
1
8
]
.
Am
o
n
g
ad
a
p
tiv
e
ap
p
r
o
ac
h
es
s
t,
f
u
zz
y
lo
g
ic
is
p
ar
ticu
lar
ly
attr
ac
tiv
e
b
ec
au
s
e
it
p
r
o
v
id
es
an
in
ter
p
r
etab
le
r
u
le
-
b
ased
m
ec
h
an
is
m
f
o
r
m
ap
p
in
g
m
ea
s
u
r
ab
l
e
s
ea
r
ch
s
ig
n
als
to
p
ar
am
eter
u
p
d
ates.
Fu
zz
y
PS
O
v
ar
ian
ts
ad
ju
s
t
in
er
tia
weig
h
t
an
d
lear
n
i
n
g
co
e
f
f
icien
ts
b
ased
o
n
iter
atio
n
p
r
o
g
r
ess
an
d
d
iv
er
s
ity
m
ea
s
u
r
es,
en
h
an
cin
g
th
e
s
h
if
t
f
r
o
m
ex
p
lo
r
atio
n
to
e
x
p
lo
itatio
n
[
1
9
]
,
[
2
0
]
.
T
h
is
m
ak
es
f
u
zz
y
ad
ap
tatio
n
esp
ec
ially
s
u
itab
le
wh
er
e
th
e
r
elatio
n
s
h
ip
b
etwe
en
s
war
m
b
eh
a
v
io
r
a
n
d
p
ar
am
eter
co
n
tr
o
l is n
o
n
lin
ea
r
o
r
u
n
ce
r
tain
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
16
,
No
.
5
,
Octo
b
e
r
20
26
:
2
7
2
0
-
2
7
3
0
2722
At
th
e
s
am
e
tim
e,
m
an
y
s
tr
o
n
g
PS
O
v
ar
ian
ts
ar
e
h
y
b
r
id
r
ath
er
th
an
p
u
r
ely
a
d
ap
tiv
e.
H
y
b
r
id
PS
O
m
eth
o
d
s
in
co
r
p
o
r
ate
o
p
er
ato
r
s
f
r
o
m
o
th
er
p
ar
ad
ig
m
s
,
s
u
ch
as
d
if
f
er
en
tial
ev
o
l
u
tio
n
,
o
p
p
o
s
itio
n
-
b
ased
lear
n
in
g
,
o
r
lo
ca
l
s
ea
r
ch
,
to
r
esto
r
e
d
iv
er
s
ity
an
d
im
p
r
o
v
e
r
o
b
u
s
tn
ess
[
2
1
]
,
[
2
2
]
.
R
ec
en
t
s
tu
d
ies
co
n
f
ir
m
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
h
y
b
r
i
d
PS
O
ap
p
r
o
ac
h
[
2
3
]
,
[
2
4
]
.
An
o
th
er
em
er
g
in
g
d
ir
ec
tio
n
is
lear
n
in
g
-
b
ased
ad
ap
tatio
n
,
p
a
r
ticu
lar
ly
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
,
in
wh
ich
PS
O
p
ar
am
eter
s
ar
e
ad
ju
s
ted
d
y
n
am
ically
ac
co
r
d
in
g
to
th
e
s
ea
r
ch
s
tate.
T
h
is
d
ir
ec
tl
y
ad
d
r
ess
es
th
e
wea
k
n
ess
o
f
s
t
atic
p
ar
am
eter
s
ettin
g
s
an
d
h
a
s
s
h
o
wn
p
r
o
m
is
in
g
r
esu
lts
o
n
s
tan
d
ar
d
b
en
ch
m
a
r
k
f
u
n
ctio
n
s
[
2
5
]
.
Desp
ite
th
ese
ad
v
an
ce
s
,
two
is
s
u
es
r
em
ain
co
m
m
o
n
i
n
th
e
liter
atu
r
e:
m
an
y
s
tu
d
ies
ev
alu
ate
eith
er
s
in
g
le
-
o
b
jectiv
e
o
r
m
u
lti
-
o
b
jectiv
e
p
er
f
o
r
m
an
ce
,
b
u
t
n
o
t
b
o
t
h
u
n
d
e
r
a
u
n
if
ied
p
r
o
to
c
o
l,
an
d
r
ep
o
r
tin
g
is
o
f
ten
in
co
n
s
is
ten
t
ac
r
o
s
s
s
o
lu
tio
n
q
u
ality
,
r
o
b
u
s
tn
ess
,
an
d
c
o
n
v
e
r
g
en
ce
.
