IEEE Access,
Год журнала:
2020,
Номер
8, С. 26304 - 26315
Опубликована: Янв. 1, 2020
Multilevel-thresholding
is
an
efficient
method
used
in
image
segmentation.
This
paper
presents
a
hybrid
meta-heuristic
approach
for
multi-level
thresholding
segmentation
by
integrating
both
the
artificial
bee
colony
(ABC)
algorithm
and
sine-cosine
(SCA).
The
proposed
algorithm,
called
ABCSCA,
applied
to
segment
images
it
utilizes
Otsu's
function
as
objective
function.
ABCSCA
uses
ABC
optimize
threshold
reduce
search
region.
Thereafter,
SCA
output
of
determine
global
optimal
solution,
which
represents
values.
To
evaluate
performance
set
experimental
series
performed
using
nineteen
images.
In
first
series,
assessed
at
low
levels
compared
with
traditional
methods.
Moreover,
second
aims
high
six
algorithms
addition
ABC.
Besides,
evaluated
fuzzy
entropy.
results
demonstrate
effectiveness
showed
that
outperforms
other
terms
measures,
such
Peak
Signal-to-Noise
Ratio
(PSNR)
Structural
Similarity
Index
(SSIM).
Artificial Intelligence Review,
Год журнала:
2024,
Номер
57(8)
Опубликована: Июль 4, 2024
Abstract
Rapid
industrialization
has
fueled
the
need
for
effective
optimization
solutions,
which
led
to
widespread
use
of
meta-heuristic
algorithms.
Among
repertoire
over
600,
300
new
methodologies
have
been
developed
in
last
ten
years.
This
increase
highlights
a
sophisticated
grasp
these
novel
methods.
The
biological
and
natural
phenomena
inform
strategies
seen
paradigm
shift
recent
observed
trend
indicates
an
increasing
acknowledgement
effectiveness
bio-inspired
tackling
intricate
engineering
problems,
providing
solutions
that
exhibit
rapid
convergence
rates
unmatched
fitness
scores.
study
thoroughly
examines
latest
advancements
optimisation
techniques.
work
investigates
each
method’s
unique
characteristics,
properties,
operational
paradigms
determine
how
revolutionary
approaches
could
be
problem-solving
paradigms.
Additionally,
extensive
comparative
analyses
against
conventional
benchmarks,
such
as
metrics
search
history,
trajectory
plots,
functions,
are
conducted
elucidate
superiority
approaches.
Our
findings
demonstrate
potential
optimizers
provide
directions
future
research
refine
expand
upon
intriguing
methodologies.
survey
lighthouse,
guiding
scientists
towards
innovative
rooted
various
mechanisms.
Metaheuristic
optimization
algorithms
are
known
for
their
versatility
and
adaptability,
making
them
effective
tools
solving
a
wide
range
of
complex
problems.
They
don't
rely
on
specific
problem
types,
gradients,
can
explore
globally
while
handling
multi-objective
optimization.
strike
balance
between
exploration
exploitation,
contributing
to
advancements
in
However,
it's
important
note
limitations,
including
the
lack
guaranteed
global
optimum,
varying
convergence
rates,
somewhat
opaque
functioning.
In
contrast,
metaphor-based
algorithms,
intuitively
appealing,
have
faced
controversy
due
potential
oversimplification
unrealistic
expectations.
Despite
these
considerations,
metaheuristic
continue
be
widely
used
tackling
This
research
paper
aims
fundamental
components
concepts
that
underlie
focusing
use
search
references
delicate
exploitation.
Visual
representations
behavior
selected
will
also
provided.
