I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
16
,
No
.
5
,
Octo
b
er
20
26
,
p
p
.
2
8
1
9
~
2
8
3
5
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
16
i
5
.
pp
2
8
1
9
-
2
8
3
5
2819
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
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co
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PMG
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pa
rticle
-
g
uid
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lf
optimizer
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m
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f
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se
s
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is
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y
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ro
p
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th
e
p
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g
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a
d
a
p
ti
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g
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e
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lf
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p
ti
m
ize
r
(P
M
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),
wh
ich
c
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m
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in
e
s
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y
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p
ti
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ize
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(G
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-
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d
e
rsh
ip
g
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i
d
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n
c
e
with
c
ro
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e
r,
p
a
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le
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g
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id
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e
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d
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d
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ti
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l
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e
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e
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t.
P
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wa
s
e
v
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l
u
a
t
e
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o
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e
n
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LIB
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n
s
ta
n
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e
s
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n
d
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p
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lf
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ize
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(P
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),
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h
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le o
p
ti
m
iza
ti
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l
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m
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ris
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iza
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ll
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n
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b
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twe
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n
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n
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th
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o
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p
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re
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m
e
th
o
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s
.
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h
e
a
b
latio
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stu
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sh
o
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d
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t
a
d
a
p
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iv
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re
fin
e
m
e
n
t
h
a
d
th
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stro
n
g
e
st
e
ffe
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t
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ti
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q
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a
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ro
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m
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rk
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n
sta
n
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e
s.
Alt
h
o
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g
h
P
M
G
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O
re
q
u
ired
m
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re
c
o
m
p
u
tatio
n
th
a
n
th
e
o
rig
in
a
l
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it
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ru
n
ti
m
e
re
m
a
in
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o
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ti
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e
with
se
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e
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l
o
f
t
h
e
o
t
h
e
r
m
e
th
o
d
s.
Ov
e
ra
ll
,
t
h
e
re
su
lt
s
sh
o
w
th
a
t
P
M
G
HWO
is
a
p
ro
m
isin
g
a
p
p
r
o
a
c
h
fo
r
T
S
P
o
p
t
imiz
a
ti
o
n
a
n
d
wa
rra
n
ts
fu
rth
e
r
e
v
a
l
u
a
ti
o
n
o
n
lar
g
e
r
a
n
d
m
o
re
c
o
m
p
lex
c
o
m
b
in
a
to
rial
p
r
o
b
lem
s.
K
ey
w
o
r
d
s
:
C
o
m
b
in
ato
r
ial
o
p
tim
izatio
n
Gr
ey
wo
lf
o
p
tim
izer
Me
tah
eu
r
is
tic
o
p
tim
izatio
n
Swar
m
in
tellig
en
ce
T
r
av
elin
g
s
alesm
an
p
r
o
b
lem
T
SP
L
I
B
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Han
ad
i A
.
Al
-
Sh
awa
b
k
ah
Dep
ar
tm
en
t o
f
Data
Scien
ce
s
an
d
Ar
tific
ial
I
n
tellig
en
ce
,
Al
-
Z
ay
to
o
n
a
h
Un
iv
er
s
ity
Am
m
an
,
J
o
r
d
a
n
E
m
ail:
h
.
alsh
awa
b
k
h
@
zu
j.e
d
u
.
jo
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
r
a
p
id
d
ev
elo
p
m
en
t
o
f
m
e
tah
eu
r
is
tic
o
p
tim
izatio
n
tech
n
iq
u
es
h
as
s
ig
n
if
ica
n
tly
s
tr
en
g
th
en
ed
th
e
ca
p
ab
ilit
y
o
f
s
o
lv
in
g
co
m
p
lex
co
m
b
in
ato
r
ial
o
p
tim
izatio
n
p
r
o
b
lem
s
.
Am
o
n
g
th
ese
p
r
o
b
lem
s
,
th
e
tr
av
elin
g
s
alesm
an
p
r
o
b
lem
(
T
SP
)
r
em
ain
s
o
n
e
o
f
th
e
m
o
s
t
ch
allen
g
in
g
NP
-
h
ar
d
o
p
tim
izatio
n
ta
s
k
s
b
ec
au
s
e
o
f
its
ex
p
o
n
e
n
tial
s
ea
r
ch
s
p
ac
e
an
d
n
u
m
er
o
u
s
r
ea
l
-
wo
r
ld
ap
p
lica
tio
n
s
in
lo
g
is
tics
,
tr
an
s
p
o
r
tati
o
n
,
m
an
u
f
ac
tu
r
in
g
,
an
d
n
etwo
r
k
d
esig
n
.
Desp
ite
co
n
tin
u
o
u
s
p
r
o
g
r
ess
in
o
p
tim
izatio
n
alg
o
r
ith
m
s
,
m
ain
tai
n
in
g
an
ef
f
ec
tiv
e
b
alan
ce
b
etwe
en
g
l
o
b
al
ex
p
lo
r
atio
n
an
d
l
o
ca
l
ex
p
lo
itatio
n
r
em
ain
s
a
f
u
n
d
a
m
en
tal
ch
allen
g
e,
p
ar
ticu
lar
ly
f
o
r
lar
g
e
-
s
ca
le
T
SP
in
s
tan
ce
s
[
1
]
.
Po
p
u
latio
n
-
b
ased
s
war
m
in
tellig
en
ce
alg
o
r
ith
m
s
,
in
clu
d
i
n
g
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
an
d
th
e
w
h
ale
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
OA)
,
h
av
e
b
ee
n
s
u
cc
ess
f
u
lly
ap
p
lied
to
v
a
r
io
u
s
o
p
tim
izatio
n
p
r
o
b
lem
s
o
win
g
to
th
eir
s
im
p
le
s
tr
u
ctu
r
es
an
d
ef
f
icien
t
s
ea
r
ch
m
ec
h
a
n
is
m
s
.
Nev
er
th
eless
,
th
ese
alg
o
r
ith
m
s
f
r
eq
u
en
tly
e
x
p
er
ie
n
ce
p
r
em
atu
r
e
co
n
v
er
g
e
n
ce
a
n
d
lo
s
s
o
f
s
ea
r
ch
d
iv
er
s
ity
w
h
en
tack
lin
g
h
ig
h
-
d
im
en
s
io
n
al
co
m
b
in
at
o
r
ial
o
p
tim
izatio
n
p
r
o
b
lem
s
,
lim
itin
g
th
eir
ab
ilit
y
to
o
b
tain
h
ig
h
-
q
u
ality
s
o
lu
tio
n
s
[
2
]
.
O
th
er
s
war
m
-
b
ased
ap
p
r
o
ac
h
e
s
,
s
u
ch
as
th
e
s
alp
s
war
m
alg
o
r
ith
m
(
SS
A)
an
d
th
e
f
ir
e
f
ly
al
g
o
r
ith
m
(
FA)
,
h
av
e
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
8
1
9
-
2
8
3
5
2820
also
d
em
o
n
s
tr
ated
co
m
p
etitiv
e
o
p
tim
izatio
n
p
er
f
o
r
m
an
ce
.
Ho
wev
er
,
th
eir
ef
f
ec
tiv
e
n
e
s
s
is
o
f
ten
h
ig
h
ly
d
ep
en
d
e
n
t
o
n
p
ar
am
eter
s
ettin
g
s
,
an
d
t
h
eir
s
ea
r
ch
ca
p
ab
il
ity
m
ay
d
eter
i
o
r
ate
wh
en
d
ea
lin
g
with
co
m
p
le
x
m
u
ltimo
d
al
o
p
tim
izatio
n
lan
d
s
ca
p
es
[
3
]
.
T
o
o
v
er
co
m
e
th
ese
lim
itatio
n
s
,
r
ec
en
t
s
tu
d
ies
h
av
e
f
o
cu
s
ed
o
n
d
ev
elo
p
in
g
a
d
ap
tiv
e
m
etah
e
u
r
is
tic
f
r
am
ewo
r
k
s
ca
p
a
b
le
o
f
d
y
n
am
ically
ad
ju
s
tin
g
th
eir
s
ea
r
ch
b
eh
a
v
io
r
th
r
o
u
g
h
o
u
t
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
Su
ch
a
d
ap
tiv
e
m
ec
h
an
is
m
s
s
tr
en
g
th
en
th
e
b
alan
ce
b
etwe
en
ex
p
lo
r
atio
n
an
d
ex
p
l
o
itatio
n
wh
ile
r
ed
u
cin
g
th
e
d
ep
e
n
d
en
ce
o
n
m
a
n
u
al
p
ar
am
eter
tu
n
in
g
,
th
er
e
b
y
e
n
h
a
n
cin
g
co
n
v
er
g
en
ce
r
eliab
ilit
y
,
r
o
b
u
s
tn
ess
,
an
d
s
ca
lab
ilit
y
ac
r
o
s
s
d
if
f
er
e
n
t
o
p
tim
izatio
n
p
r
o
b
lem
s
[
4
]
.
A
n
o
th
er
p
r
o
m
is
in
g
r
esear
ch
d
ir
ec
tio
n
in
v
o
l
v
es
h
y
b
r
id
o
p
tim
izatio
n
f
r
am
ewo
r
k
s
th
at
in
teg
r
ate
ev
o
lu
tio
n
ar
y
co
m
p
u
tatio
n
with
s
war
m
in
tellig
en
ce
tech
n
iq
u
es.
B
y
co
m
b
in
in
g
co
m
p
lem
e
n
tar
y
s
ea
r
ch
s
tr
ateg
ies
with
in
a
u
n
if
ied
o
p
tim
izatio
n
m
o
d
el,
th
ese
h
y
b
r
id
ap
p
r
o
ac
h
es
s
tr
en
g
th
en
s
ea
r
ch
d
iv
er
s
ity
,
ac
ce
le
r
ate
co
n
v
er
g
en
ce
,
an
d
in
cr
ea
s
e
s
o
lu
tio
n
q
u
ality
f
o
r
ch
allen
g
in
g
co
m
b
in
ato
r
ial
o
p
t
im
izatio
n
p
r
o
b
lem
s
,
in
clu
d
in
g
r
o
u
tin
g
,
s
ch
e
d
u
lin
g
,
a
n
d
r
eso
u
r
ce
allo
ca
tio
n
[
5
]
.
Gr
o
win
g
atten
tio
n
h
as b
ee
n
d
i
r
ec
ted
to
war
d
h
ier
ar
ch
ical
s
war
m
in
tellig
en
ce
alg
o
r
ith
m
s
b
ec
au
s
e
th
ey
o
r
g
an
ize
th
e
s
ea
r
ch
p
r
o
ce
s
s
ac
co
r
d
in
g
to
th
e
q
u
ality
o
f
ca
n
d
id
ate
s
o
lu
tio
n
s
.
T
h
is
h
ier
ar
ch
ical
s
ea
r
ch
s
tr
ateg
y
en
ab
les
th
e
p
o
p
u
latio
n
to
m
o
v
e
p
r
o
g
r
e
s
s
iv
ely
to
war
d
p
r
o
m
is
in
g
r
eg
i
o
n
s
o
f
th
e
s
ea
r
ch
s
p
ac
e
wh
ile
av
o
id
in
g
e
x
ce
s
s
iv
e
co
m
p
u
tatio
n
al
co
s
t.
Su
ch
ch
ar
ac
ter
is
tics
h
av
e
m
ad
e
h
ier
ar
ch
ical
o
p
tim
izatio
n
ap
p
r
o
a
ch
es
attr
ac
tiv
e
f
o
r
s
o
lv
in
g
c
o
m
p
lex
co
m
b
in
ato
r
i
al
o
p
tim
izatio
n
p
r
o
b
lem
s
[
6
]
.
