Indonesian
Journal
of
Electrical
Engineering
and
Computer
Science
V
ol.
8,
No
.
1,
October
2017,
pp
.
27
35
DOI:
10.11591/ijeecs
.v8.i1.pp27-35
27
Optimisation
of
Bioc
hemical
Systems
Pr
oduction
using
Hybrid
of
Ne
wton
method,
Diff
erential
Ev
olution
Algorithm
and
Cooperative
Coe
v
olution
Algorithm
Mohd
Arfian
Ismail
*1
,
Vitaliy
Mezhuy
e
v
1
,
K
ohbalan
Moor
th
y
1
,
Shahreen
Kasim
2
,
and
Ashraf
Osman
Ibrahim
3,4
1
F
aculty
of
Computer
Systems
and
Softw
are
Engineer
ing,
Univ
ersiti
Mala
ysia
P
aha
ng,
P
ahang,
Mala
ysia
2
Soft
Computing
and
Data
Mining
Centre
,
F
aculty
of
Computer
Science
and
In
f
or
mation
T
echnology
,
Univ
ersiti
T
un
Hussein
Onn,
Johor
,
Mala
ysia
3
F
aculty
of
computer
Science
and
Inf
or
mation
T
echnology
,
Alzaiem
Alazhar
i
Univ
ersit
y
,
Khar
toum
Nor
th
13311,
Sudan
4
Ar
ab
Open
Univ
ersity
,
Khar
toum,
Sudan
*Corresponding
author
,
e-mail:
arfian@ump
.edu.m
y
Abstract
This
paper
present
a
h
ybr
id
meth
od
of
Ne
wton
method,
Diff
erential
Ev
olution
Algor
ithm
(DE)
and
Cooper
ativ
e
Coe
v
olution
Algor
ithm
(CCA).
The
proposed
method
is
used
to
solv
e
the
optimisation
prob
lem
in
optimise
the
production
of
biochemical
systems
.
The
prob
lems
are
maximising
the
biochemical
systems
pro-
duction
and
sim
ultaneously
minimising
the
total
amount
of
chemical
reaction
concentr
ation
in
v
olv
es
.
Besides
that,
the
siz
e
of
biochemical
systems
also
contr
ib
uted
to
the
prob
lem
in
optimising
the
biochemical
systems
production.
In
the
proposed
method,
the
Ne
wton
method
is
used
in
dealing
biochemical
system,
DE
f
or
opti-
misation
process
while
CCA
is
used
to
increase
the
perf
or
mance
of
DE.
In
or
der
to
e
v
aluate
the
perf
or
mance
of
the
proposed
method,
the
proposed
method
is
tested
on
tw
o
benchmar
k
bioche
mical
systems
.
Then,
the
result
that
obtained
b
y
the
proposed
method
is
compare
with
other
w
or
ks
and
the
finding
sho
ws
that
the
proposed
method
perf
or
ms
w
ell
compare
to
the
other
w
or
ks
.
K
e
yw
or
ds:
Ne
wton
method,
Diff
erential
Ev
olution
Algor
ithm,
Cooper
ativ
e
Coe
v
olut
ioan
Algor
ithm,
Biochem-
ical
systems
,
Computational
Intelligence
Cop
yright
c
2017
Institute
of
Ad
v
anced
Engineering
and
Science
.
All
rights
rese
r
ved.
1.
Intr
oduction
Biomass
is
a
good
alter
nativ
e
to
produce
the
biofuel.
This
is
because
the
biomass
is
a
plant-based
resource
that
can
be
used
to
replace
the
limited
biofuel.
No
w
ada
ys
,
the
demand
of
biomass
is
increase
where
it
leads
to
competition
of
land
and
plant
[1,
2,
3].
Recently
,
man
y
re-
searchers
ha
v
e
f
ocus
on
manipulating
the
microorganism
activity
in
order
to
produce
the
biomass
r
ather
than
really
on
increasing
the
land
a
nd
plant.
This
is
because
manipulating
the
microorgan-
ism
is
f
ar
cheaper
and
reduce
time
r
ather
than
increase
the
land
or
plant.
But,
the
biomass
that
e
xtr
acted
from
manipulating
the
microorganism
activity
has
a
limitation
where
the
production
is
lo
w
[4,
5].