T
h
is
m
o
tiv
ates
th
e
p
r
esen
t
s
tu
d
y
,
wh
ich
co
m
p
ar
es
AF
-
PSO
with
s
tan
d
ar
d
f
ix
e
d
-
p
ar
a
m
eter
PS
O
u
n
d
e
r
co
n
tr
o
lled
ex
p
e
r
im
en
tal
s
ettin
g
s
.
3.
M
E
T
H
O
D
T
h
e
p
ar
ticle
s
war
m
o
p
tim
izatio
n
f
r
am
ewo
r
k
is
a
wid
ely
u
s
e
d
o
p
tim
izatio
n
alg
o
r
ith
m
in
s
p
ir
ed
b
y
th
e
s
o
cial
b
eh
av
io
r
o
f
b
ir
d
s
,
f
is
h
,
o
r
o
th
e
r
an
im
als.
T
h
e
PS
O
ca
n
b
e
u
s
ed
to
s
o
lv
e
co
m
p
lex
o
p
tim
izatio
n
p
r
o
b
lem
s
.
I
t
h
as
s
in
ce
b
ee
n
ap
p
lied
to
co
m
p
lex
p
r
o
b
lem
s
in
n
-
d
im
en
s
io
n
al
s
p
ac
es
an
d
n
o
n
lin
ea
r
s
ea
r
c
h
s
p
ac
es.
T
h
e
k
ey
c
o
m
p
o
n
en
ts
o
f
th
e
s
tan
d
ar
d
PS
O
ar
e
as f
o
llo
ws:
a.
I
n
itializatio
n
:
T
h
e
p
r
o
ce
s
s
in
v
o
lv
es
ass
ig
n
in
g
a
p
o
p
u
latio
n
o
f
p
a
r
ticles
(
s
o
lu
tio
n
s
)
r
an
d
o
m
ly
.
I
n
th
e
PS
O,
ea
ch
p
ar
ticle
in
th
e
s
ea
r
ch
s
p
a
ce
is
a
p
o
ten
tial
s
o
lu
tio
n
to
th
e
o
p
tim
izatio
n
p
r
o
b
lem
.
Po
s
itio
n
an
d
v
elo
city
:
in
an
n
-
d
im
en
s
io
n
al
s
ea
r
ch
s
p
ac
e,
ea
ch
p
ar
ticle
.
T
h
e
n
o
tatio
n
f
o
r
is
g
iv
en
:
p
o
s
itio
n
v
ec
to
r
:
r
ep
r
esen
tin
g
a
p
o
te
n
tial
s
o
lu
tio
n
,
an
d
v
elo
city
v
ec
to
r
:
estab
lis
h
es
th
e
d
ir
ec
tio
n
an
d
s
tr
en
g
th
o
f
m
o
v
em
en
t
o
f
th
e
p
ar
ticle
in
th
e
s
ea
r
ch
s
p
ac
e.
b.
Ob
jectiv
e
f
u
n
ctio
n
:
T
h
e
PS
O
alg
o
r
ith
m
r
eq
u
ir
es
an
o
b
jecti
v
e
f
u
n
ctio
n
t
h
at
ev
alu
ates
h
o
w
g
o
o
d
a
g
iv
en
p
ar
ticle’
s
p
o
s
itio
n
is
r
elativ
e
t
o
th
e
p
r
o
b
lem
b
ei
n
g
s
o
lv
ed
.
A
ls
o
k
n
o
wn
as
th
e
f
itn
ess
f
u
n
cti
o
n
,
it
is
u
s
ed
to
f
in
d
th
e
b
est p
o
s
s
ib
le
s
o
lu
tio
n
s
(
m
in
im
u
m
o
r
m
ax
im
u
m
)
o
f
t
h
e
o
b
jectiv
e
f
u
n
ctio
n
.
c.