Am
o
n
g
h
ier
ar
c
h
ical
o
p
tim
izatio
n
alg
o
r
ith
m
s
,
t
h
e
g
r
ey
w
o
lf
o
p
tim
izer
(
GW
O)
h
as
attr
ac
ted
co
n
s
id
er
a
b
le
atten
tio
n
b
ec
a
u
s
e
o
f
its
s
im
p
le
m
ath
em
atica
l
f
o
r
m
u
latio
n
an
d
ad
a
p
tiv
e
lea
d
er
s
h
ip
m
ec
h
an
is
m
.
B
y
s
im
u
l
atin
g
th
e
c
o
o
p
e
r
ativ
e
h
u
n
tin
g
b
eh
av
i
o
r
o
f
al
p
h
a,
b
eta,
an
d
d
elta
wo
lv
es,
GW
O
p
r
o
v
id
es
an
ef
f
icien
t
s
ea
r
ch
s
tr
ateg
y
with
lim
ited
p
ar
am
eter
d
ep
en
d
en
cy
,
m
ak
in
g
it
a
s
u
itab
le
f
o
u
n
d
a
tio
n
f
o
r
d
ev
elo
p
in
g
h
y
b
r
id
o
p
tim
izatio
n
f
r
am
ewo
r
k
s
ca
p
ab
le
o
f
im
p
r
o
v
in
g
co
n
v
er
g
en
ce
p
er
f
o
r
m
an
ce
a
n
d
s
o
lu
tio
n
q
u
ality
[
7
]
.
C
u
r
r
en
t
r
esear
ch
in
m
etah
eu
r
is
tic
o
p
tim
izatio
n
h
as
f
o
cu
s
ed
o
n
im
p
r
o
v
in
g
co
n
v
er
g
en
ce
p
er
f
o
r
m
an
ce
,
m
ain
tain
i
n
g
s
o
lu
tio
n
d
i
v
er
s
ity
,
a
n
d
e
n
h
an
cin
g
s
ca
lab
ilit
y
f
o
r
co
m
p
lex
co
m
b
in
ato
r
ial
o
p
tim
izatio
n
p
r
o
b
lem
s
s
u
ch
a
s
th
e
T
SP
.
Nu
m
er
o
u
s
s
tu
d
ies
h
av
e
in
tr
o
d
u
ce
d
ad
ap
tiv
e
an
d
h
y
b
r
i
d
o
p
tim
izatio
n
s
tr
ateg
ies
to
o
v
er
co
m
e
t
h
e
lim
itatio
n
s
o
f
co
n
v
en
tio
n
a
l
p
o
p
u
latio
n
-
b
ase
d
alg
o
r
ith
m
s
.
T
h
e
f
o
llo
win
g
s
ec
tio
n
r
ev
iews
th
e
m
o
s
t
r
elev
an
t
ap
p
r
o
ac
h
es
th
at
h
av
e
co
n
tr
ib
u
ted
to
th
e
d
ev
elo
p
m
e
n
t o
f
m
o
d
e
r
n
m
etah
eu
r
is
tic
o
p
tim
izatio
n
f
r
am
ew
o
r
k
s
[
8
]
.
Mo
tiv
ated
b
y
t
h
ese
lim
itatio
n
s
,
th
is
s
tu
d
y
p
r
o
p
o
s
es
th
e
p
ar
t
icle
-
g
u
id
ed
a
d
ap
tiv
e
g
r
ey
wo
l
f
o
p
tim
izer
(
PMGHWO)
,
a
lig
h
tweig
h
t
ex
ten
s
io
n
o
f
th
e
o
r
ig
in
al
GW
O
f
o
r
lar
g
e
-
s
ca
le
T
SP
.
T
h
e
n
o
v
elty
o
f
PMGHWO
lies
in
th
e
co
o
r
d
in
ated
u
s
e
o
f
th
r
ee
co
m
p
lem
en
tar
y
m
e
ch
an
is
m
s
with
in
th
e
o
r
ig
in
al
GW
O
lead
er
s
h
ip
s
tr
u
ctu
r
e:
p
ar
ticle
-
g
u
i
d
ed
s
ea
r
ch
to
p
r
eser
v
e
ex
p
l
o
r
atio
n
,
ad
ap
tiv
e
r
ef
i
n
em
en
t
to
s
tr
e
n
g
th
en
e
x
p
lo
itatio
n
d
u
r
in
g
later
iter
atio
n
s
,
an
d
c
r
o
s
s
o
v
er
-
b
ased
s
o
lu
tio
n
g
en
e
r
atio
n
to
p
r
o
m
o
te
u
s
ef
u
l
r
ec
o
m
b
in
atio
n
.
Un
lik
e
h
y
b
r
id
ap
p
r
o
ac
h
es
t
h
at
co
m
b
in
e
s
ev
er
al
in
d
ep
en
d
en
t
o
p
tim
izer
s
o
r
in
tr
o
d
u
ce
m
an
y
ad
d
itio
n
al
c
o
n
tr
o
l
p
ar
am
eter
s
,
PMGHWO
k
ee
p
s
th
e
o
r
ig
in
al
al
p
h
a
–
b
eta
–
d
el
ta
h
ier
ar
ch
y
an
d
m
o
d
if
ies
th
e
s
ea
r
ch
b
eh
a
v
io
r
th
r
o
u
g
h
ad
a
p
tiv
e
in
ter
ac
tio
n
s
am
o
n
g
t
h
ese
co
m
p
o
n
en
ts
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
is
ev
alu
at
ed
o
n
te
n
T
SP
L
I
B
b
en
ch
m
ar
k
in
s
tan
ce
s
a
n
d
c
o
m
p
ar
ed
with
g
r
ey
w
o
lf
o
p
tim
izer
(
GW
O)
,
g
e
n
etic
alg
o
r
ith
m
s
(
GA)
,
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
wh
ale
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
OA)
,
an
d
Har
r
is
Haw
k
s
o
p
tim
izatio
n
(
HHO)
u
n
d
er
i
d
en
tical
ex
p
e
r
im
en
tal
c
o
n
d
itio
n
s
.
Desp
ite
th
e
co
n
s
id
er
ab
le
p
r
o
g
r
ess
in
m
etah
eu
r
is
tic
o
p
tim
izatio
n
,
s
o
lv
in
g
lar
g
e
-
s
ca
le
T
SP
r
em
ain
s
ch
allen
g
in
g
b
ec
au
s
e
th
e
s
ea
r
ch
s
p
ac
e
g
r
o
ws
r
ap
id
ly
wit
h
p
r
o
b
lem
s
ize.
E
x
is
tin
g
G
W
O
v
ar
ian
ts
h
av
e
im
p
r
o
v
e
d
p
er
f
o
r
m
an
ce
th
r
o
u
g
h
h
y
b
r
i
d
izatio
n
,
ad
ap
tiv
e
co
n
tr
o
l,
a
n
d
ad
d
itio
n
al
s
ea
r
ch
o
p
er
ato
r
s
.
Ho
we
v
er
,
m
an
y
o
f
th
ese
a
p
p
r
o
ac
h
es
ei
th
er
em
p
h
asize
ex
p
lo
r
atio
n
o
r
ex
p
l
o
itatio
n
,
r
e
q
u
ir
e
ad
d
itio
n
al
p
ar
am
eter
s
,
o
r
in
tr
o
d
u
ce
m
o
r
e
co
m
p
lex
s
ea
r
ch
s
tr
u
ctu
r
es.
Ma
in
tain
in
g
p
o
p
u
latio
n
d
iv
er
s
ity
in
th
e
ea
r
ly
s
tag
es
wh
ile
ef
f
ec
tiv
ely
r
ef
in
in
g
p
r
o
m
is
in
g
s
o
lu
tio
n
s
later
in
th
e
s
ea
r
ch
th
er
ef
o
r
e
r
em
ai
n
s
an
im
p
o
r
ta
n
t
ch
allen
g
e.
T
h
is
s
tu
d
y
ad
d
r
ess
es
th
is
g
ap
b
y
in
teg
r
atin
g
p
a
r
ticle
-
g
u
id
e
d
s
ea
r
ch
,
ad
ap
ti
v
e
r
ef
in
em
e
n
t,
an
d
cr
o
s
s
o
v
er
-
b
ased
s
o
lu
tio
n
g
en
e
r
atio
n
with
in
th
e
o
r
ig
in
al
GW
O
lead
er
s
h
ip
s
tr
u
ctu
r
e.
T
h
e
aim
is
to
im
p
r
o
v
e
s
o
lu
tio
n
q
u
ality
an
d
co
n
v
er
g
en
ce
b
eh
a
v
io
r
wh
ile
r
etain
in
g
a
r
elativ
ely
s
im
p
le
o
p
tim
izatio
n
f
r
am
ewo
r
k
an
d
co
m
p
etitiv
e
co
m
p
u
tatio
n
al
e
f
f
icien
cy
.
T
h
e
m
ain
o
b
jectiv
e
o
f
th
is
s
tu
d
y
is
to
d
ev
elo
p
an
d
e
v
alu
ate
th
e
PMGHWO
f
o
r
s
o
lv
in
g
m
ed
iu
m
-
an
d
lar
g
e
-
s
ca
le
T
SP
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
aim
s
to
im
p
r
o
v
e
th
e
b
alan
ce
b
etwe
en
ex
p
l
o
r
atio
n
an
d
ex
p
lo
itatio
n
b
y
co
m
b
in
in
g
p
ar
ticle
-
g
u
i
d
ed
s
ea
r
ch
,
ad
ap
tiv
e
r
ef
in
em
en
t,
a
n
d
cr
o
s
s
o
v
er
-
b
ased
s
o
lu
tio
n
g
e
n
er
atio
n
with
in
th
e
o
r
ig
in
al
GW
O
lead
er
s
h
ip
s
tr
u
ctu
r
e.
I
ts
p
e
r
f
o
r
m
an
ce
is
e
v
al
u
ated
o
n
ten
T
SP
L
I
B
b
en
ch
m
ar
k
in
s
tan
ce
s
an
d
co
m
p
ar
ed
with
GW
O,
GA,
PS
O,
W
OA,
an
d
HHO
u
n
d
er
id
en
tical
ex
p
er
im
e
n
tal
co
n
d
itio
n
s
.
T
h
e
e
v
alu
atio
n
co
n
s
id
er
s
s
o
lu
tio
n
q
u
ality
,
c
o
n
v
er
g
en
ce
b
e
h
av
io
r
,
co
m
p
u
tatio
n
al
ef
f
icien
cy
,
a
n
d
s
tatis
tical
s
ig
n
if
ican
ce
.
Ab
latio
n
an
d
p
ar
am
eter
s
en
s
it
iv
ity
an
aly
s
es
ar
e
also
co
n
d
u
c
ted
to
ex
am
in
e
th
e
co
n
tr
ib
u
ti
o
n
an
d
b
eh
a
v
io
r
o
f
th
e
p
r
o
p
o
s
ed
s
ea
r
ch
m
ec
h
an
is
m
s
.
T
h
e
m
ain
co
n
tr
ib
u
tio
n
s
o
f
th
is
s
tu
d
y
ar
e
s
u
m
m
a
r
ized
as f
o
llo
ws:
−
A
PMGHWO
i
s
p
r
o
p
o
s
ed
f
o
r
m
ed
iu
m
-
an
d
lar
g
e
-
s
ca
le
T
SP
,
wh
ile
p
r
eser
v
in
g
th
e
o
r
ig
i
n
al
alp
h
a
–
b
eta
–
d
elta
lead
er
s
h
ip
s
tr
u
ctu
r
e
o
f
G
W
O.