Due
to
that,
man
y
researcher
ha
v
e
f
ocus
on
optimisation
the
production
of
biomass
.
One
w
a
y
to
impro
v
e
the
biomass
production
is
the
optimisation
of
biochemical
systems
production
b
y
fine-tuning
the
reactions
v
alue
in
biochemical
systems
.
The
optimisation
of
the
production
in
biochemical
systems
can
be
perf
or
med
because
the
biochemical
system
can
be
represented
b
y
a
nonlinear
equations
system.
In
the
nonlinear
equa-
tions
system,
each
v
ar
iab
le
is
used
to
represent
each
reactions
of
biochemical
systems
.
The
process
of
fine-tuning
the
reactions
v
alue
can
be
perf
or
med
b
y
change
the
v
ar
iab
les
v
alue
.
Fine-
tuning
process
of
the
v
ar
iab
les
in
nonlinear
equat
ions
system
becomes
a
hard
task
if
in
v
olv
es
a
large
biochemical
systems
.
This
is
because
a
large
biochemical
systems
contains
with
man
y
reac-
tions
and
in
v
olv
es
man
y
inter
action
betw
een
reaction.
In
order
to
o
v
ercome
this
situation,
this
paper
Receiv
ed
Ma
y
6,
2017;
Re
vised
September
2,
2017;
Accepted
September
15,
2017
Evaluation Warning : The document was created with Spire.PDF for Python.
28
ISSN:
2502-4752
present
an
automated
method
to
fine-tuning
the
v
ar
iab
les
in
nonlinear
equations
system.
The
pro-
posed
method
h
ybr
id
the
Ne
wton
method,
Diff
erential
Ev
olution
Algor
ithm
(DE)
and
Cooper
ativ
e
Coe
v
olution
Algor
ithm
(CCA).
In
optimisation
of
biochemical
systems
production,
the
biochemical
systems
can
be
mod-
elled
b
y
mathematical
model,
which
is
gener
alised
mass
action
(GMA)
model.
Dur
ing
the
opti-
misation,
there
are
se
v
er
al
constr
aints
in
v
olv
e
which
are
steady
state
condition
and
reaction
con-
centr
ation
constr
ain
t.
The
steady
stat
e
condition
mak
e
all
the
equations
in
GMA
model
equal
to
0
where
it
mak
e
the
optimisation
process
become
the
process
of
solving
a
nonlinear
equations
system.
There
are
v
ar
ious
methods
that
can
be
used
in
solving
a
nonlinear
equations
system
such
as
Ne
wton
method,
Secant
method
and
Bisection
method.
In
this
study
,
Ne
wton
method
is
used
because
Ne
wton
method
is
f
ast
in
solving
the
system
[6],
simple
to
used
[7,
8]
and
v
er
y
widely
used
in
solving
nonlinear
equations
system
[9,
10,
11].
F
or
fine-tuning
the
reactions
v
alue
in
biochemical
systems
,
an
optimisation
method
is
needed.
The
reason
of
fine-tuning
is
to
disco
v
er
the
suitab
le
v
alue
that
produce
the
high
pro-
duction
of
biochemical
systems
.
The
fine-tuning
process
become
complicated
when
in
v
olv
es
a
comple
x
biochemical
system
where
contains
with
man
y
reactions
and
in
v
olv
es
man
y
inter
action
betw
een
them.
Because
of
that,
an
optimisation
method
is
need.
There
are
v
ar
ious
method
can
be
applied
such
as
genetic
algor
ithm
(GA),
DE,
and
P
ar
ticle
Sw
ar
m
Optimisation
(PSO)
algor
ithm.
This
study
used
DE
because
DE
off
er
se
v
er
al
adv
antages
such
as
DE
in
v
olv
es
f
e
w
par
ameters
[12,
13]
and
DE
is
more
rob
ust
on
se
v
er
al
prob
lems
when
compare
to
other
[14].
In
the
optimisation
of
biochemical
systems
production,
tw
o
f
actors
that
need
to
be
con-
sidered
which
are
the
production
and
the
total
of
chemical
reaction
concentr
ations
in
v
olv
es
.
In
addition,
a
large
of
biochemical
systems
th
at
content
with
man
y
reactions
and
inter
action
betw
een
them
also
contr
ib
ute
to
the
difficulty
in
optimisation
process
.