Par
ticle
u
p
d
ate:
T
h
ese
two
k
e
y
r
ef
er
e
n
ce
p
o
i
n
ts
g
u
id
e
th
e
s
ea
r
ch
p
r
o
ce
s
s
.
T
h
ey
ar
e:
˗
T
h
e
p
ar
ticle’
s
p
er
s
o
n
al
b
est p
o
s
itio
n
,
d
en
o
ted
b
y
,
an
d
˗
T
h
e
g
lo
b
al
b
est p
o
s
itio
n
f
o
u
n
d
b
y
th
e
e
n
tire
s
war
m
,
d
e
n
o
ted
b
y
.
T
h
e
v
el
o
city
a
n
d
p
o
s
itio
n
o
f
ea
ch
p
a
r
ticle
ar
e
m
eth
o
d
ically
u
p
d
ated
th
r
o
u
g
h
a
n
iter
ativ
e
p
r
o
ce
s
s
,
ac
co
r
d
in
g
to
(
1
)
an
d
(
2
)
,
r
esp
e
ctiv
ely
.
+
1
=
⋅
+
1
1
(
−
)
+
2
2
(
−
)
(
1
)
+
1
=
+
+
1
(
2
)
W
h
er
e:
r
ep
r
esen
ts
th
e
in
er
tia
weig
h
t t
h
at
co
n
tr
o
ls
th
e
i
n
f
lu
en
ce
o
f
th
e
p
r
ev
io
u
s
v
elo
city
,
1
an
d
2
s
tan
d
f
o
r
t
h
e
co
g
n
itiv
e
a
n
d
s
o
cial
ac
ce
ler
atio
n
c
o
ef
f
ici
en
ts
,
r
esp
ec
tiv
ely
,
1
an
d
2
r
ep
r
esen
ts
th
e
r
an
d
o
m
n
u
m
b
er
s
u
n
if
o
r
m
ly
d
is
tr
ib
u
ted
o
n
th
e
in
ter
v
al
[
0
,
1
]
,
d
en
o
tes th
e
b
est p
o
s
itio
n
p
r
e
v
io
u
s
ly
attain
ed
b
y
th
e
p
a
r
ticle
,
r
ep
r
esen
ts
th
e
b
est p
o
s
itio
n
f
o
u
n
d
b
y
th
e
e
n
tire
s
war
m
,
(
)
r
ef
er
s
to
th
e
c
u
r
r
e
n
t p
o
s
itio
n
o
f
th
e
p
a
r
ticle
at
iter
atio
n
.
3.
1
.
F
uzzy
inp
uts
T
wo
in
p
u
ts
ar
e
p
r
o
v
i
d
ed
to
th
e
f
u
zz
y
in
f
er
en
ce
s
y
s
tem
.
a.
No
r
m
alize
d
iter
atio
n
c
o
u
n
t
I
ter
nor
m
(
)
=
,
I
ter
nor
m
∈
[
0
,
1
]
,
(
3
)
wh
er
e
d
en
o
tes
th
e
m
ax
im
u
m
n
u
m
b
e
r
o
f
iter
atio
n
s
.
Valu
es
clo
s
e
to
0
co
r
r
esp
o
n
d
t
o
th
e
ea
r
ly
s
tag
e
o
f
th
e
s
ea
r
ch
,
wh
ile
v
alu
es n
ea
r
1
in
d
icate
th
e
late
s
tag
e.
b.