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
P
MGHW
O:
A
p
a
r
ticle
-
g
u
id
e
d
a
d
a
p
tive
g
r
ey
w
o
lf o
p
timiz
er fo
r
…
(
Ha
n
a
d
i A
l
-
S
h
a
w
a
b
ka
h
)
2821
−
A
p
ar
ticle
-
g
u
i
d
ed
s
ea
r
ch
m
ec
h
an
is
m
is
in
co
r
p
o
r
ated
t
o
m
ai
n
tain
p
o
p
u
latio
n
d
i
v
er
s
ity
an
d
s
u
p
p
o
r
t
b
r
o
ad
e
r
ex
p
lo
r
atio
n
,
p
ar
ticu
la
r
ly
d
u
r
in
g
th
e
ea
r
ly
s
tag
es o
f
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
−
An
ad
ap
tiv
e
r
ef
in
e
m
en
t
s
tr
ateg
y
is
in
tr
o
d
u
ce
d
t
o
p
r
o
g
r
ess
iv
ely
s
tr
en
g
th
en
s
o
lu
tio
n
im
p
r
o
v
em
en
t
d
u
r
in
g
th
e
later
s
ea
r
ch
s
tag
es,
p
r
o
v
id
i
n
g
a
s
m
o
o
t
h
er
tr
an
s
itio
n
f
r
o
m
ex
p
lo
r
atio
n
to
ex
p
lo
itatio
n
.
−
A
cr
o
s
s
o
v
er
-
b
ased
s
o
lu
tio
n
g
e
n
er
atio
n
m
ec
h
an
is
m
is
in
teg
r
a
ted
with
p
ar
ticle
-
g
u
i
d
ed
s
ea
r
ch
an
d
a
d
ap
tiv
e
r
ef
in
em
en
t,
allo
win
g
th
e
th
r
ee
co
m
p
o
n
e
n
ts
to
p
er
f
o
r
m
c
o
m
p
lem
en
tar
y
r
o
les
with
in
a
u
n
if
i
ed
o
p
tim
izatio
n
f
r
am
ewo
r
k
with
o
u
t c
h
a
n
g
in
g
t
h
e
asy
m
p
to
tic
co
m
p
u
tatio
n
al
c
o
m
p
lex
ity
o
f
GW
O.
−
T
h
e
co
n
t
r
ib
u
tio
n
o
f
th
e
p
r
o
p
o
s
ed
m
ec
h
an
is
m
s
is
ex
am
in
e
d
t
h
r
o
u
g
h
b
en
ch
m
a
r
k
e
x
p
er
im
e
n
ts
,
co
n
v
er
g
en
ce
an
d
r
u
n
tim
e
a
n
aly
s
es,
n
o
n
-
p
ar
am
etr
ic
s
tatis
tical
test
s
,
an
ab
latio
n
s
tu
d
y
,
a
n
d
p
ar
am
eter
s
en
s
itiv
ity
an
aly
s
is
.
T
h
e
r
esu
ltin
g
f
r
a
m
ewo
r
k
p
r
o
v
id
es
a
b
asis
f
o
r
f
u
t
u
r
e
ex
ten
s
io
n
s
to
o
th
er
lar
g
e
-
s
ca
le
co
m
b
in
ato
r
ial
o
p
tim
izatio
n
p
r
o
b
lem
s
.
2.
RE
L
AT
E
D
WO
RK
PS
O
r
em
ain
s
o
n
e
o
f
th
e
m
o
s
t
wid
ely
ad
o
p
ted
s
war
m
-
b
ased
o
p
tim
izatio
n
tech
n
i
q
u
es
b
ec
a
u
s
e
o
f
its
s
im
p
le
s
tr
u
ctu
r
e
a
n
d
r
ap
id
co
n
v
er
g
e
n
ce
.
Sev
e
r
al
r
ef
i
n
e
v
a
r
i
an
ts
h
av
e
i
n
tr
o
d
u
ce
d
ad
ap
tiv
e
in
er
tia
weig
h
ts
an
d
h
y
b
r
id
lo
ca
l
s
ea
r
ch
m
ec
h
an
i
s
m
s
to
s
tr
en
g
th
en
its
s
ea
r
c
h
ca
p
ab
ilit
y
a
n
d
r
e
f
in
e
o
p
ti
m
izatio
n
ac
cu
r
ac
y
.
Alth
o
u
g
h
th
ese
en
h
a
n
ce
m
en
t
s
h
av
e
s
tr
en
g
th
en
ed
p
e
r
f
o
r
m
a
n
ce
,
PS
O
m
ay
s
till
ex
p
er
ien
ce
r
ed
u
ce
d
s
o
lu
tio
n
d
iv
er
s
ity
an
d
p
r
em
atu
r
e
co
n
v
er
g
en
ce
wh
e
n
tack
lin
g
lar
g
e
-
s
ca
le
o
r
h
ig
h
ly
m
u
ltimo
d
al
o
p
t
im
izatio
n
p
r
o
b
lem
s
[
9
]
.
H
o
wev
er
,
n
o
n
e
o
f
th
ese
s
tu
d
ies
in
teg
r
ates
p
ar
ticle
-
g
u
i
d
ed
s
ea
r
ch
with
ad
a
p
tiv
e
r
ef
i
n
em
en
t
in
s
id
e
t
h
e
o
r
ig
in
al
GW
O
f
r
am
ewo
r
k
.
T
h
e
W
OA
h
as
al
s
o
b
ee
n
ex
ten
s
iv
ely
in
v
esti
g
ated
f
o
r
s
o
lv
in
g
co
m
p
lex
o
p
tim
izatio
n
p
r
o
b
lem
s
.
Sev
er
a
l im
p
r
o
v
ed
v
ar
ian
ts
h
av
e
in
co
r
p
o
r
ated
ch
a
o
tic
m
ap
s
,
L
év
y
f
li
g
h
t stra
teg
ies,
an
d
m
u
lti
-
p
o
p
u
latio
n
s
ea
r
ch
m
ec
h
an
is
m
s
to
en
h
an
ce
g
lo
b
al
ex
p
lo
r
atio
n
a
n
d
c
o
n
v
er
g
en
ce
s
p
ee
d
.
Nev
er
th
eless
,
m
ain
tain
in
g
co
m
p
etitiv
e
co
m
p
u
tatio
n
al
ef
f
icien
c
y
wh
ile
d
eliv
er
in
g
co
n
s
is
ten
t
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
co
m
p
lex
co
m
b
in
ato
r
ial
s
ea
r
ch
s
p
ac
es
r
em
ain
s
a
ch
allen
g
e,
p
ar
ticu
lar
ly
as
th
e
d
im
en
s
io
n
ality
o
f
th
e
p
r
o
b
lem
in
cr
ea
s
es
[
1
0
]
.
Alth
o
u
g
h
th
ese
im
p
r
o
v
e
m
en
ts
h
av
e
en
h
a
n
ce
d
th
e
s
ea
r
ch
ca
p
ab
ilit
y
o
f
W
OA,
th
eir
p
er
f
o
r
m
a
n
ce
m
ay
s
till
v
ar
y
ac
r
o
s
s
lar
g
e
-
s
ca
le
c
o
m
b
i
n
ato
r
ial
o
p
tim
izatio
n
p
r
o
b
lem
s
.
T
h
is
s
u
g
g
ests
th
at
im
p
r
o
v
i
n
g
a
s
in
g
le
s
ea
r
c
h
m
ec
h
an
is
m
is
o
f
ten
in
s
u
f
f
icie
n
t
to
en
s
u
r
e
s
tab
le
o
p
tim
izatio
n
p
er
f
o
r
m
an
ce
u
n
d
er
d
if
f
er
e
n
t
s
ea
r
ch
co
n
d
itio
n
s
.
T
h
e
SS
A
h
as
attr
ac
ted
c
o
n
s
id
er
ab
le
atten
tio
n
b
ec
a
u
s
e
o
f
its
ef
f
icien
t
ex
p
lo
r
atio
n
ca
p
a
b
ilit
y
an
d
r
elativ
ely
s
im
p
le
s
ea
r
ch
m
ec
h
an
is
m
.
T
o
r
ef
in
e
its
o
p
tim
izatio
n
p
er
f
o
r
m
an
ce
,
r
ec
en
t
s
tu
d
ies
h
av
e
in
t
eg
r
ated
d
if
f
e
r
en
tial
ev
o
lu
tio
n
o
p
er
at
o
r
s
an
d
ad
a
p
tiv
e
m
u
tatio
n
s
tr
ateg
ies,
lead
in
g
to
b
etter
co
n
v
er
g
e
n
ce
an
d
s
c
alab
ilit
y
.
Ho
wev
er
,
th
e
ex
p
lo
itatio
n
ca
p
ab
ilit
y
o
f
SS
A
m
ay
s
till
b
ec
o
m
e
in
s
u
f
f
icien
t
d
u
r
in
g
th
e
later
s
tag
es
o
f
o
p
tim
izatio
n
,
af
f
ec
tin
g
s
o
lu
tio
n
r
e
f
in
em
en
t
[
1
1
]
.
FA
h
as
b
ee
n
s
u
cc
ess
f
u
lly
ap
p
lied
to
a
wid
e
r
an
g
e
o
f
o
p
tim
izatio
n
p
r
o
b
lem
s
o
win
g
to
its
attr
ac
tio
n
-
b
ased
s
ea
r
ch
b
eh
av
i
o
r
.
R
ec
en
t
r
ef
in
em
en
ts
m
ain
ly
f
o
cu
s
o
n
c
o
m
b
in
in
g
ad
ap
tiv
e
attr
ac
tiv
en
ess
co
n
tr
o
l
with
lo
ca
l
s
ea
r
ch
s
tr
ateg
ies
to
ac
h
iev
e
h
i
g
h
er
s
o
lu
tio
n
ac
cu
r
ac
y
.
Desp
ite
th
ese
ad
v
an
ce
s
,
th
e
ad
d
itio
n
al
s
ea
r
ch
m
ec
h
a
n
is
m
s
o
f
ten
in
cr
ea
s
e
co
m
p
u
tatio
n
al
c
o
m
p
lex
ity
,
wh
ich
m
ay
r
e
d
u
ce
ef
f
icien
cy
wh
en
s
o
lv
in
g
lar
g
e
-
s
ca
le
o
p
tim
izatio
n
p
r
o
b
lem
s
[
1
2
]
.
T
h
ese
s
tu
d
ies
d
em
o
n
s
tr
ate
th
at
ad
ap
tiv
e
o
p
er
ato
r
s
ca
n
r
ef
i
n
e
o
p
tim
iz
atio
n
p
er
f
o
r
m
a
n
ce
.
Nev
er
t
h
eless
,
m
ain
tain
in
g
an
e
f
f
ec
tiv
e
b
alan
ce
b
etwe
en
ex
p
lo
r
atio
n
a
n
d
ex
p
lo
itatio
n
t
h
r
o
u
g
h
o
u
t
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
r
em
ain
s
a
co
m
m
o
n
ch
a
llen
g
e,
p
ar
ticu
lar
ly
f
o
r
co
m
p
lex
b
en
ch
m
a
r
k
p
r
o
b
l
em
s
.
B
ey
o
n
d
im
p
r
o
v
in
g
in
d
iv
i
d
u
al
s
war
m
-
b
ased
alg
o
r
ith
m
s
,
e
v
o
lu
tio
n
ar
y
ap
p
r
o
ac
h
es
s
u
ch
a
s
GA
h
av
e
also
b
ee
n
a
p
p
lied
to
p
r
ac
tical
o
p
tim
izatio
n
p
r
o
b
lem
s
.
Fo
r
e
x
am
p
le,
Al
-
Ma
d
i
an
d
Hn
aif
e
m
p
lo
y
ed
a
g
en
etic
alg
o
r
ith
m
-
b
ased
ap
p
r
o
ac
h
f
o
r
tr
af
f
ic
s
ig
n
al
o
p
tim
izatio
n
,
d
em
o
n
s
tr
atin
g
t
h
e
ap
p
licab
ilit
y
o
f
e
v
o
lu
tio
n
ar
y
s
ea
r
ch
to
co
m
p
lex
o
p
tim
izatio
n
an
d
d
ec
is
io
n
-
m
ak
in
g
p
r
o
b
lem
s
[
1
3
]
.
