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elem
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1
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[
2
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4
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1
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I
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1419
2.
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(
+
.
)
−
1
]
(
2
)
ℎ
=
+
.
ℎ
(
3
)
T
h
e
ce
ll
tem
p
er
atu
r
e
in
th
is
in
s
tan
ce
is
T
in
Kelv
in
(
K)
,
th
e
elec
tr
o
n
ch
ar
g
e
is
=
1
.
60217646
×
10
−
19
C
,
th
e
d
io
d
e
id
ea
lity
f
ac
to
r
is
n
,
an
d
th
e
B
o
ltzm
an
n
c
o
n
s
tan
t
is
=
1
.
3806503
×
10
−
23
J
/K.
I
t
is
p
o
s
s
ib
le
to
r
ep
r
esen
t th
e
o
u
tp
u
t c
u
r
r
en
t
u
s
in
g
(
1
)
-
(
3
).
=
−
0
[
(
(
+
.
)
−
1
]
−
+
.
ℎ
(
4
)
T
h
e
f
iv
e
u
n
k
n
o
wn
p
a
r
am
eter
s
ar
e
I
pv
, I
0
, R
s
, R
sh
,
an
d
n
.
Fig
u
r
e
1
.
T
h
e
s
in
g
le
d
io
d
e
m
o
d
el'
s
co
r
r
esp
o
n
d
in
g
elec
tr
ic
cir
cu
it
3.
WAR
ST
RAT
E
G
Y
O
P
T
I
M
I
Z
AT
I
O
N
An
cien
t
k
in
g
d
o
m
s
,
w
h
ich
k
ep
t
m
ilit
ar
y
f
o
r
ce
s
m
ad
e
u
p
o
f
m
an
y
g
r
o
u
p
s
to
r
ep
el
e
n
em
y
i
n
cu
r
s
io
n
s
,
s
er
v
ed
as
th
e
m
o
d
el
f
o
r
th
e
W
SO
[
1
8
]
.
E
ac
h
k
in
g
d
o
m
cr
ea
ted
tactica
l
s
tr
ateg
ies
to
co
u
n
ter
en
em
y
tr
o
o
p
s
d
u
r
in
g
b
attles,
an
d
t
h
e
m
o
n
a
r
ch
o
r
co
m
m
a
n
d
er
s
et
u
p
p
a
r
ticu
lar
co
o
r
d
in
atin
g
m
eth
o
d
s
to
ac
h
iev
e
g
o
als.
W
SO
h
as
two
m
ain
s
tr
ateg
ies
[
1
9
]
.
T
h
e
f
ir
s
t
is
"a
ttack
m
o
d
e,
"
in
wh
ich
e
v
er
y
s
o
ld
ie
r
s
h
i
f
ts
p
o
s
itio
n
s
b
ased
o
n
wh
er
e
th
e
co
m
m
an
d
er
an
d
k
in
g
ar
e
.
T
h
e
s
o
ld
ier
wh
o
is
th
e
m
o
s
t
f
it
is
cr
o
wn
ed
th
e
n
ex
t
k
in
g
in
th
is
m
o
d
e
[
2
0
]
.
E
v
er
y
s
o
ld
ier
s
tar
ts
f
r
o
m
th
e
s
am
e
s
tar
tin
g
p
o
in
t,
an
d
t
h
eir
r
an
k
s
r
is
e
wh
en
th
ey
ac
c
o
m
p
lis
h
th
eir
g
o
als:
(
+
1
)
=
(
)
+
.
.
(
−
(
)
)
+
2
.
.
(
−
(
)
)
(
5
)
T
h
e
n
ew
s
o
ld
ier
'
s
p
o
s
it
io
n
is
r
ep
r
esen
ted
b
y
Yi(
t
+
1
)
,
th
e
o
l
d
s
o
ld
ier
b
y
Yi(
t)
,
th
e
co
m
m
a
n
d
er
b
y
C
,
th
e
m
o
n
ar
ch
b
y
K,
th
e
weig
h
t
b
y
W
i,
an
d
th
e
th
r
esh
o
ld
v
alu
e
b
y
ρ
.