Ma
x
im
u
m
d
im
e
n
s
io
n
d
if
f
er
en
ce
weig
h
t
(
MD
DW
)
T
h
e
p
ar
ticle
-
s
tate
m
etr
ic
is
d
e
f
in
ed
as
th
e
n
o
r
m
alize
d
E
u
cli
d
ea
n
d
is
tan
ce
b
etwe
en
th
e
p
ar
ticle
an
d
th
e
g
lo
b
al
b
est p
o
s
itio
n
:
d
is
t
=
∥
−
∥
2
=
∑
(
=
1
,
−
)
2
,
(
4
)
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
-
8
7
0
8
A
co
mp
a
r
a
tive
s
tu
d
y
o
f sta
n
d
a
r
d
P
S
O
a
n
d
a
d
a
p
tive
fu
z
z
y
P
S
O
fo
r
o
p
timiz
a
tio
n
(
P
a
u
l O
lu
jid
e
A
d
eb
a
y
o
)
2723
d
is
t
=
∑
(
=
1
−
)
2
,
(
5
)
MDDW
=
dis
t
dis
t
+
,
MDDW
∈
[
0
,
1
]
,
(
6
)
wh
er
e
an
d
p
ar
am
eter
s
r
ep
r
esen
t
lo
wer
an
d
u
p
p
er
b
o
u
n
d
s
o
f
th
e
d
im
en
s
io
n
.
is
a
s
m
all
in
f
in
ites
im
al
co
n
s
tan
t
in
tr
o
d
u
ce
d
to
a
v
o
id
d
iv
is
io
n
b
y
ze
r
o
d
u
r
in
g
co
m
p
u
tatio
n
.
Ho
wev
e
r
,
Hig
h
MD
DW
v
alu
es
in
d
icate
th
at
a
p
ar
ticle
is
f
ar
f
r
o
m
th
e
b
est
-
k
n
o
wn
r
eg
io
n
an
d
s
h
o
u
ld
b
e
ex
p
l
o
r
ed
,
wh
er
ea
s
l
o
w
v
alu
es
in
d
icate
p
r
o
x
im
ity
t
o
th
e
g
l
o
b
al
b
est an
d
f
av
o
u
r
ex
p
lo
itatio
n
.
3
.
2
.
F
uzzy
o
utput
s
a
nd
pa
ra
m
et
er
bo
un
din
g
T
h
e
f
u
zz
y
co
n
tr
o
ller
g
en
er
ate
s
in
cr
em
en
tal
ad
ju
s
tm
en
ts
Δ
,
Δ
1
,
,
an
d
Δ
2
,
.
T
h
ese
in
cr
e
m
en
ts
ar
e
ap
p
lied
to
u
p
d
ate
t
h
e
PS
O
p
ar
am
eter
s
,
wh
ich
ar
e
t
h
en
clip
p
ed
to
p
r
ed
ef
i
n
ed
b
o
u
n
d
s
t
o
en
s
u
r
e
n
u
m
er
ical
s
tab
ilit
y
:
=
clip
(
−
1
+
Δ
,
m
i
n
,
m
ax
)
,
1
,
=
clip
(
1
,
−
1
+
Δ
1
,
,
1
,
m
i
n
,
1
,
m
ax
)
,
2
,
=
clip
(
2
,
−
1
+
Δ
2
,
,
2
,
m
i
n
,
2
,
m
ax
)
.
(
7
)
3
.
3
.
M
em
bersh
ip
f
un
ct
io
ns
B
o
th
f
u
zz
y
in
p
u
ts
ar
e
m
a
p
p
ed
o
n
to
th
r
ee
lin
g
u
is
tic
ter
m
s
o
v
e
r
th
e
in
ter
v
al
[
0
,
1
]
:
a.
I
ter
nor
m
:
E
a
r
ly
,
Mid
,
La
te
b.
MDDW
:
N
ea
r
,
Med
iu
m
,
F
a
r
.
T
h
e
o
u
tp
u
t
v
a
r
iab
les
Δ
,
Δ
1
,
an
d
Δ
2
ar
e
d
ef
in
ed
o
v
er
th
e
n
o
r
m
aliz
ed
d
o
m
ain
[
−
1
,
1
]
.
Fo
r
ea
ch
o
u
tp
u
t,
t
h
r
ee
lin
g
u
is
tic
ter
m
s
ar
e
u
s
ed
:
Dec
r
ea
s
e
(
)
,
Z
er
o
(
)
,
an
d
I
n
cr
ea
s
e
(
)
.
T
h
e
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
s
ar
e
s
p
ec
if
ied
:
(
)
=
(
−
1
,
−
1
,
−
0
.
5
,
0
)
,
(
)
=
(
−
0
.
