NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 25, 2024
Image
classification
is
a
critical
area
of
research
with
widespread
applications
across
various
disciplines,
including
computer
vision,
pattern
recognition,
and
artificial
intelligence.
Despite
the
advancements
in
Convolutional
Neural
Networks
(CNNs),
which
have
revolutionized
field
by
providing
powerful
tools
for
image
classification,
many
studies
encountered
challenges
achieving
optimal
performance.
These
often
arise
from
complex
nature
CNN
architectures
multitude
hyperparameters
that
require
fine-tuning.
Among
models,
AlexNet
has
been
widely
recognized
its
contributions
to
deep
learning,
yet
there
remains
significant
potential
improvement
through
optimization
hyperparameters.
In
this
study,
WF-AlexNET
designed
enhance
performance
architecture
optimizing
first
convolutional
layer
using
Equilibrium
Optimization
(EO)
algorithm.
The
EO
algorithm,
was
employed
fine-tune
filter
size,
number,
stride,
padding
parameters,
are
crucial
effective
feature
extraction.
proposed
method
rigorously
tested
on
multi-class
weather
dataset
evaluate
accuracy
robustness.
Experimental
results
demonstrate
significantly
outperforms
standard
model,
10.5%
increase
mean
validation
6.51%
test
accuracy.
Furthermore,
approach
compared
against
other
prominent
architectures,
VGG-16,
GoogleNet,
ShuffleNet,
MobileNet-V2,
VGG-19.
consistently
exhibited
superior
multiple
metrics,
F1-score
maximum
accuracy,
highlighting
efficacy
addressing
associated
hyperparameter
CNNs.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 11, 2025
Abstract
The
Parrot
Optimizer
(PO)
has
recently
emerged
as
a
powerful
algorithm
for
single-objective
optimization,
known
its
strong
global
search
capabilities.
This
study
extends
PO
into
the
Multi-Objective
(MOPO),
tailored
multi-objective
optimization
(MOO)
problems.
MOPO
integrates
an
outward
archive
to
preserve
Pareto
optimal
solutions,
inspired
by
behavior
of
Pyrrhura
Molinae
parrots.
Its
performance
is
validated
on
Congress
Evolutionary
Computation
2020
(CEC’2020)
benchmark
suite.
Additionally,
extensive
testing
four
constrained
engineering
design
challenges
and
eight
popular
confined
unconstrained
test
cases
proves
MOPO’s
superiority.
Moreover,
real-world
helical
coil
springs
automotive
applications
conducted
depict
reliability
proposed
in
solving
practical
Comparative
analysis
was
performed
with
seven
published,
state-of-the-art
algorithms
chosen
their
proven
effectiveness
representation
current
research
landscape-Improved
Manta-Ray
Foraging
Optimization
(IMOMRFO),
Gorilla
Troops
(MOGTO),
Grey
Wolf
(MOGWO),
Whale
Algorithm
(MOWOA),
Slime
Mold
(MOSMA),
Particle
Swarm
(MOPSO),
Non-Dominated
Sorting
Genetic
II
(NSGA-II).
results
indicate
that
consistently
outperforms
these
across
several
key
metrics,
including
Set
Proximity
(PSP),
Inverted
Generational
Distance
Decision
Space
(IGDX),
Hypervolume
(HV),
(GD),
spacing,
maximum
spread,
confirming
potential
robust
method
addressing
complex
MOO
Intelligent Decision Technologies,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 18
Published: Aug. 30, 2024
Inspired
by
the
fundamentals
of
biological
evolution,
bio-inspired
algorithms
are
becoming
increasingly
popular
for
developing
robust
optimization
techniques.
These
metaheuristic
algorithms,
unlike
gradient
descent
methods,
computationally
more
efficient
and
excel
in
handling
higher
order
multi-dimensional
non-linear.
OBJECTIVES:
To
understand
hybrid
Bio-inspired
domain
Medical
Imaging
its
challenges
feature
selection
METHOD:
The
primary
research
was
conducted
using
three
major
indexing
database
Scopus,
Web
Science
Google
Scholar.
RESULT:
included
198
articles,
after
removing
103
duplicates,
95
articles
remained
as
per
criteria.
Finally
41
were
selected
study.
CONCLUSION:
We
recommend
that
further
area
based
field
diagnostic
imaging
clustering.
Additionally,
there
is
a
need
to
investigate
use
Deep
Learning
models
integrating
include
strengths
each
enhances
overall
model.