Because
of
these
f
actors
,
this
mak
e
the
representation
of
the
solution
become
comple
x.
This
mak
e
the
optimisation
proses
become
hard
and
complicated.
In
order
to
o
v
ercome
these
issues
,
this
study
use
CCA
in
order
to
simplify
the
representation
of
the
solution
b
y
dividing
the
complete
into
m
ultiple
sub-solutions
.
In
this
paper
,
the
h
ybr
id
of
Ne
wton
method,
GA
and
CCA
is
proposed
and
discuss
in
detail.
The
aim
of
the
proposed
method
is
to
solv
e
the
prob
lems
in
optimisation
of
biochemical
systems
production
which
are
to
impro
v
e
the
biochemical
systems
production
and
at
the
same
time
reduce
the
total
of
chemical
reaction
concentr
ations
in
v
olv
es
.
In
the
proposed
method,
the
function
of
Ne
wton
method
is
to
solv
e
the
nonlinear
equations
system,
DE
is
used
in
optimisation
process
where
DE
is
used
to
fine-tuning
process
while
CCA
is
utilised
to
impro
v
e
the
perf
or
mance
of
DE.
In
the
f
ollo
wing
section,
the
e
xplanation
of
the
proposed
method
is
discussed
in
detail.
Then,
the
model
and
e
xper
imental
data
is
descr
ibe
in
detail
where
tw
o
benchmar
k
biochemical
systems
are
used
namely
the
Saccharom
yces
cere
visiae
(
S
.cere
visiae
)
pathw
a
y
and
the
Escher
ichia
Coli
(
E.coli
)
pathw
a
y
.
After
that,
the
e
xper
imental
result
and
discussion
is
presented
bef
ore
this
paper
w
as
conclude
in
conclusion.
2.
A
Hybrid
Method
of
Ne
wton
Method,
Diff
erential
Ev
olution
Algorithm
and
Cooperative
Coe
v
olution
Algorithm
This
section
is
about
the
discussion
of
the
proposed
method.
The
proposed
method
h
ybr
id
Ne
wton
method,
DE
and
CCA.
In
the
proposed
method,
Ne
wton
method
is
utilised
to
deal
with
nonlinear
equations
system,
DE
is
used
in
optimisation
process
and
CCA
is
embodied
into
DE
in
order
to
impro
v
e
the
perf
or
mance
of
DE
b
y
simplifies
the
chromosome
representation.
Figure
1
sho
ws
the
proposed
method
in
flo
wchar
t
f
or
m.
The
detail
steps
in
the
proposed
method
are
as
f
ollo
ws:
Step
1:
Gener
ate
the
initial
solution.
In
the
first
step
,
the
first
gener
ation
of
m
solution
is
gen-
er
ated
separ
ately
in
n
sub-population
(the
n
umber
of
sub-population
is
equal
to
the
n
umber
of
v
ar
iab
les
that
need
to
be
tuned).
The
v
ar
iab
le
(in
non
linear
equations
system)
is
represented
b
y
sub-chromosome
.
The
sub-chromoso
me
is
in
binar
y
f
or
mat.
The
sub-chromosome
is
gener
ated
r
andomly
and
in
a
specific
f
or
mat
(depends
on
the
v
alue
of
chemical
reaction
concentr
ation).
Step
2
:
F
or
m
the
complete
chromosome
.
The
complete
solution
is
f
or
m
in
this
step
b
y
combine
all
IJEECS
V
ol.
8,
No
.
1,
October
2017
:
27
35
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEECS
ISSN:
2502-4752
29
Figure
1.
The
flo
wchar
of
the
proposed
method
sub-chromosomes
from
all
sub-populations
.
The
sub-chromosome
is
selected
based
on
their
fit-
ness
v
alue
where
the
sub-chromosome
that
has
lo
w
est
fitness
v
alue
is
select
ed
and
then
combine
with
other
sub-chromosome
from
each
sub-population.
This
is
because
the
selection
process
is
in-
tended
to
minimise
the
total
amount
of
chemical
reaction
concentr
ation
in
v
olv
es
.
Figure
2
depicted
the
gener
ation
of
sub-chromosome
until
the
f
or
mation
of
complete
chromosome
.
Step
3:
Ev
aluate
the
complete
chromosome
.