5
,
0
,
0
.
5
)
,
an
d
(
)
=
(
0
,
0
.
5
,
1
,
1
)
.
3
.
4
.
Rule
ba
s
e
T
h
e
f
u
zz
y
r
u
le
b
ase
,
as
s
h
o
wn
in
T
ab
le
1
,
e
n
co
d
es
t
h
e
d
esire
d
b
alan
ce
b
etwe
en
ex
p
lo
r
atio
n
a
n
d
ex
p
lo
itatio
n
.
E
a
r
ly
iter
atio
n
s
p
r
o
m
o
te
ex
p
lo
r
atio
n
,
wh
er
ea
s
l
ater
iter
atio
n
s
en
co
u
r
a
g
e
co
n
v
er
g
en
ce
.
E
ac
h
r
u
le
s
p
ec
if
ies th
e
o
u
tp
u
t a
ctio
n
s
(
Δ
,
Δ
1
,
Δ
2
)
as f
o
llo
ws:
T
ab
le
1
.
Fu
zz
y
r
u
le
b
ase
en
co
d
in
g
I
t
e
r
norm
M
D
D
W
N
e
a
r
M
e
d
i
u
m
F
a
r
Ea
r
l
y
(
Z,
Z,
I
)
(
I
,
I
,
Z)
(
I
,
I
,
D
)
M
i
d
(
D
,
Z,
I
)
(
Z,
Z,
Z)
(
Z,
I
,
Z)
La
t
e
(
D
,
D
,
I
)
(
D
,
Z,
I
)
(
D
,
Z,
I
)
T
h
is
s
tr
u
ctu
r
e
in
cr
ea
s
es
in
er
t
ia
an
d
s
elf
-
g
u
id
e
d
lear
n
in
g
wh
en
ex
p
lo
r
atio
n
is
r
e
q
u
ir
ed
(
h
ig
h
er
an
d
1
)
.
Hen
ce
,
th
e
in
tu
itiv
e
p
r
o
ce
s
s
th
r
o
u
g
h
t
h
e
r
u
le
-
b
ased
p
r
o
g
r
ess
iv
ely
s
tr
en
g
t
h
en
s
co
n
v
er
g
en
ce
p
r
ess
u
r
e
in
th
e
later
s
tag
es b
y
r
e
d
u
cin
g
an
d
1
wh
ile
in
cr
ea
s
in
g
2
.
3
.
5
.
I
nfe
re
nce
a
nd
def
uzzif
ica
t
io
n
A
Ma
m
d
an
i
-
ty
p
e
f
u
zz
y
in
f
er
e
n
ce
s
y
s
tem
is
u
s
ed
in
th
is
s
tu
d
y
.
Als
o
,
th
e
r
u
le
f
ir
in
g
s
tr
en
g
th
is
u
s
ed
to
d
eter
m
i
n
e
th
e
s
elec
tio
n
u
s
in
g
th
e
s
tan
d
ar
d
AND
o
p
er
a
to
r
,
s
elec
tin
g
t
h
e
lo
west
v
al
u
e.
Ou
tp
u
t
s
ets
ar
e
co
m
b
in
ed
with
t
h
e
m
ax
im
u
m
o
p
er
ato
r
.
C
en
tr
o
id
d
e
f
u
zz
if
ic
atio
n
p
r
o
d
u
ce
s
p
r
ec
is
e
p
ar
a
m
eter
u
p
d
ates.
T
h
is
ap
p
r
o
ac
h
en
ab
les p
a
r
am
eter
s
to
ad
ap
t sm
o
o
th
ly
an
d
co
n
s
is
te
n
tly
,
m
in
im
izin
g
s
u
d
d
en
o
r
u
n
s
tab
le
ch
an
g
es.
3
.
6
.