T
h
e
s
o
ld
ier
will
d
ec
i
d
e
to
r
e
m
ain
in
th
eir
p
r
esen
t p
o
s
itio
n
if
t
h
e
attac
k
p
o
wer
(
f
n
)
o
f
th
e
n
ew
o
n
e
is
less
th
an
th
e
ex
is
tin
g
p
o
s
itio
n
'
s
f
p
.
(
+
1
)
=
(
(
)
)
∗
(
<
)
+
(
+
1
)
∗
(
≥
)
(
6
)
I
f
a
s
o
ld
ier
ca
n
u
p
d
ate
th
eir
lo
ca
tio
n
,
th
eir
r
a
n
k
(
R
i)
will b
e
i
n
cr
ea
s
ed
.
=
∗
(
<
)
+
(
+
1
)
∗
(
≥
)
(
7
)
T
h
e
(
8
)
d
eter
m
in
es th
e
n
ew
w
eig
h
t b
ased
o
n
th
e
r
a
n
k
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
1
7
,
No
.
2
,
J
u
n
e
20
2
6
:
1
4
1
8
-
1425
1420
=
×
(
1
−
_
)
(
8
)
wh
er
e
th
e
m
ax
im
u
m
n
u
m
b
e
r
o
f
iter
atio
n
s
is
Ma
x
_
iter
an
d
α
is
a
co
n
f
ig
u
r
ab
le
p
ar
am
eter
.
T
h
e
W
SO
alg
o
r
ith
m
'
s
s
ec
o
n
d
p
o
s
itio
n
u
p
d
ate
m
eth
o
d
en
tails
m
o
v
i
n
g
th
e
c
o
m
m
an
d
er
,
t
h
e
m
o
n
ar
c
h
,
an
d
o
n
e
s
o
ld
ie
r
ch
o
s
en
at
r
an
d
o
m
wh
ile
k
ee
p
i
n
g
th
e
in
itial we
ig
h
t a
n
d
r
a
n
k
ad
ju
s
tm
en
ts
.
(
+
1
)
=
.
.
(
C
−
(
)
)
+
(
)
+
2
.
.
(
−
(
)
)
(
9
)
W
h
er
e
X
rand
is
a
r
an
d
o
m
s
o
l
d
ier
'
s
p
o
s
itio
n
.
4.
T
H
E
W
SO
'S P
S
E
UDO
-
CO
DE
T
h
e
alg
o
r
ith
m
i
n
itializes
m
ax
iter
atio
n
s
(
M
)
,
d
im
en
s
io
n
(
D
)
,
an
d
s
o
ld
ier
s
(
N
)
,
th
en
d
e
p
lo
y
s
s
o
ld
ier
s
ch
ao
tically
v
ia
a
ch
ao
s
m
a
p
.
Fo
r
ea
ch
s
o
ld
ier
an
d
ea
ch
d
im
en
s
io
n
,
it
co
m
p
u
tes
th
e
f
itn
ess
(
attac
k
)
an
d
ev
alu
ates
o
v
er
all
p
er
f
o
r
m
an
ce
.
Du
r
in
g
t
<
M,
Yi(
t+1
)
is
u
p
d
ated
ac
co
r
d
i
n
g
to
a
r
a
n
d
o
m
th
r
esh
o
ld
ρ
1
,
th
e
n
f
itn
ess
is
r
ec
alcu
lated
,
an
d
W
i
is
u
p
d
ated
,
with
GA
cr
o
s
s
o
v
e
r
s
to
f
o
r
m
n
ew
p
o
s
itio
n
s
.
At
th
e
en
d
,
it
id
e
n
tifie
s
th
e
wea
k
est
s
o
ld
ier
an
d
o
u
tp
u
ts
th
e
k
in
g
’
s
p
o
s
itio
n
an
d
f
itn
ess
.