In
this
step
,
the
complete
chromosome
is
decoded
into
v
ar
iab
les
f
or
m.
At
this
stage
,
the
Ne
wton
method
is
used
in
solving
t
he
nonlinear
equations
system.
Besides
that,
tw
o
ter
mination
conditions
are
applied
which
are;
the
maxim
um
n
umber
of
gener
ation
is
reach
and
all
the
chemical
reaction
concentr
ation
v
alue
is
in
their
r
ange
.
The
process
mo
v
e
f
orw
ard
to
Step
6
if
these
conditions
are
meet,
otherwise
the
process
enter
the
ne
xt
step
.
Step
4:
Decompose
the
complete
chromosome
.
In
this
step
,
the
complete
chromosome
is
decom-
posed
into
m
ultiple
sub-chromosomes
.
After
that,
all
sub-chromosomes
w
ent
bac
k
into
their
o
wn
sub-population
f
or
reproduction
process
.
Step
5:
Pr
oduce
ne
w
gener
ation.
This
step
is
intended
to
impro
v
e
the
solution
b
y
producing
the
ne
xt
gener
ation
of
the
solution.
This
step
happens
in
all
sub-population.
The
m
utation
and
crosso
v
er
process
are
applied
on
all
sub-chromosome
.
Step
6:
Retur
n
the
best
solution.
This
is
the
final
step
.
In
this
step
,
the
best
so
lution
is
giv
en.
3.
Model
and
Experimental
Data
In
order
to
test
the
perf
or
mance
of
the
proposed
method,
tw
o
benchmar
k
biochemical
sys-
tems
are
used
which
are
the
optimisation
of
the
ethanol
production
in
S
.
cere
visiae
pathw
a
y
and
the
optimization
of
the
tr
p
biosynthesis
in
E.
Coli
.
A
J
a
v
a
prog
r
am
based
on
J
AMA
v
ersion
1.0.3
and
jMetal
[15]
are
used.
The
J
AM
A
prog
r
am
is
used
in
dealing
with
nonlinear
equations
system
while
Optimisation
of
biochemical
systems
production
using
h
ybr
id
of
Ne
wton
method,
...
(M.A.
Ismail)
Evaluation Warning : The document was created with Spire.PDF for Python.
30
ISSN:
2502-4752
Figure
2.
The
process
of
f
or
mation
the
complete
chromosome
jMetal
f
or
optimisation
process
.
The
J
AMA
can
be
obtained
from
http://math.nist.go
v/ja
v
an
umer
ics/jama/
and
jMetal
can
be
do
wnloaded
from
http://jmetal.sourcef
orge
.net.
The
detail
descr
iption
of
tw
o
benchmar
k
biochemical
systems
are
descr
ibe
in
the
ne
xt
sub
section.
3.1.
Optimisation
of
the
ethanol
pr
oduction
in
Sacc
har
om
yces
cere
visiae
pathwa
y
The
proposed
method
is
used
to
optimise
the
ethanol
production
in
S
.cere
visiae
pathw
a
y
.
The
detail
descr
iption
of
this
pathw
a
y
can
be
f
oun
d
in
[16].
In
this
pathw
a
y
,
the
nonlinear
equations
system
can
be
represented
as
f
ollo
ws:
V
in
V
H
K
=
0
V
H
K
V
P
F
K
V
C
ar
b
=
0
V
P
F
K
V
GAP
D
0
:
5
V
Gr
o
=
0
(1)
2
V
GAP
D
V
P
K
=
0
2
V
GAP
D
+
V
P
K
V
H
K
V
C
ar
b
V
P
F
K
V
AT
P
ase
=
0
where
at
steady
state
conditions
,
these
chemical
reaction
concentr
ations
(denoted
b
y
V
)
ha
v
e
the
f
ollo
wing
v
alue:
V
in
=
0
:
8122
X
0
:
2344
2
Y
1
V
H
K
=
2
:
8632
X
0
:
7464
1
X
0
:
0243
5
Y
2
V
P
F
K
=
0
:
5232
X
0
:
7318
2
X
0
:
3941
5
Y
3
V
C
ar
b
=
8
:
904
10
4
X
8
:
6107
2
Y
7
(2)
V
GAP
D
=
7
:
6092
10
2
X
0
:
6159
3
X
0
:
1308
5
Y
4
V
Gr
o
=
9
:
272
10
2
X
0
:
05
3
X
0
:
533
4
X
0
:
0822
5
Y
8
V
P
K
=
9
:
471
10
2
X
0
:
05
3
X
0
:
533
4
X
0
:
0822
5
Y
5
V
AT
P
a
se
=
X
5
Y
6
IJEECS
V
ol.
