PLoS ONE,
Journal Year:
2024,
Volume and Issue:
19(11), P. e0310840 - e0310840
Published: Nov. 1, 2024
Over
the
last
25
years,
a
considerable
proliferation
of
software
metrics
and
plethora
tools
have
emerged
to
extract
them.
While
this
is
indeed
positive
concerning
previous
situations
limited
data,
it
still
leads
significant
problem
arising
both
from
theoretical
practical
standpoint.
From
perspective,
several
are
likely
result
in
collinearity,
overfitting,
etc.
such
set
difficult
manage
companies,
especially
small
ones,
may
feel
overwhelmed
unable
select
viable
subset
Still,
so
far
has
not
been
fully
understood
what
suitable
properly
projects
products.
In
paper,
we
attempt
address
issue.
We
focus
on
case
programs
written
Java
consider
classes
methods.
use
Sammon
error
as
measure
similarity
metrics.
Utilizing
Particle
Swarm
Optimization
Genetic
Algorithm,
adapted
method
for
identification
that
could
solve
mentioned
problem.
Furthermore,
experiment
with
our
approach
800
coming
GitHub
validate
results
200
projects.
With
proposed
got
optimal
subsets
engineering
These
gave
us
low
values
at
more
than
70%
class
levels
validation
dataset.
Systems Science & Control Engineering,
Journal Year:
2024,
Volume and Issue:
12(1)
Published: Aug. 1, 2024
Bald
Eagle
Search
(BES)
is
a
recent
and
highly
successful
swarm-based
metaheuristic
algorithm
inspired
by
the
hunting
strategy
of
bald
eagles
in
capturing
prey.
With
its
remarkable
ability
to
balance
global
local
searches
during
optimization,
BES
effectively
addresses
various
optimization
challenges
across
diverse
domains,
yielding
nearly
optimal
results.
This
paper
offers
comprehensive
review
research
on
BES.
Beginning
with
an
introduction
BES's
natural
inspiration
conceptual
framework,
it
explores
modifications,
hybridizations,
applications
domains.
Then,
critical
evaluation
performance
provided,
offering
update
effectiveness
compared
recently
published
algorithms.
Furthermore,
presents
meta-analysis
developments
outlines
potential
future
directions.
As
swarm-inspired
algorithms
become
increasingly
important
tackling
complex
problems,
this
study
valuable
resource
for
researchers
aiming
understand
algorithms,
mainly
focusing
comprehensively.
It
investigates
evolution,
exploring
solving
intricate
fields.
Biomimetics,
Journal Year:
2025,
Volume and Issue:
10(1), P. 53 - 53
Published: Jan. 14, 2025
Optimization
algorithms
play
a
crucial
role
in
solving
complex
problems
across
various
fields,
including
global
optimization
and
feature
selection
(FS).
This
paper
presents
the
enhanced
polar
lights
with
cryptobiosis
differential
evolution
(CPLODE),
novel
improvement
upon
original
(PLO)
algorithm.
CPLODE
integrates
mechanism
(DE)
operators
to
enhance
PLO's
search
capabilities.
The
particle
collision
strategy
is
replaced
DE's
mutation
crossover
operators,
enabling
more
effective
exploration
using
dynamic
rate
improve
convergence.
Furthermore,
records
reuses
historically
successful
solutions,
thereby
improving
greedy
process.
experimental
results
on
29
CEC
2017
benchmark
functions
demonstrate
CPLODE's
superior
performance
compared
eight
classical
algorithms,
higher
average
ranks
faster
Moreover,
achieved
competitive
ten
real-world
datasets,
outperforming
several
well-known
binary
metaheuristic
classification
accuracy
reduction.
These
highlight
effectiveness
for
both
selection.