Fig
u
r
e
2
s
h
o
ws
th
e
W
SO
f
lo
wch
ar
t,
an
d
th
e
f
o
llo
win
g
is
th
e
p
s
eu
d
o
-
co
d
e
f
o
r
W
SO
.
Fig
u
r
e
2
.
T
h
e
W
SO a
lg
o
r
ith
m
'
s
f
lo
wch
ar
t f
o
r
th
e
PV p
ar
am
e
ter
ex
tr
ac
tio
n
u
s
e
ca
s
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
P
a
r
a
mete
r
s
o
p
timiz
a
tio
n
o
f so
la
r
P
V
ce
ll u
s
in
g
w
a
r
s
tr
a
teg
y
(
R
a
d
o
u
a
n
G
o
u
a
a
ma
r
)
1421
Alg
o
r
ith
m
1
.
Ps
eu
d
o
co
d
e
f
o
r
W
SO
Start by setting the maximum number of iterations (M), issue dimension (D), and soldier
size (N).
To evenly and randomly deploy soldiers throughout the battlefield, use
chaos mapping.
For k = 1:N
For i = 1:D
Determine each soldier's fitness (attack force).
End
End
Assess each soldier's level of fitness.
While t <
M
For 1:N
ρ1
= rand
If
ρ
>
ρ1
Utilizing (5), update Yi(t + 1)
Else use(6) to update the Yi(t + 1).
Final if condition
Determine each soldier's fitness level.
Update
Yi(t
+ 1)
Utilizing (9) update Wi
The for
loop's end
Determine which soldier is the weakest and least fit. Cross
-
mutations are added in
conjunction with
genetic algorithms to create the positions of new warriors.
t = t + 1
When the while loop ends
Show off the king's position and fitness.
5.
T
H
E
W
SO
P
ARAM
E
T
E
R
S ALGO
RI
T
H
M
AN
D
O
B
J
E
CT
I
V
E
F
UNC
T
I
O
N
T
h
e
p
r
o
p
o
s
ed
s
o
f
t
co
m
p
u
tin
g
m
eth
o
d
d
eter
m
in
es
f
iv
e
p
ar
a
m
eter
s
,
X
=
[I
0
,
I
ph
,
n,
R
sh
,
R
s
]
,
u
s
in
g
an
o
p
tim
izatio
n
alg
o
r
ith
m
th
at
m
in
im
izes
a
p
r
ed
ef
in
ed
o
b
je
ctiv
e
f
u
n
ctio
n
u
n
til
a
s
to
p
p
i
n
g
cr
iter
io
n
is
m
et.
E
s
tab
lis
h
in
g
an
ap
p
r
o
p
r
iate
o
b
jectiv
e
f
u
n
ctio
n
is
cr
u
cial
b
ef
o
r
e
o
p
tim
izatio
n
,
an
d
th
is
r
esear
ch
u
s
es
th
e
R
MSE
to
d
ef
in
e
it [
2
0
]
.
=
√
1
∑
(
,
,
)
2
=
1
(
1
0
)
W
h
er
e
x
i
d
e
n
o
tes
th
e
v
ec
to
r
o
f
u
n
k
n
o
wn
p
ar
am
eter
s
s
h
o
wn
as
=
[
0
ℎ
]
.
T
h
e
(
,
,
)
is
s
h
o
wn
as
(
1
1
)
an
d
(
1
2
)
.
(
,
,
)
=
+
ℎ
+
−
(
ℎ
−
0
×
{
[
(
+
)
]
−
1
}
)
(
1
1
)
=
√
1
∑
{
−
(
ℎ
−
0
×
{
[
(
+
)
]
−
1
}
−
+
ℎ
)
}
2
=
1
(
1
2
)
I
n
th
e
ab
o
v
e
o
b
jectiv
e
f
u
n
ctio
n
s
,
V
an
d
I
e
ar
e
th
e
e
x
p
er
im
e
n
tal
v
alu
es
o
f
th
e
v
o
ltag
e
an
d
cu
r
r
en
t
o
f
th
e
s
o
la
r
m
o
d
u
le,
r
esp
ec
tiv
ely
.