Ada
ptiv
e
f
uzzy
pa
rt
icle
s
wa
rm
o
ptim
iza
t
io
n
(
AF
-
P
S
O
)
I
n
th
e
co
m
p
a
r
ativ
e
o
f
th
e
two
alg
o
r
ith
m
s
,
th
e
s
tu
d
y
u
s
es
a
n
AF
-
PS
O
f
r
am
ewo
r
k
in
w
h
ich
a
f
u
zz
y
in
f
er
en
ce
s
y
s
tem
(
FIS)
d
y
n
a
m
ically
ad
ju
s
ts
th
e
in
er
tia
weig
h
t
an
d
t
h
e
ac
ce
ler
atio
n
c
o
ef
f
icien
ts
d
u
r
in
g
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
Un
lik
e
t
r
ad
itio
n
al
PS
O,
in
wh
ich
p
a
r
a
m
eter
s
ar
e
f
ix
ed
o
r
f
o
llo
w
a
p
r
ed
ef
in
ed
s
ch
ed
u
le,
AF
-
PS
O
m
o
d
if
ies
th
em
in
r
e
al
tim
e
b
ased
o
n
th
e
cu
r
r
en
t
s
ea
r
ch
s
tate.
T
h
is
ad
ju
s
tm
en
t
o
f
th
e
p
ar
am
eter
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
16
,
No
.
5
,
Octo
b
e
r
20
26
:
2
7
2
0
-
2
7
3
0
2724
b
ec
o
m
es
n
ec
ess
ar
y
,
g
iv
en
th
a
t
f
ix
ed
p
ar
am
eter
s
ettin
g
s
ca
n
lead
to
p
r
em
atu
r
e
co
n
v
er
g
e
n
ce
[
9
]
,
[
2
6
]
.
Hen
ce
,
th
e
p
r
o
p
o
s
ed
AF
-
PS
O
ad
ju
s
ts
,
1
,
an
d
2
n
o
r
m
alize
d
iter
atio
n
p
r
o
g
r
ess
,
an
d
to
t
h
e
co
m
p
u
ter
p
ar
ticle
-
s
tate
m
etr
ic
th
at
q
u
a
n
tifie
s
th
e
d
is
ta
n
ce
b
etwe
en
ea
c
h
p
a
r
ticle
an
d
th
e
cu
r
r
en
t
b
est
s
o
lu
tio
n
g
l
o
b
ally
.
T
h
is
ad
ap
tiv
e
m
ec
h
an
is
m
p
r
o
m
o
tes
ex
ten
s
iv
e
ex
p
lo
r
atio
n
d
u
r
in
g
ea
r
ly
o
r
h
ig
h
ly
d
is
p
er
s
ed
s
ea
r
ch
s
tag
es
an
d
g
r
ad
u
ally
s
h
if
ts
to
war
d
s
in
ten
s
if
ied
ex
p
l
o
itatio
n
as th
e
s
war
m
co
n
v
er
g
es.
Alg
o
r
ith
m
1
.
Ad
ap
tiv
e
f
u
zz
y
p
ar
ticle
s
war
m
o
p
tim
izatio
n
Input:
Objective function
(
⋅
)
; swarm size
; dimension
; maximum iterations
; bounds
,
; FIS; parameter limits
,
,
1
,
,
1
,
,
2
,
,
2
,
Output:
Global best solution
:
0
, velocities
0
, and parameters
0
,
1
,
0
,
2
,
0
Evaluate fitness; set
0
=
0
; determine
0
for
=
0
to
−
1
do
Compute
=
/
(3)
for
each particle
=
1
,
…
,
do
Compute
=
∥
−
∥
2
2
/
(
∑
(
−
)
2
+
)
(6)
Infer
Δ
,
Δ
1
,
,
Δ
2
,
using FIS
Update parameters with bounds:
←
(
−
1
+
Δ
)
1
,
←
(
1
,
−
1
+
Δ
1
,
)
2
,
←
(
2
,
−
1
+
Δ
2
,
)
(7)
Update velocity and position:
,
+
1
=
,
+
1
,
1
(
,
−
,
)
+
2
,
2
(
−
,
)
(1)
,
+
1
=
,
+
,
+
1
(2)
Apply boundary handling and evaluate fitness
Update personal best
+
1
end for
Update global best
+
1
end for
return
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
ex
p
er
im
e
n
t
s
h
o
ws
a
co
m
p
ar
ativ
e
an
aly
s
is
o
f
s
tan
d
ar
d
PS
O
an
d
AF
-
P
SO
u
n
d
er
two
d
if
f
er
en
t
co
n
d
itio
n
s
.