8,
No
.
1,
October
2017
:
27
35
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEECS
ISSN:
2502-4752
31
In
this
biochemical
system,
the
ethanol
production
is
giv
en
b
y
V
P
K
and
it
became
the
fitness
function
of
complete
chromosome
.
This
lead
to
the
impro
ving
the
production
as
f
ollo
ws:
max
F
1
(
v
)
=
V
P
K
(3)
F
or
the
total
of
chemical
reaction
concentr
ations
in
v
olv
es
,
it
can
be
f
or
m
ulated
as
f
ollo
w:
min
F
2
=
5
X
j
=1
X
j
+
6
X
j
=6
Y
j
(4)
where
the
r
ange
of
X
is
set
betw
een
0.2
to
1.2
and
Y
in
the
r
ange
of
0
to
50
[17,
18].
3.2.
Optimisation
of
the
tr
yptophan
biosynthesis
in
Esc
heric
hia
Coli
pathwa
y
In
this
pathw
a
y
,
the
proposed
method
is
used
to
optimise
the
tr
p
production.
Xiu
et
al.
has
e
xplained
in
detail
of
this
pathw
a
y[19].
F
or
this
pathw
a
y
,
the
nonlinear
equations
system
can
be
f
or
m
ulated
as
f
ollo
ws:
V
11
V
12
=
0
V
21
V
22
=
0
(5)
V
31
V
32
V
33
V
34
=
0
All
reaction
concentr
ation
(denoted
b
y
V
)
has
the
f
ollo
wing
v
alues
at
steady
state
condition:
V
11
=
0
:
6403
X
5
:
87
10
4
3
X
0
:
8332
5
V
12
=
1
:
0233
X
1
X
0
:
0035
4
X
0
:
9965
11
V
21
=
X
1
V
22
=
1
:
4854
X
2
X
0
:
1349
4
X
0
:
8651
12
(6)
V
31
=
0
:
5534
X
2
X
0
:
5573
3
X
0
:
5573
6
V
32
=
X
3
X
4
V
33
=
0
:
9942
X
7
:
0426
10
4
3
X
7
V
34
=
0
:
8925
X
3
:
5
10
6
3
X
0
:
9760
4
X
8
X
0
:
0240
9
X
3
:
5
10
6
10
The
tr
p
production
is
giv
en
b
y
reaction
V
34
thus
it
become
the
fitness
function
of
the
com-
plete
chromosome
.
This
lead
to
the
impro
ving
the
production
as
f
ollo
ws:
max
F
1
=
V
34
(7)
F
or
the
total
of
chemical
reaction
concentr
ations
in
v
olv
es
,
it
can
be
f
or
m
ulated
as
f
ollo
w:
min
F
2
=
6
X
j
=1
X
j
+
X
8
(8)
where
the
r
ange
of
X
1
to
X
3
is
betw
een
0.8
to
1.2,
X
4
betw
een
0
to
0.00624,
X
5
betw
een
4
to
10,
X
6
betw
een
500
to
5000
and
betw
een
X
8
0
to
1000
[17,
18].
4.
Experimental
results
and
discussions
In
producing
the
best
result,
se
v
er
al
e
xper
iments
are
perf
or
med.
T
ab
le
1
list
the
DE
pa-
r
ameters
setting
used.
F
or
CCA,
the
n
umber
of
sub-populations
depend
on
the
v
ar
iab
les
in
non-
linear
equations
system
the
need
to
be
tuned.
F
or
the
S
.cere
visiae
pathw
a
y
,
the
n
umber
of
sub-
populations
is
11
while
f
or
E.coli
pathw
a
y
,
the
n
umber
of
sub-population
s
is
7.
F
or
the
Ne
wton
Optimisation
of
biochemical
systems
production
using
h
ybr
id
of
Ne
wton
method,
...
(M.A.
Ismail)
Evaluation Warning : The document was created with Spire.PDF for Python.