An
o
th
er
im
p
o
r
tan
t
r
esear
ch
d
ir
e
ctio
n
in
v
o
lv
es
h
ier
ar
c
h
ical
s
war
m
in
tellig
en
ce
m
o
d
els,
wh
er
e
th
e
s
ea
r
ch
p
r
o
ce
s
s
is
g
u
id
ed
ac
co
r
d
in
g
to
th
e
q
u
ality
o
f
ca
n
d
i
d
ate
s
o
lu
tio
n
s
.
Am
o
n
g
th
ese
ap
p
r
o
ac
h
es,
th
e
GW
O
h
as
g
ain
ed
c
o
n
s
id
er
ab
le
atte
n
tio
n
b
ec
a
u
s
e
o
f
its
s
im
p
le
m
ath
em
atica
l
f
o
r
m
u
la
tio
n
an
d
ad
ap
tiv
e
lead
er
s
h
ip
h
ier
ar
ch
y
.
T
h
ese
ch
ar
ac
ter
is
tics
m
ak
e
GW
O
a
s
u
itab
le
f
o
u
n
d
atio
n
f
o
r
co
n
s
tr
u
ctin
g
h
y
b
r
id
o
p
tim
izatio
n
f
r
a
m
ewo
r
k
s
ca
p
ab
le
o
f
im
p
r
o
v
in
g
b
o
th
e
x
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
p
er
f
o
r
m
an
ce
[
1
4
]
.
W
h
ile
h
y
b
r
id
o
p
tim
izati
o
n
s
tr
ateg
ies
h
av
e
p
r
o
d
u
ce
d
e
n
co
u
r
a
g
in
g
r
esu
lts
,
m
an
y
ex
is
tin
g
a
p
p
r
o
ac
h
es
r
e
ly
o
n
c
o
m
b
in
i
n
g
m
u
ltip
le
o
p
tim
izatio
n
tech
n
iq
u
es
o
r
in
tr
o
d
u
cin
g
ad
d
itio
n
al
co
n
tr
o
l
p
a
r
am
eter
s
.
Su
ch
d
esi
g
n
s
m
ay
in
cr
ea
s
e
p
er
f
o
r
m
a
n
c
e,
b
u
t
th
ey
also
in
cr
ea
s
e
alg
o
r
ith
m
ic
co
m
p
lex
ity
an
d
r
ed
u
ce
g
e
n
er
al
ap
p
licab
ilit
y
ac
r
o
s
s
d
if
f
er
en
t
o
p
tim
iz
atio
n
s
ce
n
ar
io
s
.
R
ec
en
t
co
m
p
r
eh
en
s
iv
e
r
e
v
iew
s
tu
d
ies
h
av
e
h
ig
h
lig
h
te
d
th
e
co
n
tin
u
o
u
s
ev
o
l
u
tio
n
o
f
m
eta
h
eu
r
is
tic
o
p
tim
izatio
n
f
r
o
m
c
o
n
v
en
tio
n
al
s
war
m
in
tellig
en
ce
alg
o
r
ith
m
s
to
war
d
ad
a
p
tiv
e
a
n
d
h
y
b
r
id
o
p
tim
iz
atio
n
f
r
a
m
ewo
r
k
s
.
T
h
ese
r
ev
i
ews
em
p
h
asize
th
at
ad
ap
tiv
e
h
y
b
r
id
s
tr
ateg
ies
p
r
o
v
id
e
a
p
r
o
m
is
in
g
d
ir
ec
tio
n
f
o
r
im
p
r
o
v
in
g
s
ca
lab
ilit
y
,
co
n
v
er
g
en
ce
b
eh
a
v
io
r
,
an
d
o
p
tim
izatio
n
r
o
b
u
s
tn
ess
ac
r
o
s
s
co
m
p
lex
o
p
tim
izatio
n
p
r
o
b
lem
s
[
1
5
]
.
A
r
ep
r
esen
tativ
e
e
x
am
p
le
o
f
a
d
ap
tiv
e
h
y
b
r
id
o
p
tim
izatio
n
is
th
e
en
h
an
ce
d
m
o
th
–
f
lam
e
o
p
tim
izatio
n
(
MFO)
ap
p
r
o
ac
h
r
e
p
o
r
ted
in
[
1
6
]
.
B
y
in
co
r
p
o
r
atin
g
n
ew
s
elec
tio
n
m
ec
h
an
is
m
s
in
to
th
e
o
r
ig
i
n
al
MFO
f
r
am
ewo
r
k
,
th
e
s
tu
d
y
d
em
o
n
s
tr
ated
th
at
ad
ap
tiv
e
m
o
d
if
icatio
n
s
co
u
ld
i
m
p
r
o
v
e
o
p
tim
izatio
n
p
er
f
o
r
m
an
ce
an
d
s
ea
r
ch
b
e
h
av
io
r
.
T
h
ese
f
in
d
in
g
s
f
u
r
th
e
r
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
8
1
9
-
2
8
3
5
2822
illu
s
tr
ate
th
e
p
o
ten
tial
o
f
ad
ap
tiv
e
s
ea
r
ch
s
tr
ateg
ies
f
o
r
ad
d
r
ess
in
g
co
m
p
lex
o
p
tim
izatio
n
p
r
o
b
lem
s
.
R
ec
en
t
r
esear
ch
clea
r
ly
in
d
icate
s
th
a
t
ad
ap
tiv
e
s
ea
r
ch
s
tr
ateg
ies
h
av
e
b
ec
o
m
e
a
m
ajo
r
d
ir
ec
tio
n
in
m
etah
eu
r
is
tic
o
p
tim
izatio
n
.
R
ath
er
th
an
r
ely
in
g
o
n
i
n
cr
ea
s
in
g
ly
c
o
m
p
le
x
h
y
b
r
id
s
tr
u
ctu
r
es,
th
er
e
is
g
r
o
win
g
in
ter
est
in
d
ev
elo
p
in
g
lig
h
tweig
h
t
o
p
tim
izatio
n
f
r
am
ewo
r
k
s
th
at
r
ef
in
e
s
ea
r
ch
ef
f
icien
c
y
wh
ile
p
r
e
s
er
v
in
g
alg
o
r
ith
m
ic
s
im
p
licity
.
R
ec
en
t
r
ef
in
em
en
ts
in
GW
O
-
b
ased
alg
o
r
ith
m
s
f
o
r
th
e
tr
a
v
elin
g
s
alesm
an
p
r
o
b
lem
h
av
e
also
co
n
f
ir
m
e
d
th
at
in
co
r
p
o
r
atin
g
ad
ap
tiv
e
s
ea
r
ch
m
ec
h
an
is
m
s
ca
n
s
u
b
s
tan
tially
b
o
o
s
t
s
o
lu
tio
n
q
u
ality
an
d
co
n
v
er
g
en
ce
ch
ar
ac
ter
is
tics
.
Nev
er
th
eless
,
m
ain
tain
in
g
co
m
p
etitiv
e
co
m
p
u
tatio
n
al
ef
f
icien
cy
wh
ile
p
r
eser
v
in
g
a
n
ef
f
ec
tiv
e
b
ala
n
ce
b
etwe
en
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
r
e
m
ain
s
a
s
ig
n
if
ican
t
ch
allen
g
e
,
p
ar
ticu
lar
ly
f
o
r
lar
g
e
-
s
ca
le
b
en
ch
m
ar
k
in
s
tan
ce
s
[
1
7
]
.
T
h
ese
o
b
s
er
v
atio
n
s
h
ig
h
lig
h
t
th
at,
d
esp
ite
th
e
co
n
tin
u
o
u
s
d
ev
elo
p
m
en
t
o
f
GW
O
v
ar
ian
ts
,
ac
h
iev
in
g
co
n
s
is
ten
t
o
p
tim
izatio
n
p
er
f
o
r
m
an
ce
o
n
lar
g
e
-
s
ca
le
T
SP
r
em
ain
s
an
ac
tiv
e
r
esear
ch
ch
allen
g
e.
T
h
is
m
o
tiv
ate
s
th
e
d
ev
elo
p
m
en
t
o
f
th
e
p
r
o
p
o
s
ed
PMGHWO
f
r
am
ewo
r
k
,
wh
ich
co
m
b
i
n
es
p
ar
ticle
-
g
u
id
e
d
s
ea
r
ch
with
a
d
ap
tiv
e
r
e
f
in
em
en
t
t
o
s
tr
en
g
th
en
b
o
t
h
ex
p
lo
r
atio
n
an
d
co
n
v
er
g
e
n
ce
with
o
u
t
i
n
tr
o
d
u
cin
g
u
n
n
ec
ess
ar
y
alg
o
r
ith
m
ic
co
m
p
lex
ity
to
p
r
o
v
id
e
a
cle
ar
er
co
m
p
ar
is
o
n
o
f
r
ec
en
t
d
e
v
elo
p
m
e
n
ts
in
m
etah
eu
r
is
tic
o
p
tim
izatio
n
,
T
ab
l
e
1
s
u
m
m
ar
izes
th
e
m
ain
h
y
b
r
id
an
d
ad
a
p
tiv
e
s
tr
ateg
ies
r
ep
o
r
ted
in
r
ec
en
t
s
tu
d
ies
an
d
h
ig
h
lig
h
ts
th
eir
r
ep
o
r
ted
p
er
f
o
r
m
an
ce
g
ai
n
s
ac
r
o
s
s
d
if
f
er
en
t
p
r
o
b
lem
d
o
m
ain
s
.
T
ab
le
1
.
Su
m
m
a
r
y
o
f
k
e
y
h
y
b
r
id
an
d
a
d
ap
tiv
e
m
etah
e
u
r
is
tic
ad
v
an
ce
s
(
2
0
2
0
–
2
0
2
5
)
A
l
g
o
r
i
t
h
m
/
A
p
p
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a
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me
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y
M
a
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n
B
e
n
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P
S
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d
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p
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v
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a
w
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g
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sea
r
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a
st
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W
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C
h
a
o
s
ma
p
s,
Lé
v
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f
l
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B
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p
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SSA
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b
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t
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a
t
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p
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r
f
o
r
m
a
n
c
e
Me
tah
eu
r
is
tic
o
p
tim
izatio
n
h
a
s
ev
o
lv
ed
f
r
o
m
b
asic
s
war
m
in
tellig
en
ce
m
eth
o
d
s
to
m
o
r
e
ad
v
a
n
ce
d
h
y
b
r
id
a
n
d
ad
a
p
tiv
e
f
r
am
ew
o
r
k
s
th
at
aim
to
r
ef
in
e
p
e
r
f
o
r
m
an
ce
in
s
o
lv
in
g
co
m
p
lex
o
p
tim
izatio
n
p
r
o
b
lem
s
.
Desp
ite
th
ese
d
ev
elo
p
m
en
ts
,
m
an
y
ex
is
tin
g
a
p
p
r
o
ac
h
es
s
till
r
ely
o
n
p
ar
tially
i
n
teg
r
ated
s
t
r
ateg
ies
an
d
r
e
q
u
ir
e
m
an
u
al
p
a
r
am
eter
tu
n
in
g
,
w
h
ich
ca
n
lim
it
th
eir
e
f
f
icien
cy
a
n
d
ad
a
p
tab
ilit
y
.
T
h
e
r
ef
o
r
e
,
th
e
r
e
is
s
till
a
n
ee
d
f
o
r
s
im
p
ler
an
d
m
o
r
e
ef
f
ec
tiv
e
ad
ap
tiv
e
ap
p
r
o
ac
h
es
th
at
e
n
s
u
r
e
a
p
r
o
p
er
tr
a
d
e
-
o
f
f
b
etwe
en
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
,
esp
ec
ially
in
lar
g
e
-
s
ca
le
co
m
b
in
ato
r
ial
p
r
o
b
lem
s
lik
e
th
e
T
S
P
.