T
h
e
f
ir
s
t
ex
p
e
r
im
en
t
ev
alu
ates
th
e
two
al
g
o
r
it
h
m
s
b
ased
o
n
t
h
eir
p
er
f
o
r
m
a
n
ce
o
n
b
e
n
ch
m
ar
k
o
p
tim
izatio
n
f
u
n
ctio
n
s
,
wh
ile
th
e
s
ec
o
n
d
e
x
p
er
im
e
n
t
ev
alu
a
tes
th
e
s
o
lu
tio
n
q
u
ality
,
co
n
v
e
r
g
en
ce
,
a
n
d
Par
eto
f
r
o
n
t.
T
a
b
le
2
p
r
esen
ts
th
e
in
p
u
t
v
alu
e
u
s
ed
i
n
th
e
ex
p
er
im
e
n
t.
T
h
ese
in
clu
d
e
d
im
en
s
io
n
,
iter
atio
n
/g
en
er
atio
n
,
an
d
th
e
n
u
m
b
er
o
f
in
d
ep
en
d
e
n
t
r
u
n
s
/tria
ls
o
f
PS
O.
Ho
wev
er
,
in
T
ab
les
3
an
d
4
,
c
o
m
p
u
tatio
n
al
r
esu
lts
f
o
r
ea
ch
ex
p
er
im
e
n
t
ar
e
p
r
esen
ted
.
AF
-
PS
O
g
en
er
ally
o
u
tp
er
f
o
r
m
s
s
tan
d
ar
d
PS
O
o
n
m
o
s
t
b
en
ch
m
ar
k
s
.
T
h
e
im
p
r
o
v
em
e
n
t
is
m
o
r
e
s
ig
n
if
ican
t
f
o
r
co
m
p
le
x
m
u
lt
im
o
d
al
f
u
n
ctio
n
s
,
with
b
etter
co
n
v
er
g
en
ce
s
p
ee
d
a
n
d
h
ig
h
er
s
u
cc
ess
r
ates.
Ho
wev
er
,
b
o
th
alg
o
r
ith
m
s
p
er
f
o
r
m
s
im
ilar
ly
o
n
s
im
p
le
co
n
v
ex
f
u
n
ctio
n
s
.
As
ev
alu
ated
ac
r
o
s
s
m
u
ltip
le
m
etr
ics,
Fig
u
r
e
1
d
e
m
o
n
s
tr
ate
s
th
e
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
o
f
AF
-
PS
O
co
m
p
ar
ed
to
Stan
d
a
r
d
PS
O,
s
h
o
win
g
b
etter
f
in
al
s
o
lu
tio
n
q
u
ality
ac
r
o
s
s
b
en
ch
m
a
r
k
f
u
n
ctio
n
s
,
f
aster
co
n
v
er
g
en
ce
s
p
ee
d
,
an
d
h
ig
h
e
r
s
u
cc
ess
r
ates a
s
d
ep
icted
in
Fig
u
r
e
s
1
(
a)
t
o
(
c)
,
r
esp
ec
tiv
ely
.
On
s
im
p
le
co
n
v
e
x
f
u
n
ctio
n
s
,
Fig
u
r
e
2
illu
s
tr
ates
th
e
d
etailed
p
er
f
o
r
m
an
ce
o
n
th
e
s
p
h
er
e
b
en
ch
m
ar
k
f
u
n
ctio
n
.
T
h
e
co
n
v
er
g
e
n
ce
h
i
s
to
r
y
o
b
s
er
v
ed
in
Fig
u
r
e
2
(
a)
in
d
icate
s
r
ap
id
o
p
tim
izatio
n
f
o
r
b
o
th
alg
o
r
ith
m
s
,
d
r
iv
en
b
y
d
y
n
am
ic
a
d
ap
tiv
e
p
ar
am
eter
a
d
ju
s
tm
en
t
illu
s
tr
ated
in
Fig
u
r
e
2
(
b
)
,
wh
ile
AF
-
PS
O
m
ain
tain
s
co
n
s
is
ten
t so
lu
tio
n
q
u
ality
as
d
im
en
s
io
n
ality
in
cr
ea
s
es a
s
ca
p
tu
r
ed
in
Fig
u
r
e
2
(
c)
.
