Applied Soft Computing,
Journal Year:
2023,
Volume and Issue:
139, P. 110269 - 110269
Published: March 29, 2023
The
integration
of
multiple
genes
to
maximize
protein
expression
levels
represents
an
important
challenge
in
synthetic
biology.
This
task
relies
on
the
definition
protein-coding
sequences,
which
must
be
as
different
possible
avoid
information
loss.
Proteins
can
encoded
ways,
using
synonymous
codons
that
translate
into
same
amino
acid.
Some
are
better
suited
host
than
others,
thus
being
preferable
use
most
fitting
ones.
However,
adopting
only
highly
adapted
would
lead
very
similar
coding
sequences.
An
additional
criterion
is
given
by
fact
designed
sequences
contain
a
suitable
guanine–cytosine
(GC)
ratio
accordance
with
characteristics
organism.
Therefore,
this
biological
requires
simultaneous
optimization
several,
conflicting
objectives.
work
proposes
novel
multi-objective
approach
for
encoding,
tackles
problem
according
new
formulation
based
three
objective
functions:
codon
adaptation
index,
Hamming
distance
between
and
GC
content.
Our
extends
recent
Butterfly
Optimization
Algorithm
contexts,
integrating
problem-specific
operators
boost
solution
quality
covering
aspects
required
accurate
encoding.
Two
key
structures,
taboo
list
best
list,
defined
conduct
improved
searches
attending
potential
improvements
each
population
promote.
Experiments
conducted
nine
real-world
proteins
reveal
attainment
relevant
solutions
from
evaluation
perspectives,
showing
significant
over
other
single
methods
literature.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: April 26, 2025
Abstract
In
our
modern
societies,
the
wireless
sensor
network
(WSN)
is
categorized
as
a
smart
motivated
technology
that
can
be
utilized
in
many
work
environments
and
activities
to
enhance
daily
life.
However,
several
challenging
concerns
have
been
assigned
WSN.
The
clustering
process
main
complex
concern
still
an
open
problem
To
support
efficient
process,
two
crucial
requirements
must
considered,
energy
management
lifetime
extension,
especially
development
of
large-scale
primary
objective
this
article
introduce
new
meta-heuristic
algorithm,
denoted
hybrid
gazelle
optimization
reptile
search
algorithm
(HGORSA),
which
optimizes
cluster
head
selection
WSNs.
proposed
mathematical
models
for
exploration
exploitation
phases
traditional
(GOA)
are
enhanced
by
integrating
hunting
operator,
reduction
function,
predator
cumulative
effect
operators
from
RSA.
These
modifications
improve
balance
between
diversification
intensification
processes,
effectively
addressing
key
mentioned
above.
At
same
time,
they
also
positively
impact
overall
performance
evaluation
Various
simulation
scenarios
designed
evaluate
HGORSA
different
configurations.
First,
experiment
was
conducted
with
300
nodes
(SNs).
experimental
results
then
analyzed
assess
effectiveness
under
conditions
against
six
state-of-the-art
algorithms.
Based
on
outputs,
demonstrated
superior
compared
particle
swarm
optimization,
grey
Wolf
optimizer,
sperm
chernobyl
disaster
algorithm.
Specifically,
achieved
percentage
improvements
terms
stability
period
(37.3%,
49.6%,
46.8%,
55.3%,
19.1%,
34.4%,
respectively),
consumption
(10.8%,
10.5%,
9.6%,
8.6%,
8.3%,
3.5%,
(44.5%,
40.8%,
23.8%,
16.8%,
9.3%,
7.2%,
number
dead
(30.3%,
29.7%,
28.9%,
24.3%,
18%,
11.5%,
throughput
(36.4%,
43.9%,
34.2%,
25%,
20%,
14.4%,
respectively).
Moreover,
supplementary
test
efficiency
dense
sparse
networks,
where
SNs
set
at
50
500.
evaluated
based
five
standard
aforementioned
metrics.
Furthermore,
robustness
validated
using
statistical
measures,
including
deviation
(Std),
average
(Avg),
worst
best
values,
box
plots
fitness
function
across
20
independent
runs.
results,
outperformed
other
comparative
meta-heuristics.
Applied Soft Computing,
Journal Year:
2023,
Volume and Issue:
139, P. 110269 - 110269
Published: March 29, 2023
The
integration
of
multiple
genes
to
maximize
protein
expression
levels
represents
an
important
challenge
in
synthetic
biology.
This
task
relies
on
the
definition
protein-coding
sequences,
which
must
be
as
different
possible
avoid
information
loss.
Proteins
can
encoded
ways,
using
synonymous
codons
that
translate
into
same
amino
acid.
Some
are
better
suited
host
than
others,
thus
being
preferable
use
most
fitting
ones.
However,
adopting
only
highly
adapted
would
lead
very
similar
coding
sequences.
An
additional
criterion
is
given
by
fact
designed
sequences
contain
a
suitable
guanine–cytosine
(GC)
ratio
accordance
with
characteristics
organism.
Therefore,
this
biological
requires
simultaneous
optimization
several,
conflicting
objectives.
work
proposes
novel
multi-objective
approach
for
encoding,
tackles
problem
according
new
formulation
based
three
objective
functions:
codon
adaptation
index,
Hamming
distance
between
and
GC
content.
Our
extends
recent
Butterfly
Optimization
Algorithm
contexts,
integrating
problem-specific
operators
boost
solution
quality
covering
aspects
required
accurate
encoding.
Two
key
structures,
taboo
list
best
list,
defined
conduct
improved
searches
attending
potential
improvements
each
population
promote.
Experiments
conducted
nine
real-world
proteins
reveal
attainment
relevant
solutions
from
evaluation
perspectives,
showing
significant
over
other
single
methods
literature.