I
n
th
is
co
n
tex
t,
th
is
s
tu
d
y
p
r
o
p
o
s
es
a
GW
O
-
b
ased
h
y
b
r
i
d
f
r
am
ewo
r
k
to
in
cr
ea
s
e
ad
ap
tab
ilit
y
,
r
ed
u
ce
c
o
m
p
lex
ity
,
a
n
d
en
h
a
n
ce
o
v
er
all
p
er
f
o
r
m
an
ce
.
3.
M
E
T
H
O
DO
L
O
G
Y
T
h
e
p
r
o
p
o
s
ed
PMGHWO
is
d
ev
elo
p
e
d
as
an
ex
ten
s
io
n
o
f
th
e
o
r
ig
i
n
al
GW
O
to
en
h
an
ce
its
s
ea
r
ch
p
er
f
o
r
m
an
ce
o
n
lar
g
e
-
s
ca
le
T
SP
.
R
ath
er
th
an
ch
an
g
in
g
th
e
co
r
e
lead
er
s
h
ip
m
ec
h
a
n
is
m
o
f
GW
O,
th
e
m
eth
o
d
p
r
eser
v
es
th
e
o
r
ig
in
al
alp
h
a
–
b
eta
–
d
elta
h
ier
ar
c
h
y
an
d
co
m
p
lem
en
ts
it
with
a
p
ar
ticle
-
g
u
i
d
ed
s
ea
r
ch
s
tr
ateg
y
an
d
an
ad
a
p
tiv
e
r
ef
i
n
em
en
t
p
r
o
ce
s
s
.
T
h
e
o
v
er
all
d
esig
n
o
f
t
h
e
p
r
o
p
o
s
ed
f
r
a
m
ewo
r
k
is
illu
s
tr
ated
in
Fig
u
r
e
1
T
h
ese
co
m
p
o
n
en
ts
wo
r
k
to
g
e
th
er
th
r
o
u
g
h
o
u
t
t
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
,
allo
win
g
th
e
p
o
p
u
latio
n
to
ex
p
l
o
r
e
n
ew
s
ea
r
ch
r
eg
io
n
s
in
th
e
ea
r
l
y
s
tag
es
wh
ile
g
r
ad
u
ally
f
o
cu
s
in
g
o
n
r
ef
in
i
n
g
p
r
o
m
is
in
g
s
o
lu
tio
n
s
as
th
e
s
ea
r
c
h
p
r
o
g
r
ess
es.
T
h
is
d
esig
n
h
elp
s
m
ain
tain
s
ea
r
ch
d
i
v
er
s
ity
an
d
s
u
p
p
o
r
ts
a
m
o
r
e
s
tab
le
co
n
v
er
g
en
ce
to
war
d
h
ig
h
-
q
u
ality
s
o
lu
tio
n
s
.
3
.
1
.
Desig
n principl
es o
f
P
M
G
H
WO
T
h
e
d
esig
n
o
f
PMGHWO
f
o
cu
s
es
o
n
im
p
r
o
v
in
g
th
e
b
alan
ce
b
etwe
en
e
x
p
lo
r
atio
n
a
n
d
e
x
p
l
o
itatio
n
in
th
e
o
r
ig
in
al
GW
O
wh
ile
m
ain
tain
in
g
a
s
im
p
le
o
p
tim
izatio
n
s
tr
u
ctu
r
e.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
co
m
b
in
es
th
r
ee
co
m
p
lem
en
tar
y
m
ec
h
a
n
is
m
s
:
p
ar
ticle
-
g
u
id
e
d
s
ea
r
ch
,
ad
ap
tiv
e
r
ef
in
em
en
t,
an
d
cr
o
s
s
o
v
er
-
b
ased
d
iv
er
s
if
icatio
n
.
T
h
e
p
ar
ticle
-
g
u
id
ed
c
o
m
p
o
n
en
t
in
tr
o
d
u
ce
s
a
d
d
itio
n
al
s
ea
r
ch
d
ir
ec
tio
n
s
to
im
p
r
o
v
e
p
o
p
u
latio
n
d
iv
er
s
ity
,
wh
ile
th
e
ad
a
p
tiv
e
r
ef
in
em
en
t
m
ec
h
an
is
m
g
r
ad
u
ally
s
tr
en
g
th
en
s
th
e
e
x
p
lo
it
atio
n
o
f
p
r
o
m
is
in
g
r
eg
io
n
s
as
th
e
s
ea
r
ch
p
r
o
g
r
e
s
s
es.
T
h
e
cr
o
s
s
o
v
er
o
p
er
at
o
r
f
u
r
t
h
er
p
r
o
m
o
tes
in
f
o
r
m
atio
n
ex
c
h
an
g
e
am
o
n
g
ca
n
d
id
ate
s
o
lu
tio
n
s
an
d
h
elp
s
r
ed
u
ce
p
r
em
atu
r
e
c
o
n
v
e
r
g
en
ce
.
T
h
ese
m
ec
h
an
is
m
s
ar
e
in
t
eg
r
ated
with
in
th
e
GW
O
s
ea
r
ch
p
r
o
ce
s
s
with
o
u
t
r
ep
lacin
g
its
o
r
ig
i
n
al
lead
er
s
h
i
p
s
tr
u
ctu
r
e,
allo
win
g
PMGHWO
to
p
r
eser
v
e
th
e
m
ain
ch
ar
ac
ter
is
tics
o
f
GW
O
wh
ile
im
p
r
o
v
in
g
its
s
ea
r
ch
ca
p
ab
ilit
y
f
o
r
th
e
T
SP
.
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
P
MGHW
O:
A
p
a
r
ticle
-
g
u
id
e
d
a
d
a
p
tive
g
r
ey
w
o
lf o
p
timiz
er fo
r
…
(
Ha
n
a
d
i A
l
-
S
h
a
w
a
b
ka
h
)
2823
Fi
g
u
r
e
1
.
C
o
n
ce
p
tu
al
ar
ch
itect
u
r
e
o
f
t
h
e
p
r
o
p
o
s
ed
PMGHWO f
r
am
ewo
r
k
3.
2
.
B
a
s
eline
a
lg
o
rit
hm
f
o
r
perf
o
rm
a
nce
co
m
pa
riso
n
T
o
ev
alu
ate
th
e
ef
f
ec
tiv
en
ess
o
f
PMGHWO,
its
p
er
f
o
r
m
an
c
e
was
co
m
p
ar
ed
with
f
iv
e
wi
d
ely
u
s
ed
m
etah
eu
r
is
tic
o
p
tim
izatio
n
alg
o
r
ith
m
s
,
n
am
ely
th
e
GW
O,
GA,
PS
O,
W
OA,
an
d
HHO.
T
h
ese
alg
o
r
ith
m
s
wer
e
s
elec
ted
b
ec
au
s
e
th
ey
r
ep
r
esen
t
d
if
f
er
en
t
o
p
tim
izatio
n
s
tr
ateg
ies
an
d
h
av
e
b
ee
n
s
u
cc
e
s
s
f
u
lly
ap
p
lied
to
co
m
b
in
ato
r
ial
o
p
tim
izatio
n
p
r
o
b
lem
s
.
T
o
en
s
u
r
e
a
f
air
co
m
p
ar
is
o
n
,
all
alg
o
r
ith
m
s
wer
e
e
x
ec
u
ted
u
n
d
er
th
e
s
am
e
ex
p
er
im
en
tal
co
n
d
itio
n
s
u
s
in
g
id
en
tical
b
en
c
h
m
ar
k
i
n
s
tan
ce
s
,
p
o
p
u
latio
n
s
ize,
s
to
p
p
in
g
cr
iter
ia,
an
d
co
m
p
u
tatio
n
al
e
n
v
ir
o
n
m
en
t.
Sin
ce
m
etah
eu
r
is
tic
alg
o
r
ith
m
s
in
v
o
lv
e
s
to
ch
asti
c
s
ea
r
ch
p
r
o
ce
s
s
es,
ea
ch
ex
p
er
im
en
t
was
r
ep
ea
te
d
in
d
e
p
en
d
en
tly
,
an
d
t
h
e
r
e
p
o
r
te
d
r
esu
lts
co
r
r
esp
o
n
d
to
th
e
av
er
a
g
e
v
alu
es
o
b
tain
e
d
o
v
er
ten
r
u
n
s
.
3.
3
.
B
enchm
a
r
k
pro
blem
s
T
h
e
ex
p
e
r
im
en
tal
ev
alu
atio
n
was
co
n
d
u
cted
u
s
in
g
ten
b
en
c
h
m
ar
k
i
n
s
tan
ce
s
s
elec
ted
f
r
o
m
th
e
well
-
estab
lis
h
ed
T
SP
L
I
B
r
ep
o
s
ito
r
y
.
T
h
ese
b
en
c
h
m
ar
k
in
s
tan
ce
s
r
ep
r
esen
t
d
if
f
e
r
en
t
lev
els
o
f
c
o
m
p
lex
ity
,
r
an
g
in
g
f
r
o
m
s
m
all
-
to
lar
g
e
-
s
ca
le
T
SP
,
allo
win
g
a
co
m
p
r
eh
en
s
i
v
e
ass
ess
m
en
t
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
u
n
d
er
v
ar
io
u
s
s
ea
r
ch
co
n
d
itio
n
s
.
T
h
e
s
elec
ted
in
s
tan
ce
s
in
clu
d
e
eil5
1
,
b
er
lin
5
2
,
k
r
o
A
1
0
0
,
c
h
1
3
0
,
p
r
1
5
2
,
r
at1
9
5
,
d
1
9
8
,
lin
3
1
8
,
r
d
4
0
0
,
a
n
d
p
cb
4
4
2
.
Af
ter
in
tr
o
d
u
cin
g
th
e
m
ain
d
esig
n
c
o
n
ce
p
ts
o
f
PMGHWO,
th
e
s
eq
u
en
ce
o
f
o
p
er
atio
n
s
p
er
f
o
r
m
ed
d
u
r
in
g
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
is
illu
s
tr
ated
in
Fig
u
r
e
2
T
h
e
alg
o
r
ith
m
s
tar
ts
b
y
g
en
er
atin
g
an
in
itial
p
o
p
u
latio
n
an
d
ev
alu
atin
g
th
e
ca
n
d
id
ate
s
o
lu
tio
n
s
to
id
en
tify
th
e
t
h
r
ee
lead
in
g
wo
lv
es.
Gu
id
ed
b
y
th
ese
lead
er
s
,
th
e
p
o
p
u
latio
n
is
u
p
d
ated
iter
ativ
ely
u
s
in
g
th
e
p
r
o
p
o
s
ed
p
ar
ticle
-
g
u
id
ed
s
ea
r
c
h
an
d
ad
ap
tiv
e
r
ef
in
em
e
n
t
m
ec
h
an
is
m
s
.
T
h
is
p
r
o
ce
s
s
co
n
tin
u
es
u
n
til
th
e
s
to
p
p
in
g
cr
iter
io
n
is
r
ea
ch
ed
,
af
ter
wh
ich
th
e
b
est to
u
r
f
o
u
n
d
is
r
ep
o
r
ted
as th
e
f
in
al
s
o
lu
tio
n
.
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
8
1
9
-
2
8
3
5
2824
3.
4
.
P
M
G
H
WO
f
r
a
m
ewo
r
k
PMGHW
O
ex
ten
d
s
th
e
co
n
v
e
n
tio
n
al
GW
O
wh
ile
r
etain
in
g
its
alp
h
a
–
b
eta
–
d
elta
lead
er
s
h
i
p
s
tr
u
ctu
r
e.