Fu
r
th
er
m
o
r
e
,
Fig
u
r
e
3
h
ig
h
lig
h
ts
s
war
m
d
y
n
am
ics,
m
u
lti
-
o
b
jectiv
e
p
er
f
o
r
m
an
ce
,
a
n
d
o
v
er
all
g
ain
s
.
AF
-
PS
O
m
ain
tain
s
h
ig
h
er
s
wa
r
m
d
iv
er
s
ity
th
r
o
u
g
h
o
u
t
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
,
p
r
o
d
u
ce
s
a
b
etter
-
d
is
tr
ib
u
ted
Par
eto
f
r
o
n
t
f
o
r
m
u
lti
-
o
b
jectiv
e
p
r
o
b
lem
s
,
an
d
ac
h
ie
v
es
a
2
0
–
3
0
%
p
er
f
o
r
m
an
ce
im
p
r
o
v
em
en
t
o
v
e
r
s
tan
d
ar
d
PS
O
o
n
co
m
p
lex
m
u
ltimo
d
al
f
u
n
ctio
n
s
s
u
c
h
as
R
o
s
en
b
r
o
ck
an
d
R
astrig
in
as
d
etailed
in
Fig
u
r
e
s
3
(
a)
to
(
c)
,
r
esp
ec
tiv
ely
.
Scalab
ilit
y
an
aly
s
is
in
Fig
u
r
e
4
f
u
r
th
er
c
o
n
f
ir
m
s
th
at
AF
-
PS
O
m
ain
tain
s
h
ig
h
er
s
o
lu
tio
n
q
u
ality
a
s
p
r
esen
ted
in
Fig
u
r
e
4
(
a)
,
ac
h
iev
es
f
aster
co
n
v
er
g
en
ce
s
p
ee
d
ac
r
o
s
s
m
ax
im
u
m
iter
atio
n
s
ev
id
en
ce
d
i
n
Fig
u
r
e
4
(
b
)
,
an
d
p
r
eser
v
es g
r
e
ater
d
iv
er
s
ity
as d
im
en
s
io
n
s
in
cr
ea
s
e
as p
lo
tted
in
Fig
u
r
e
4
(
c
)
.
Fin
ally
,
Fig
u
r
e
5
d
etails
th
e
p
ar
am
eter
tu
n
in
g
m
ec
h
an
ic
s
an
d
h
ig
h
-
d
im
en
s
io
n
al
b
eh
av
io
r
.
T
h
e
ad
ap
tiv
e
p
a
r
am
eter
a
d
ju
s
tm
en
t
m
ain
tain
s
a
b
ala
n
ce
b
etw
ee
n
ex
p
l
o
r
atio
n
a
n
d
e
x
p
lo
ita
tio
n
illu
s
tr
ated
in
Fig
u
r
e
5
(
a)
,
r
esu
ltin
g
i
n
a
b
etter
Par
eto
f
r
o
n
t
co
m
p
ar
is
o
n
at
h
ig
h
er
d
im
e
n
s
io
n
s
(
=
50
)
a
s
s
h
o
wn
in
Fig
u
r
e
5
(
b
)
an
d
a
m
o
r
e
s
tab
le
co
n
v
er
g
en
ce
h
is
to
r
y
d
em
o
n
s
tr
ated
in
Fig
u
r
e
5
(
c)
.
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
-
8
7
0
8
A
co
mp
a
r
a
tive
s
tu
d
y
o
f sta
n
d
a
r
d
P
S
O
a
n
d
a
d
a
p
tive
fu
z
z
y
P
S
O
fo
r
o
p
timiz
a
tio
n
(
P
a
u
l O
lu
jid
e
A
d
eb
a
y
o
)
2725
T
ab
le
2
.
Dim
en
s
io
n
,
iter
atio
n
,
an
d
n
u
m
b
er
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I
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