32
ISSN:
2502-4752
P
ar
ameter
S
.cere
visiae
pathw
a
y
E.coli
pathw
a
y
Mutation
(Scaling
f
actor)
0.8
0.7
Crosso
v
er
0.2
0.2
T
ab
le
1.
The
DE
par
ameters
method,
fix
ed
par
ameter
used
f
or
both
pathw
a
y;
the
n
umber
of
iter
ation
is
100
and
the
toler
ance
v
alue
is
10
6
.
In
S
.cere
visiae
pathw
a
y
,
the
best
result
obtained
b
y
the
proposed
method
is
52.7269
in
maximising
the
ethanol
production
while
295.2405
in
minimising
the
total
of
chemical
concentr
ation
in
v
olv
es
.
The
detail
result,
a
v
er
age
result
and
compar
ison
with
other
methods
are
listed
in
T
ab
le
2.
F
rom
T
ab
le
2,
it
can
be
obser
v
ed
that
the
perf
or
mance
of
the
proposed
method
is
outperf
or
m
the
result
from
other
w
or
ks
in
maximising
the
ethanol
production
and
at
the
same
time
minimising
the
total
amount
of
chemical
reaction
concentr
ation
in
v
olv
es
.
P
ar
ameter
This
w
or
k
W
or
k
b
y
[20]
W
or
k
b
y
[18]
W
or
k
b
y
[21]
X
1
1.113
1.14
1.102
1.11
X
2
1.053
1.05
1.046
1.03
X
3
1.127
1.15
1.141
1.13
X
4
1.164
1.17
1.171
1.18
X
5
0.92
1.
12
1.113
51.14
Y
1
49.972
49.97
50
49.99
Y
2
49.810
44.77
45.953
45.83
Y
3
49.90
49.89
50
49.92
Y
4
47.333
47.26
47.772
47.97
Y
5
48.062
48
48.366
48.30
Y
8
49.792
49.75
50
49.79
F
1
52.727
52.084
52.512
52.57
F
2
295.241
295.28
297.664
297.384
T
ab
le
2.
The
detail
result
obtained
b
y
the
proposed
method
in
S
.cere
visiae
pathw
a
y
Meanwhile
,
the
best
result
produce
b
y
the
proposed
method
in
E.coli
pathw
a
y
is
3.9988
in
maximising
the
tr
p
production
and
6015.5871
in
minimising
the
total
of
chemical
concentr
ation
in
v
olv
es
.
The
detail
result,
a
v
er
age
result
and
compar
ison
with
other
methods
are
listed
in
T
ab
le
3.
Same
obser
v
ation
with
S
.cere
visiae
pathw
a
y
,
the
perf
or
mance
of
the
proposed
method
also
perf
or
m
better
when
it
compare
to
other
w
or
ks
.
P
ar
ameter
This
w
or
k
W
or
k
b
y
[22]
W
or
k
b
y
[23]
W
or
k
b
y
[18]
W
or
k
b
y
[21]
X
1
1.191
1.19
1.2
1.2
1.11
X
2
1.119
1.15
1.15
1.12
1.114
X
3
0.8
0.8
0.8
0.8
0.8
X
4
0.0054
0.0041
0.004
0.0054
0.0054
X
5
4.037
4
4
4.011
4.75
X
6
5000
5000
5000
5000
5000
X
8
1000
1000
1000
1000
1000
F
1
3.999
3.06
3.06
3.95
3.98
F
2
6015.5871
6016.38
6016.57
6016.57
6016.22
T
ab
le
3.
The
detail
result
obtained
b
y
the
proposed
method
in
E.coli
pathw
a
y
Besides
that,
the
compar
ison
betw
een
m
ulti
sub-population
that
used
in
this
study
with
single
population
(dont
use
CCA).
The
pur
pose
of
CCA
is
to
enhance
the
perf
or
mance
of
DE
in
minimising
the
total
amount
of
chemical
reaction
concentr
at
ion
in
v
olv
es
.
Se
v
er
al
e
xper
iments
are
IJEECS
V
ol.
8,
No
.
1,
October
2017
:
27
35
Evaluation Warning : The document was created with Spire.PDF for Python.
IJEECS
ISSN:
2502-4752
33
conducted
using
par
ameters
setting
in
T
ab
le
1.