T
h
e
m
ain
d
if
f
e
r
en
ce
lies
in
h
o
w
ca
n
d
id
ate
s
o
lu
tio
n
s
ar
e
g
en
er
ated
an
d
r
ef
in
ed
d
u
r
in
g
th
e
s
ea
r
ch
.
Par
ticle
-
g
u
id
ed
s
ea
r
c
h
is
u
s
ed
to
d
i
v
e
r
s
if
y
ca
n
d
id
ate
m
o
v
es
an
d
r
e
d
u
ce
ex
ce
s
s
iv
e
co
n
ce
n
tr
atio
n
ar
o
u
n
d
th
e
cu
r
r
en
t
lead
er
s
,
wh
ile
cr
o
s
s
o
v
er
allo
ws
u
s
ef
u
l
in
f
o
r
m
atio
n
f
r
o
m
d
if
f
er
en
t
s
o
lu
tio
n
s
to
b
e
r
ec
o
m
b
in
ed
.
Ad
a
p
tiv
e
r
ef
in
em
en
t
is
ap
p
lied
m
o
r
e
s
tr
o
n
g
ly
d
u
r
in
g
th
e
later
iter
a
tio
n
s
to
im
p
r
o
v
e
p
r
o
m
is
in
g
t
o
u
r
s
th
r
o
u
g
h
l
o
ca
l
s
ea
r
ch
.
R
ath
er
th
an
o
p
er
atin
g
as
s
ep
ar
ate
o
p
tim
izatio
n
alg
o
r
ith
m
s
,
th
ese
m
ec
h
an
is
m
s
ar
e
in
co
r
p
o
r
ated
in
to
th
e
GW
O
s
ea
r
ch
cy
cle
an
d
th
eir
r
o
les
ch
an
g
e
as
th
e
s
ea
r
ch
p
r
o
g
r
ess
es.
T
h
is
d
es
ig
n
allo
ws
PMG
HW
O
t
o
em
p
h
asize
ex
p
lo
r
ati
o
n
at
th
e
b
eg
in
n
in
g
o
f
t
h
e
s
ea
r
ch
an
d
g
r
ad
u
ally
d
ev
o
te
m
o
r
e
ef
f
o
r
t
to
s
o
lu
tio
n
r
ef
in
em
e
n
t
with
o
u
t r
ep
lacin
g
th
e
b
asic G
W
O
lead
er
s
h
ip
m
ec
h
an
is
m
.
Fig
u
r
e
2
.
W
o
r
k
f
lo
w
o
f
th
e
p
r
o
p
o
s
ed
PMGHWO a
lg
o
r
ith
m
3.
5
.
M
a
t
hema
t
ica
l
f
o
rm
ula
t
i
o
n o
f
P
M
G
H
WO
T
h
ese
m
ec
h
a
n
is
m
s
ar
e
in
te
g
r
a
ted
in
to
th
e
s
ea
r
ch
p
r
o
ce
s
s
to
p
r
o
m
o
te
d
iv
e
r
s
if
icatio
n
d
u
r
in
g
th
e
ea
r
ly
iter
atio
n
s
wh
ile
p
r
o
g
r
ess
iv
ely
s
tr
en
g
th
en
in
g
th
e
r
ef
i
n
em
en
t o
f
p
r
o
m
is
in
g
s
o
lu
tio
n
s
.
T
h
e
f
o
ll
o
win
g
s
u
b
s
ec
tio
n
s
d
escr
ib
e
th
e
m
ain
c
o
m
p
o
n
en
ts
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
.
3
.
5
.
1
.
Sea
rc
h
div
er
s
it
y
a
nd
ex
plo
ra
t
io
n
Sear
ch
d
iv
er
s
ity
in
PMGHWO
is
m
ain
tain
ed
th
r
o
u
g
h
th
e
co
m
b
in
ed
u
s
e
o
f
lea
d
er
-
g
u
i
d
ed
r
ec
o
m
b
in
atio
n
,
p
ar
ticle
-
g
u
id
e
d
p
er
tu
r
b
atio
n
,
a
n
d
lim
ited
a
cc
ep
tan
ce
o
f
n
o
n
-
im
p
r
o
v
in
g
ca
n
d
id
ates.
R
ath
er
th
an
ex
p
licitly
ca
lcu
latin
g
a
p
o
p
u
latio
n
-
d
iv
e
r
s
ity
in
d
ex
d
u
r
in
g
o
p
tim
izatio
n
,
t
h
e
im
p
lem
en
ted
alg
o
r
ith
m
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
P
MGHW
O:
A
p
a
r
ticle
-
g
u
id
e
d
a
d
a
p
tive
g
r
ey
w
o
lf o
p
timiz
er fo
r
…
(
Ha
n
a
d
i A
l
-
S
h
a
w
a
b
ka
h
)
2825
p
r
o
m
o
tes
d
iv
er
s
if
icatio
n
th
r
o
u
g
h
its
s
ea
r
ch
o
p
e
r
ato
r
s
.
Du
r
in
g
th
e
f
ir
s
t
5
5
%
o
f
th
e
iter
atio
n
s
,
p
ar
ticle
-
g
u
id
ed
p
er
tu
r
b
atio
n
m
ay
r
el
o
ca
te
a
city
to
a
n
ea
r
b
y
p
o
s
itio
n
in
t
h
e
ca
n
d
id
ate
to
u
r
with
a
p
r
o
b
ab
ilit
y
o
f
0
.
2
5
.
I
n
ad
d
itio
n
,
a
n
o
n
-
im
p
r
o
v
in
g
ca
n
d
id
ate
m
a
y
b
e
r
etain
ed
with
a
s
m
all
p
r
o
b
ab
ilit
y
o
f
0
.
0
2
.
T
h
ese
m
ec
h
an
is
m
s
r
ed
u
ce
th
e
lik
elih
o
o
d
o
f
p
r
e
m
atu
r
e
co
n
v
er
g
en
ce
wh
ile
p
r
es
er
v
in
g
th
e
p
er
m
u
tatio
n
s
tr
u
ct
u
r
e
o
f
f
ea
s
ib
le
T
SP
to
u
r
s
.
3
.
5
.
2
.
G
WO
-
ba
s
ed
lea
dersh
i
p g
uid
a
nce
T
h
e
p
r
o
p
o
s
ed
PMGHWO
p
r
eser
v
es
th
e
lead
er
s
h
i
p
p
r
in
cip
l
e
o
f
th
e
GW
O
b
y
id
e
n
tify
in
g
th
e
th
r
ee
b
est
s
o
lu
tio
n
s
in
th
e
cu
r
r
e
n
t
p
o
p
u
latio
n
as
al
p
h
a,
b
eta,
an
d
d
elta.
Ho
wev
er
,
b
ec
a
u
s
e
th
e
tr
av
elin
g
s
alesm
an
p
r
o
b
lem
is
r
ep
r
esen
ted
u
s
in
g
d
is
cr
ete
p
er
m
u
tatio
n
s
o
f
city
in
d
ices,
PMGH
W
O
d
o
es
n
o
t
d
ir
ec
tly
ap
p
ly
th
e
co
n
tin
u
o
u
s
p
o
s
itio
n
-
u
p
d
ate
eq
u
atio
n
s
o
f
th
e
o
r
ig
i
n
al
GW
O.
I
n
s
tead
,
o
n
e
s
o
lu
tio
n
is
r
an
d
o
m
ly
s
elec
ted
f
r
o
m
alp
h
a,
b
eta,
an
d
d
elta
as
th
e
f
ir
s
t
p
ar
en
t,
wh
ile
th
e
g
lo
b
al
-
b
est
to
u
r
is
u
s
ed
as
th
e
s
ec
o
n
d
p
ar
e
n
t.
T
h
ese
lead
er
s
h
ip
s
o
lu
tio
n
s
g
u
id
e
th
e
g
en
er
atio
n
o
f
n
ew
ca
n
d
id
a
te
to
u
r
s
th
r
o
u
g
h
p
er
m
u
tatio
n
-
p
r
eser
v
in
g
s
ea
r
ch
o
p
er
ato
r
s
.
T
h
is
s
tr
ateg
y
r
etain
s
th
e
lead
er
-
g
u
id
ed
s
ea
r
ch
c
o
n
ce
p
t
o
f
GW
O
wh
ile
ad
ap
tin
g
it
to
th
e
d
is
cr
ete
s
tr
u
ctu
r
e
o
f
t
h
e
T
SP
.
3
.
5
.
3
.
Ada
ptiv
e
ca
nd
ida
t
e
g
e
nera
t
io
n a
nd
re
f
inem
ent
I
n
PMGHWO,
ca
n
d
id
ate
to
u
r
s
ar
e
g
en
er
ated
u
s
in
g
th
e
G
W
O
lead
er
s
h
ip
in
f
o
r
m
atio
n
to
g
eth
er
with
cr
o
s
s
o
v
er
,
p
ar
ticle
-
g
u
id
ed
p
er
tu
r
b
atio
n
,
a
n
d
ad
a
p
tiv
e
lo
ca
l
r
ef
in
em
en
t.
At
ea
ch
iter
atio
n
,
t
h
e
alp
h
a,
b
eta,
a
n
d
d
elta
s
o
lu
tio
n
s
ar
e
id
en
tifie
d
ac
co
r
d
in
g
to
th
ei
r
f
itn
ess
v
alu
es.
On
e
o
f
th
ese
th
r
ee
lead
er
s
i
s
r
an
d
o
m
ly
s
elec
ted
as
th
e
f
ir
s
t
p
ar
en
t,
wh
ile
th
e
g
l
o
b
al
-
b
est
s
o
lu
tio
n
is
u
s
ed
as
t
h
e
s
ec
o
n
d
p
a
r
en
t.
Or
d
er
cr
o
s
s
o
v
er
is
th
en
ap
p
lied
with
a
p
r
o
b
a
b
ilit
y
o
f
0
.
9
0
to
g
en
er
ate
a
n
ew
ca
n
d
id
ate
to
u
r
;
o
th
er
wis
e,
th
e
s
elec
ted
lead
er
is
r
etain
ed
.
Du
r
in
g
th
e
ea
r
ly
s
ea
r
ch
s
tag
e,
d
ef
in
e
d
as
th
e
f
ir
s
t
5
5
%
o
f
th
e
to
tal
iter
atio
n
s
,
p
ar
ticle
-
g
u
id
ed
p
er
tu
r
b
atio
n
is
ap
p
lied
with
a
p
r
o
b
ab
ilit
y
o
f
0
.
2
5
.
T
h
e
p
er
tu
r
b
atio
n
r
elo
ca
tes
o
n
e
c
ity
to
a
n
ea
r
b
y
p
o
s
itio
n
i
n
th
e
to
u
r
,
p
r
o
v
id
in
g
a
co
n
tr
o
lled
m
o
d
if
icatio
n
th
at
s
u
p
p
o
r
ts
ex
p
lo
r
atio
n
with
o
u
t
s
u
b
s
tan
tially
d
is
r
u
p
tin
g
th
e
cu
r
r
en
t
s
o
lu
tio
n
s
tr
u
ctu
r
e.
Du
r
in
g
th
e
later
s
ea
r
ch
s
tag
e,
af
ter
6
0
%
o
f
th
e
to
t
al
iter
atio
n
s
,
lo
ca
l
r
ef
in
em
e
n
t
is
ac
tiv
ated
with
a
p
r
o
b
a
b
ilit
y
o
f
0
.
7
0
.
A
b
o
u
n
d
e
d
2
-
o
p
t
p
r
o
ce
d
u
r
e
with
a
m
ax
i
m
u
m
o
f
2
5
tr
ials
is
u
s
ed
to
im
p
r
o
v
e
th
e
ca
n
d
id
ate
to
u
r
.