6.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
First,
b
y
f
o
r
ec
asti
n
g
th
e
u
n
k
n
o
wn
p
ar
am
eter
s
o
f
th
e
b
asic
m
o
d
el,
th
e
s
u
g
g
ested
W
SO
m
eth
o
d
'
s
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
an
d
d
e
p
en
d
ab
ilit
y
ar
e
v
alid
ated
.
T
o
v
alid
ate
th
e
p
r
o
p
o
s
ed
m
e
th
o
d
,
f
o
u
r
s
ets
o
f
ex
p
er
im
en
tal
d
ata
wer
e
s
elec
t
ed
:
a
co
m
m
er
cial
s
ilico
n
s
o
la
r
p
a
n
el
with
a
d
iam
eter
o
f
5
7
m
m
,
ca
lled
R
T
C
Fra
n
ce
,
wh
ich
o
p
er
ates
at
3
3
°C
with
a
s
o
lar
ir
r
ad
ian
ce
o
f
1
0
0
0
W
/m
²;
a
p
h
o
to
wat
t
-
PW
P
-
2
0
1
s
o
lar
m
o
d
u
le,
co
n
s
is
tin
g
o
f
3
6
p
o
ly
cr
y
s
tallin
e
s
ilico
n
ce
lls
o
p
er
atin
g
in
s
er
ies
at
4
5
°C
with
an
ir
r
a
d
ia
n
ce
o
f
1
0
0
0
W
/m
²;
an
d
a
c
o
m
m
er
cial
m
o
d
u
le
o
f
ty
p
e
STP6
-
1
2
0
/3
6
,
c
o
n
s
is
tin
g
o
f
3
6
p
o
l
y
cr
y
s
tallin
e
s
ilico
n
ce
lls
o
p
er
atin
g
in
s
er
ies
at
5
5
°C
[
2
1
]
.
T
h
e
I
(
V)
an
d
P(V
)
c
u
r
v
es
d
e
r
iv
e
d
f
r
o
m
th
e
W
SO
alg
o
r
ith
m
s
h
o
w
r
em
ar
k
a
b
le
co
n
s
is
ten
cy
with
r
ea
l d
ata,
as i
llu
s
tr
ated
in
Fig
u
r
es 3
(
a
)
a
n
d
3
(
b
)
.
A
s
tr
o
n
g
c
o
r
r
elatio
n
is
in
d
icativ
e
o
f
th
e
h
ig
h
ac
cu
r
ac
y
a
n
d
r
eliab
ilit
y
o
f
o
u
r
m
o
d
elin
g
ap
p
r
o
ac
h
.
As
s
h
o
wn
in
T
ab
le
1
,
th
e
R
MSE
v
alu
es
d
em
o
n
s
tr
ate
h
o
w
o
u
r
s
u
g
g
ested
m
et
h
o
d
p
er
f
o
r
m
s
r
elativ
ely
b
etter
with
a
R
MSE
o
f
7
.
7
2
9
8
×
10
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.
Fo
r
t
h
e
f
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at
th
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R
MSE
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o
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GAM
NU
m
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d
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2
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a
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
0
8
8
-
8
6
9
4
I
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t J Po
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lec
&
Dr
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s
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,
Vo
l.
1
7
,
No
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2
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J
u
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20
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1
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-
1425
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u
r
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3
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r
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0
1
:
(
a)
P(V
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an
d
(
b
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I
(
V)
f
ea
tu
r
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
P
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1423
Fig
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r
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5
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ate
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ates
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2
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ates
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(
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Fig
u
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e
5
.
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[
1
]
H
.
B
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.
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R
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Evaluation Warning : The document was created with Spire.PDF for Python.