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:
2023,
Volume and Issue:
13(1)
Published: Aug. 9, 2023
This
study
suggests
a
new
nature-inspired
metaheuristic
optimization
algorithm
called
the
red-tailed
hawk
(RTH).
As
predator,
has
hunting
strategy
from
detecting
prey
until
swoop
stage.
There
are
three
stages
during
process.
In
high
soaring
stage,
explores
search
space
and
determines
area
with
location.
low
moves
inside
selected
around
to
choose
best
position
for
hunt.
Then,
swings
hits
its
target
in
stooping
swooping
stages.
The
proposed
mimics
prey-hunting
method
of
solving
real-world
problems.
performance
RTH
been
evaluated
on
classes
first
class
includes
specific
kinds
problems:
22
standard
benchmark
functions,
including
unimodal,
multimodal,
fixed-dimensional
multimodal
IEEE
Congress
Evolutionary
Computation
2020
(CEC2020),
CEC2022.
is
compared
eight
recent
algorithms
confirm
contribution
these
considered
Farmland
Fertility
Optimizer
(FO),
African
Vultures
Optimization
Algorithm
(AVOA),
Mountain
Gazelle
(MGO),
Gorilla
Troops
(GTO),
COOT
algorithm,
Hunger
Games
Search
(HGS),
Aquila
(AO),
Harris
Hawks
(HHO).
results
regarding
accuracy,
robustness,
convergence
speed.
second
seven
engineering
problems
that
will
be
investigate
other
published
profoundly.
Finally,
proton
exchange
membrane
fuel
cell
(PEMFC)
extraction
parameters
performed
evaluate
complex
problem.
several
papers
approve
performance.
ultimate
each
ability
provide
higher
most
cases.
For
class,
mostly
got
optimal
solutions
functions
faster
provided
better
third
when
resolving
real
word
or
extracting
PEMFC
parameters.
Energy Strategy Reviews,
Journal Year:
2024,
Volume and Issue:
53, P. 101409 - 101409
Published: May 1, 2024
The
synergy
between
deep
learning
and
meta-heuristic
algorithms
presents
a
promising
avenue
for
tackling
the
complexities
of
energy-related
modeling
forecasting
tasks.
While
excels
in
capturing
intricate
patterns
data,
it
may
falter
achieving
optimality
due
to
nonlinear
nature
energy
data.
Conversely,
offer
optimization
capabilities
but
suffer
from
computational
burdens,
especially
with
high-dimensional
This
paper
provides
comprehensive
review
spanning
2018
2023,
examining
integration
within
frameworks
applications.
We
analyze
state-of-the-art
techniques,
innovations,
recent
advancements,
identifying
open
research
challenges.
Additionally,
we
propose
novel
framework
that
seamlessly
merges
into
paradigms,
aiming
enhance
performance
efficiency
addressing
problems.
contributions
include:
1.
Overview
advancements
MHs,
DL,
integration.
2.
Coverage
trends
2023.
3.
Introduction
Alpha
metric
evaluation.
4.
Innovative
harmonizing
MHs
DL
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 94094 - 94115
Published: Jan. 1, 2023
The
Arithmetic
Optimization
Algorithm
(AOA)
is
a
recently
proposed
metaheuristic
algorithm
that
has
been
shown
to
perform
well
in
several
benchmark
tests.
AOA
uses
the
main
arithmetic
operators'
distribution
behavior,
such
as
multiplication,
division,
subtraction,
and
addition.
This
paper
proposes
binary
version
of
(BAOA)
tackle
feature
selection
problem
classification.
algorithm's
search
space
converted
from
continuous
one
using
sigmoid
transfer
function
meet
nature
task.
classifier
method
known
wrapper-based
approach
K-Nearest
Neighbors
(KNN),
find
best
possible
solutions.
study
18
datasets
University
California,
Irvine
(UCI)
repository
evaluate
suggested
performance.
results
demonstrate
BAOA
outperformed
Binary
Dragonfly
(BDF),
Particle
Swarm
(BPSO),
Genetic
(BGA),
Cat
(BCAT)
when
various
performance
metrics
were
used,
including
classification
accuracy,
selected
features
worst
optimum
fitness
values.