T
h
is
s
tag
e
s
tr
en
g
th
e
n
s
ex
p
lo
itatio
n
a
r
o
u
n
d
p
r
o
m
is
in
g
s
o
lu
tio
n
s
wh
ile
k
ee
p
i
n
g
th
e
ad
d
itio
n
al
co
m
p
u
tatio
n
al
ef
f
o
r
t
b
o
u
n
d
ed
.
Af
ter
th
ese
o
p
er
atio
n
s
,
th
e
c
an
d
id
ate
is
co
m
p
ar
e
d
with
its
s
elec
ted
p
ar
en
t.
I
f
th
e
ca
n
d
id
ate
h
as
a
s
h
o
r
ter
to
u
r
len
g
th
,
it
r
ep
lace
s
th
e
p
ar
e
n
t.
Oth
er
wis
e,
th
e
ca
n
d
id
ate
is
ac
ce
p
ted
with
a
s
m
all
p
r
o
b
ab
ilit
y
o
f
0
.
0
2
;
if
it
is
n
o
t
ac
ce
p
ted
,
th
e
p
a
r
en
t
is
r
etain
ed
.
T
h
is
ac
ce
p
tan
ce
m
e
ch
an
is
m
p
r
im
ar
il
y
f
av
o
r
s
im
p
r
o
v
in
g
s
o
lu
tio
n
s
wh
ile
allo
win
g
lim
ited
d
iv
er
s
if
ic
atio
n
d
u
r
i
n
g
th
e
s
ea
r
c
h
.
3.
6
.
T
he
o
ptim
izer
a
lg
o
rit
h
m
(
P
s
eudo
-
co
de)
Alg
o
r
ith
m
1
s
u
m
m
ar
izes
th
e
m
ain
s
tep
s
o
f
PMGHWO.
T
h
e
p
r
o
ce
d
u
r
e
b
eg
i
n
s
with
p
o
p
u
latio
n
in
itializatio
n
an
d
f
itn
ess
ev
al
u
atio
n
,
f
o
llo
wed
b
y
th
e
i
d
en
t
if
icatio
n
o
f
t
h
e
alp
h
a,
b
eta,
a
n
d
d
elta
s
o
lu
tio
n
s
.
C
an
d
id
ate
to
u
r
s
ar
e
th
e
n
u
p
d
a
ted
iter
ativ
ely
ac
co
r
d
in
g
to
th
e
PMGHW
O
s
ea
r
ch
p
r
o
ce
d
u
r
e
u
n
til
th
e
s
to
p
p
in
g
cr
iter
io
n
is
r
ea
ch
ed
.
T
h
e
b
est to
u
r
o
b
tain
ed
d
u
r
in
g
th
e
s
ea
r
ch
is
r
etu
r
n
ed
as th
e
f
in
al
s
o
lu
tio
n
.
Alg
o
r
ith
m
1
.
PMGHWO P
s
eu
d
o
-
co
d
e
Input:
Distance matrix
, population size
, maximum iterations
Output:
Best tour and its fitness
1.
Initialize a population of
random valid TSP tours.
2.
Evaluate the fitness (tour length) of each solution.
3.
Identify Alpha, Beta, and Delta as the three best solutions.
4.
Initialize the global
-
best solution.
5.
For
=
1
to
do
6.
Rank the population according to fitness.
7.
Update Alpha, Beta, Delta, and the global
-
best solution.
8.
For
each solution
in the population
do
9.
Randomly select Parent1 from {Alpha, Beta, Delta}.
10.
Set Parent2 = global
-
best solution.
11.
With probability 0.90:
12.
Generate a candidate tour using Order Crossover
between Parent1 and Parent2.
13.
Otherwise:
14.
Candidate = Parent1.
15.
If
<
0
.
55
,
then
16.
With probability 0.25:
17.
Apply particle
-
guided perturbation to Candidate.
18.
End if
19.
If
>
0
.
60
,
then
20.
With probability 0.70:
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
8
1
9
-
2
8
3
5
2826
21.
Apply bounded 2
-
opt local refinement
to Candidate (maximum 25 trials).
22.
End if
23.
Evaluate Candidate.
24.
If
Candidate is better than Parent1,
then
25.
Accept Candidate.
26.
Else
27.
Accept Candidate with probability 0.02;
28.
otherwise retain Parent1.
29.
End if
30.
End for
31.
Replace the current population with the updated population.
32.
Update the global
-
best solution if an improved tour is found.
33.
End for
34.
Return
the global
-
best tour and its fitness.
3.
7
.
I
m
ple
m
ent
a
t
io
n
d
et
a
ils
PMGHW
O
was
im
p
lem
en
ted
in
Py
th
o
n
u
s
in
g
a
p
er
m
u
tatio
n
-
b
ased
r
e
p
r
esen
tatio
n
f
o
r
th
e
T
SP
.
E
ac
h
ca
n
d
id
ate
s
o
lu
tio
n
is
r
ep
r
esen
ted
as
an
o
r
d
er
ed
s
eq
u
e
n
ce
o
f
city
in
d
ices
in
wh
ich
e
v
er
y
c
ity
ap
p
ea
r
s
ex
ac
tly
o
n
ce
.
I
n
itial
to
u
r
s
ar
e
g
en
er
at
ed
as
r
an
d
o
m
p
er
m
u
tatio
n
s
o
f
all
citie
s
.
T
o
p
r
eser
v
e
f
ea
s
ib
ilit
y
th
r
o
u
g
h
o
u
t
th
e
s
ea
r
ch
,
all
s
ea
r
ch
o
p
er
ato
r
s
ar
e
p
er
m
u
tatio
n
-
p
r
eser
v
in
g
.
Or
d
er
cr
o
s
s
o
v
e
r
r
etain
s
a
s
u
b
s
eq
u
en
ce
f
r
o
m
o
n
e
p
ar
en
t
an
d
f
ills
th
e
r
em
ain
in
g
p
o
s
itio
n
s
u
s
in
g
n
o
n
-
d
u
p
lica
ted
cities
f
r
o
m
th
e
s
ec
o
n
d
p
a
r
en
t.
T
h
e
p
a
r
ticle
-
g
u
id
ed
p
er
tu
r
b
atio
n
r
elo
ca
tes
a
s
in
g
le
city
to
a
n
ea
r
b
y
p
o
s
itio
n
,
wh
ile
th
e
b
o
u
n
d
ed
2
-
o
p
t
p
r
o
ce
d
u
r
e
r
e
v
er
s
es
a
s
elec
ted
to
u
r
s
eg
m
en
t
wh
en
a
n
im
p
r
o
v
em
en
t
is
o
b
tain
ed
.
T
h
ese
o
p
er
atio
n
s
th
er
ef
o
r
e
m
o
d
if
y
th
e
o
r
d
er
in
g
o
f
cities
with
o
u
t
in
tr
o
d
u
cin
g
d
u
p
licated
o
r
m
is
s
in
g
cities.
T
h
e
PMGHWO
s
ea
r
ch
p
r
o
ce
d
u
r
e
u
s
es
th
e
th
r
ee
cu
r
r
en
t
GW
O
lead
er
s
an
d
th
e
g
lo
b
al
-
b
est
s
o
lu
tio
n
to
g
u
id
e
ca
n
d
id
ate
g
e
n
er
atio
n
.
Or
d
er
c
r
o
s
s
o
v
er
is
ap
p
lied
with
a
p
r
o
b
ab
ilit
y
o
f
0
.
9
0
.
D
u
r
in
g
th
e
f
ir
s
t
5
5
%
o
f
th
e
ite
r
atio
n
s
,
p
ar
ticle
-
g
u
i
d
ed
p
e
r
tu
r
b
atio
n
is
ac
tiv
ated
with
a
p
r
o
b
ab
ilit
y
o
f
0
.
2
5
to
s
u
p
p
o
r
t
ex
p
lo
r
atio
n
.
A
f
ter
6
0
%
o
f
th
e
to
tal
iter
atio
n
s
,
b
o
u
n
d
e
d
2
-
o
p
t
lo
ca
l
r
ef
in
em
en
t
is
ap
p
lied
with
a
p
r
o
b
ab
ilit
y
o
f
0
.
7
0
u
s
in
g
a
m
ax
im
u
m
o
f
2
5
tr
ials
.
C
an
d
id
ate
s
o
lu
tio
n
s
a
r
e
ac
ce
p
ted
wh
en
th
ey
im
p
r
o
v
e
o
n
th
e
s
elec
ted
p
ar
en
t,
wh
ile
a
n
o
n
-
im
p
r
o
v
in
g
ca
n
d
id
ate
is
r
etain
ed
with
a
s
m
all
p
r
o
b
a
b
ilit
y
o
f
0
.
0
2
to
m
ain
tain
lim
ited
s
ea
r
ch
d
iv
er
s
ity
.
3.
8
.
E
x
perim
ent
a
l
s
et
up
T
h
e
ex
p
er
im
e
n
ts
wer
e
co
n
d
u
cted
o
n
th
e
ten
T
SP
L
I
B
in
s
t
an
ce
s
d
escr
ib
ed
in
s
ec
tio
n
3
.
3
.
T
ab
le
2
s
u
m
m
ar
izes
th
e
co
m
p
u
tatio
n
a
l
en
v
ir
o
n
m
en
t,
p
a
r
am
eter
s
ettin
g
s
,
s
to
p
p
in
g
c
r
iter
ia,
an
d
p
er
f
o
r
m
an
ce
m
ea
s
u
r
es
u
s
ed
in
th
e
ev
al
u
atio
n
.
T
h
ese
s
ettin
g
s
wer
e
k
ep
t c
o
n
s
is
ten
t a
cr
o
s
s
th
e
co
m
p
ar
e
d
alg
o
r
ith
m
s
.
T
ab
le
2
.
E
x
p
er
im
en
tal
c
o
n
f
ig
u
r
atio
n
an
d
p
ar
am
eter
s
ettin
g
s
C
o
m
p
o
n
e
n
t
S
e
t
t
i
n
g
P
r
o
g
r
a
m
mi
n
g
l
a
n
g
u
a
g
e
P
y
t
h
o
n
3
.
1
0
H
a
r
d
w
a
r
e
e
n
v
i
r
o
n
me
n
t
I
n
t
e
l
C
o
r
e
i
7
/
1
6
G
B
R
A
M
O
p
e
r
a
t
i
n
g
sy
st
e
m
W
i
n
d
o
w
s 1
0
/
1
1
(
6
4
-
b
i
t
)
P
o
p
u
l
a
t
i
o
n
s
i
z
e
30
M
a
x
i
m
u
m
i
t
e
r
a
t
i
o
n
s
3
0
0
I
n
d
e
p
e
n
d
e
n
t
r
u
n
s
10
I
n
i
t
i
a
l
i
z
a
t
i
o
n
me
t
h
o
d
R
a
n
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o
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p
e
r
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t
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t
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r
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r
i
a
M
a
x
i
m
u
m
n
u
m
b
e
r
o
f
i
t
e
r
a
t
i
o
n
s
B
e
n
c
h
mar
k
p
r
o
b
l
e
ms
TSP
LI
B
TSP
i
n
st
a
n
c
e
s
P
e
r
f
o
r
ma
n
c
e
me
t
r
i
c
s
B
e
st
F
i
t
n
e
ss,
A
v
e
r
a
g
e
F
i
t
n
e
ss,
R
u
n
t
i
m
e
,
S
t
a
n
d
a
r
d
D
e
v
i
a
t
i
o
n
3.
9
.
P
a
ra
m
et
er
s
ens
it
iv
it
y
a
na
ly
s
is
A
s
en
s
itiv
ity
an
aly
s
i
s
was
c
o
n
d
u
cte
d
to
ex
am
in
e
th
e
ef
f
ec
t
o
f
th
e
ad
ap
tiv
e
r
ef
in
em
e
n
t
r
ate
o
n
PMGHW
O.
Fiv
e
v
alu
es
(
0
.
2
0
,
0
.
4
0
,
0
.
6
0
,
0
.