Figure
3
and
Figure
4
depicted
the
bar
g
r
aph
of
the
compar
ison
betw
een
m
ulti
sub-population
with
single
population
in
S
.cere
visiae
pathw
a
y
and
E.coli
pathw
a
y
.
F
rom
that
figures
,
it
can
be
seen
clear
ly
that
all
the
results
of
m
ulti
sub-population
are
lo
w
er
compare
to
the
results
obtained
b
y
single
population.
It
can
be
conclu
ded
that,
the
CCA
ab
le
to
impro
v
e
the
perf
or
mance
of
DE
in
minimising
the
total
amount
of
chemical
reaction
concentr
ation
in
v
olv
es
.
Figure
3.
The
compar
ison
of
m
ulti
population
and
single
population
in
S
.cere
visiae
pathw
a
y
Figure
4.
The
compar
ison
of
m
ulti
population
and
single
population
in
E.coli
pathw
a
y
In
order
to
sho
w
the
consistency
in
apply
CCA,
the
proposed
method
is
compared
with
the
method
that
not
use
CCA
(only
use
Ne
wton
method
and
DE).
About
100
indep
endent
e
xper
iments
are
perf
or
med.
Figure
5
and
Figure
6
sho
w
the
compar
ison
in
bo
x
pl
ot
f
or
m.
Figure
5
sho
w
the
ethanol
production
in
S
.cere
visiae
pathw
a
y
while
Figure
6
sho
w
the
tr
p
production
in
E.coli
pathw
a
y
.
F
rom
the
figures
,
the
result
produce
b
y
the
proposed
method
are
not
too
wide
compare
to
the
result
that
not
use
CCA.
F
rom
t
his
obser
v
ation,
it
can
be
e
xplained
that
the
propose
method
ab
le
to
produce
a
consistent
result
if
the
e
xper
iment
r
un
se
v
er
al
times
.
Figure
5.
The
bo
xplot
of
the
ethanol
production
Optimisation
of
biochemical
systems
production
using
h
ybr
id
of
Ne
wton
method,
...
(M.A.
Ismail)
Evaluation Warning : The document was created with Spire.PDF for Python.
34
ISSN:
2502-4752
Figure
6.
The
bo
xplot
of
the
tr
p
production
5.
Conc
lusion
This
paper
has
proposed
a
h
ybr
id
method
of
Ne
wton
method,
DE
and
CCA.
The
proposed
method
is
proposed
to
o
v
ercome
the
prob
lems
in
optimisation
of
biochemical
systems
where
the
prob
lems
are
to
maximise
the
biochemical
systems
production
and
sim
ultaneously
minimise
the
total
amount
of
chemical
reaction
concentr
ation
in
v
olv
es
.
The
proposed
method
w
or
ks
b
y
vie
w
the
biochemical
systems
as
nonlinear
equation
system.
Firstly
,
the
Ne
wton
method
is
used
to
solv
e
the
nonlinear
equations
system.
Then,
DE
is
used
in
optimisation
process
.
The
perf
or
mance
of
DE
is
drop
when
applied
on
alrge
and
comple
x
biochemical
systems
and
CCA
is
utilised
to
impro
v
e
the
perf
or
mance
of
DE.
Th
e
proposed
method
is
applied
on
benchmar
k
biochemical
systems
and
the
e
xper
imental
result
sho
w
that
the
perf
or
mance
is
outperf
or
m
the
other
w
or
ks
.
Ac
kno
wledg
ement
Special
thanks
and
appreciation
to
the
editor
and
anon
ymous
re
vie
w
ers
that
re
vie
w
ed
this
paper
.
The
author
also
w
ould
thanks
to
the
sponsor
from
RDU
Gr
ant
V
ot
No
.
RDU1603115
f
or
m
Univ
ersiti
Mala
ysia
P
ahang.
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ol.
8,
No
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October
2017
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27
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Evaluation Warning : The document was created with Spire.PDF for Python.
IJEECS
ISSN:
2502-4752
35
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Abr
aham,
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.
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Q.
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”
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A.
Ismail,
S
.
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is
,
M.
S
.
Mohamad,
and
A.
Abdullah,
“A
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wton
cooper
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genetic
al-
gor
ithm
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f
or
in
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optimization
of
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pathw
a
y
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”
PloS
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