8
0
,
a
n
d
1
.
0
0
)
wer
e
test
ed
o
n
f
o
u
r
r
e
p
r
esen
tativ
e
T
SP
L
I
B
in
s
tan
ce
s
:
k
r
o
A1
0
0
,
p
r
1
5
2
,
lin
3
1
8
,
a
n
d
p
cb
4
4
2
.
All
o
th
er
ex
p
er
im
en
tal
s
ettin
g
s
wer
e
k
ep
t
u
n
ch
an
g
ed
,
an
d
ea
ch
co
n
f
ig
u
r
atio
n
was
ev
alu
a
ted
to
b
e
o
v
er
ten
in
d
ep
en
d
en
t
r
u
n
s
.
T
h
e
r
ef
in
e
m
en
t
m
ec
h
a
n
is
m
was
ac
tiv
ated
af
ter
6
5
%
o
f
th
e
to
tal
iter
atio
n
.
T
h
is
an
aly
s
is
was
u
s
ed
to
ass
es
s
h
o
w
d
if
f
er
en
t
r
ef
in
em
en
t
r
ates
af
f
ec
t
th
e
s
o
lu
tio
n
q
u
ality
o
f
PMGHWO
.
3.
10
.
Co
m
pu
t
a
t
i
o
na
l
co
m
plex
it
y
a
na
ly
s
is
L
et
N
d
en
o
te
th
e
p
o
p
u
latio
n
s
ize,
T
th
e
n
u
m
b
er
o
f
iter
atio
n
s
,
an
d
D
th
e
n
u
m
b
er
o
f
cities.
I
n
ea
ch
iter
atio
n
,
PMGHWO
p
r
o
ce
s
s
e
s
th
e
p
o
p
u
latio
n
an
d
e
v
alu
ates
ca
n
d
id
ate
to
u
r
s
,
wh
er
e
th
e
c
o
m
p
u
tatio
n
o
f
a
to
u
r
len
g
th
r
eq
u
ir
es
O(
D)
tim
e.
T
h
e
p
ar
ticle
-
g
u
id
e
d
p
er
tu
r
b
atio
n
an
d
th
e
b
o
u
n
d
e
d
2
-
o
p
t r
ef
in
e
m
en
t r
eq
u
ir
e
at
m
o
s
t
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
P
MGHW
O:
A
p
a
r
ticle
-
g
u
id
e
d
a
d
a
p
tive
g
r
ey
w
o
lf o
p
timiz
er fo
r
…
(
Ha
n
a
d
i A
l
-
S
h
a
w
a
b
ka
h
)
2827
lin
ea
r
tim
e
with
r
esp
ec
t
to
t
h
e
to
u
r
s
ize
u
n
d
er
th
e
f
ix
e
d
n
u
m
b
er
o
f
r
ef
in
e
m
en
t
tr
ials
u
s
ed
i
n
th
is
s
tu
d
y
.
Ho
wev
er
,
th
e
im
p
lem
e
n
ted
o
r
d
er
-
cr
o
s
s
o
v
er
o
p
er
ato
r
p
e
r
f
o
r
m
s
p
er
m
u
tatio
n
-
p
r
eser
v
in
g
m
em
b
er
s
h
ip
c
h
ec
k
s
wh
ile
co
n
s
tr
u
ctin
g
a
ca
n
d
id
a
te
to
u
r
,
r
esu
ltin
g
in
a
wo
r
s
t
-
ca
s
e
co
m
p
lex
ity
o
f
(
2
)
.
Sin
ce
cr
o
s
s
o
v
er
is
ap
p
lied
d
u
r
in
g
ca
n
d
id
ate
g
en
e
r
atio
n
ac
r
o
s
s
th
e
p
o
p
u
latio
n
,
i
t
d
o
m
in
ates
th
e
p
er
-
iter
atio
n
c
o
m
p
u
tatio
n
al
co
s
t.
T
h
er
ef
o
r
e,
th
e
o
v
er
all
wo
r
s
t
-
c
ase
tim
e
co
m
p
lex
ity
o
f
th
e
im
p
lem
en
ted
PMGHWO is ex
p
r
ess
ed
in
(
1
)
:
O
(
N
×
T
×
D
2
)
(
1
)
T
h
e
ad
d
itio
n
al
s
ea
r
ch
m
ec
h
a
n
is
m
s
in
cr
ea
s
e
th
e
co
m
p
u
tatio
n
al
co
s
t
co
m
p
ar
ed
with
th
e
b
aselin
e
GW
O,
wh
ich
is
co
n
s
i
s
ten
t
wit
h
th
e
r
u
n
tim
e
r
esu
lts
r
ep
o
r
ted
in
s
ec
tio
n
4
.
4
.
T
h
e
m
em
o
r
y
r
e
q
u
ir
em
en
t
r
e
m
ain
s
O(
ND)
,
s
in
ce
th
e
alg
o
r
ith
m
s
to
r
es a
p
o
p
u
latio
n
o
f
N
ca
n
d
id
ate
to
u
r
s
,
ea
ch
c
o
n
tain
in
g
D
city
in
d
ices.
As
s
h
o
wn
in
T
ab
le
3
,
th
e
im
p
lem
en
ted
PMGHWO
h
as
a
h
ig
h
er
wo
r
s
t
-
ca
s
e
asy
m
p
to
tic
co
m
p
lex
ity
th
an
th
e
s
im
p
ler
GW
O
an
d
PS
O
im
p
lem
en
tatio
n
s
b
ec
a
u
s
e
o
f
its
p
er
m
u
tatio
n
-
p
r
eser
v
i
n
g
o
r
d
er
-
cr
o
s
s
o
v
er
o
p
er
atio
n
.
I
ts
asy
m
p
to
tic
o
r
d
e
r
is
co
m
p
ar
a
b
le
to
th
e
im
p
lem
en
ted
GA,
W
OA,
an
d
HHO
v
ar
ian
ts
,
wh
ich
also
em
p
lo
y
o
r
d
er
cr
o
s
s
o
v
er
.
T
h
e
p
r
ac
tical
r
u
n
tim
e
im
p
ac
t
o
f
th
es
e
o
p
er
atio
n
s
is
ex
am
in
e
d
in
s
e
ctio
n
4
.
4
.
T
ab
le
3
.
C
o
m
p
a
r
is
o
n
o
f
th
eo
r
e
tical
co
m
p
u
tatio
n
al
co
m
p
lex
it
y
A
l
g
o
r
i
t
h
m
Ti
me
c
o
m
p
l
e
x
i
t
y
GA
O
(
N
×
T
×
D
)
PSO
O
(
N
×
T
×
D
)
W
O
A
O
(
N
×
T
×
D
)
HHO
O
(
N
×
T
×
D
)
G
W
O
O
(
N
×
T
×
D
)
PMG
HWO
O(
N
× T
×
)
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
r
o
p
o
s
ed
PMGHWO
was
ev
alu
ated
o
n
a
s
et
o
f
s
tan
d
ar
d
T
SP
L
I
B
b
en
ch
m
ar
k
in
s
tan
ce
s
u
s
in
g
th
e
ex
p
er
im
en
tal
s
ettin
g
s
d
escr
ib
ed
in
th
e
p
r
ev
io
u
s
s
ec
tio
n
.
T
h
is
s
ec
tio
n
r
ep
o
r
ts
th
e
r
esu
lts
o
b
tain
ed
an
d
co
m
p
ar
es
th
e
p
er
f
o
r
m
a
n
ce
o
f
PMGHW
O
with
f
iv
e
well
-
estab
lis
h
ed
m
etah
eu
r
is
tic
alg
o
r
it
h
m
s
.
T
h
e
ev
alu
atio
n
co
n
s
id
er
s
s
o
lu
tio
n
q
u
ality
,
r
u
n
tim
e,
co
n
v
er
g
e
n
ce
b
eh
av
io
r
,
an
d
s
tatis
tical
s
ig
n
if
ican
ce
to
p
r
o
v
id
e
a
co
m
p
r
eh
e
n
s
iv
e
ass
ess
m
en
t o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
4
.
1
.
O
v
er
a
ll
perf
o
r
m
a
nce
co
m
pa
riso
n
T
ab
le
4
co
m
p
ar
es
th
e
av
er
a
g
e
to
u
r
len
g
t
h
s
o
b
tain
ed
b
y
PM
GHWO
an
d
th
e
f
iv
e
b
aselin
e
alg
o
r
ith
m
s
ac
r
o
s
s
th
e
ten
T
SP
L
I
B
in
s
tan
ce
s
.
Sin
ce
th
e
o
b
jectiv
e
o
f
th
e
T
SP
is
to
m
in
im
ize
th
e
to
ta
l
to
u
r
len
g
th
,
lo
wer
v
alu
es
in
d
icate
b
etter
s
o
lu
tio
n
s
.
PMGH
W
O
ac
h
iev
ed
th
e
lo
west
av
er
ag
e
to
u
r
len
g
th
o
n
ev
er
y
in
s
tan
ce
co
n
s
id
er
ed
in
th
e
e
x
p
er
im
en
t.
T
h
e
d
if
f
er
e
n
ce
was
p
ar
ticu
lar
l
y
n
o
ticea
b
le
o
n
t
h
e
lar
g
er
p
r
o
b
lem
s
.
Fo
r
p
cb
4
4
2
,
f
o
r
e
x
am
p
le,
PMGHWO
o
b
tain
ed
an
av
e
r
ag
e
t
o
u
r
len
g
th
o
f
3
2
8
,
6
0
8
.
6
0
,
co
m
p
a
r
ed
with
3
9
2
,
1
5
2
.
9
0
f
o
r
GW
O
an
d
3
7
9
,
2
1
6
.
4
0
f
o
r
HHO.
A
s
im
ilar
p
atter
n
ca
n
b
e
s
ee
n
o
n
lin
3
1
8
,
w
h
er
e
th
e
co
r
r
esp
o
n
d
in
g
v
al
u
es
wer
e
2
1
2
,
6
9
2
.
8
0
,
2
6
5
,
6
0
1
.
7
0
,
a
n
d
2
6
4
,
1
5
7
.
7
0
.
Acr
o
s
s
th
e
co
m
p
lete
b
en
ch
m
a
r
k
s
et,
th
e
av
er
ag
e
to
u
r
len
g
th
o
b
tain
ed
b
y
PMGHWO
wa
s
a
p
p
r
o
x
im
ately
1
8
.
7
5
%
lo
wer
t
h
an
GW
O,
1
9
.
7
7
%
lo
wer
th
a
n
HHO,
an
d
2
3
.
3
0
%
lo
wer
th
an
GA.
T
h
e
d
if
f
er
en
ce
s
r
elativ
e
to
W
OA
an
d
PS
O
wer
e
3
9
.
0
8
%
an
d
5
7
.
5
1
%,
r
esp
ec
tiv
ely
.
T
h
ese
r
esu
lts
s
h
o
w
th
at
th
e
p
er
f
o
r
m
an
ce
ad
v
a
n
tag
e
was
n
o
t
co
n
f
in
ed
to
o
n
e
p
r
o
b
lem
s
ize
o
r
a
s
m
all
g
r
o
u
p
o
f
in
s
tan
ce
s
.
T
h
e
co
n
tr
ib
u
tio
n
o
f
th
e
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I
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I
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t J E
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&
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Vo
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16
,
No
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5
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Octo
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2828
4
.
2
.
Abla
t
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s
t
ud
y
An
ab
latio
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s
tu
d
y
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co
n
d
u
c
ted
to
ex
am
in
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th
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co
n
tr
ib
u
tio
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o
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o
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in
PMGHW
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p
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ar
ately
wh
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em
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in
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n
ch
an
g
ed
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T
ab
le
5
r
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d
Fig
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3
s
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T
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.
Ab
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Evaluation Warning : The document was created with Spire.PDF for Python.