Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Март 22, 2024
Abstract
Feature
selection
is
a
critical
component
of
machine
learning
and
data
mining
to
remove
redundant
irrelevant
features
from
dataset.
The
Chimp
Optimization
Algorithm
(CHoA)
widely
applicable
various
optimization
problems
due
its
low
number
parameters
fast
convergence
rate.
However,
CHoA
has
weak
exploration
capability
tends
fall
into
local
optimal
solutions
in
solving
the
feature
process,
leading
ineffective
removal
features.
To
solve
this
problem,
paper
proposes
Enhanced
Hierarchy
for
adaptive
lens
imaging
(ALI-CHoASH)
searching
classification
subset
Specifically,
enhance
exploitation
CHoA,
we
designed
chimp
social
hierarchy.
We
employed
novel
class
factor
label
situation
each
chimp,
enabling
effective
modelling
relationships
among
individuals.
Then,
parse
chimps’
collaborative
behaviours
with
different
classes,
introduce
other
attacking
prey
autonomous
search
strategies
help
individuals
approach
solution
faster.
In
addition,
considering
poor
diversity
groups
late
iteration,
propose
an
back-learning
strategy
avoid
algorithm
falling
optimum.
Finally,
validate
improvement
ALI-CHoASH
capabilities
using
several
high-dimensional
datasets.
also
compare
eight
state-of-the-art
methods
accuracy,
size,
computation
time
demonstrate
superiority.
Spectrum of Mechanical Engineering and Operational Research.,
Год журнала:
2024,
Номер
1(1), С. 215 - 226
Опубликована: Сен. 1, 2024
Multi-Criteria
Decision
Analysis
(MCDA)
addresses
complex
decision-making
problems
across
various
fields
such
as
logistics,
management,
medicine,
and
sustainability.
MCDA
tools
provide
a
structured
approach
to
evaluating
decisions
with
multiple
conflicting
criteria,
assisting
decision-makers
in
navigating
intricate
scenarios.
Engaging
experts
is
crucial
for
identifying
multi-criteria
models
due
the
diverse
aspects
of
problems.
Techniques
pairwise
comparisons
criterion
weight
assignment
are
commonly
used
incorporate
expert
knowledge
into
decision
models.
Criterion
allows
indicate
importance
each
criterion;
however,
issues
can
arise
if
model
parameters
lost
or
become
unavailable.
To
mitigate
these
issues,
techniques
like
entropy
standard
deviation
determine
weights
without
direct
input.
In
this
context,
Stochastic
Identification
Weights
(SITW)
method
utilizes
existing
assessment
samples
re-identify
obtain
that
replicate
rankings
reference
model.
This
study
compares
information-based
methods
(Entropy,
STD)
SITW
re-identifying
TRI
medical
function
benchmark.
The
effectiveness
evaluated
using
Spearman's
weighted
correlation
coefficient
scenarios
alternative
numbers.
Results
provides
more
significant
results
than
other
by
leveraging
previously
alternatives.
Future
research
could
explore
broader
approaches
uncertainty
ensure
comprehensive
support
contexts.
Structural Concrete,
Год журнала:
2024,
Номер
unknown
Опубликована: Май 19, 2024
Abstract
This
paper
focuses
on
the
applicability
of
CatBoost
models
constructed
using
various
optimization
techniques
for
improved
forecasting
compressive
strength
ultra‐high‐performance
concrete
(UHPC).
Phasor
particle
swarm
(PPSO),
dwarf
mongoose
(DMO),
and
atom
search
(ASO),
which
have
been
very
popular
recently,
are
preferred
as
algorithms.
A
comprehensive
reliable
data
set
is
used
to
develop
models,
include
785
test
results
with
15
input
features.
The
performance
(PPSO‐CatBoost,
DMO‐CatBoost,
ASO‐CatBoost)
optimized
different
algorithms
thoroughly
assessed
by
means
statistical
metrics
error
analysis
determine
model
best
capability,
this
compared
obtained
from
previous
studies.
In
addition,
Shapley
additive
exPlanations
(SHAP)
ensure
interpretability
overcome
“black
box”
problem
machine
learning
(ML)
models.
demonstrate
that
all
outstandingly
forecast
UHPC.
Among
these
DMO‐CatBoost
stands
out
other
in
metrics,
such
high
coefficient
determination
(
R
2
)
values,
low
root
mean
squared
(RMSE),
absolute
percentage
(MAPE),
(MAE)
along
a
smaller
ratio.
words,
RMSE,
,
MAPE,
MAE
values
training
3.67,
0.993,
0.019,
2.35,
respectively,
whereas
those
6.15,
0.978,
0.038,
4.51.
Additionally,
ranking
optimize
hyperparameters
follows:
DMO
>
PPSO
ASO.
On
hand,
SHAP
showed
age,
fiber
dosage,
cement
dosage
significantly
influence
These
findings
can
guide
structural
engineers
design
UHPC,
thus
assisting
them
developing
strategies
improve
properties
material.
Finally,
based
developed
work,
graphical
user
interface
has
easily
UHPC
practical
applications
without
additional
tools
or
software.
Alexandria Engineering Journal,
Год журнала:
2023,
Номер
87, С. 148 - 163
Опубликована: Дек. 22, 2023
Vegetation
evolution
(VEGE)
is
a
newly
proposed
meta-heuristic
algorithm
(MA)
with
excellent
exploitation
but
relatively
weak
exploration
capacity.
We
thus
focus
on
further
balancing
the
and
of
VEGE
well
to
improve
overall
optimization
performance.
This
paper
proposes
an
improved
Q-learning
based
VEGE,
we
design
archive
provide
variety
search
strategies,
each
contains
four
efficient
easy-implemented
strategies.
In
addition,
online
Q-Learning,
as
ε-greedy
scheme,
are
employed
decision-maker
role
learn
knowledge
from
past
process
determine
strategy
for
individual
automatically
intelligently.
numerical
experiments,
compare
our
QVEGE
eight
state-of-the-art
MAs
including
original
CEC2020
benchmark
functions,
twelve
engineering
problems,
wireless
sensor
networks
(WSN)
coverage
problems.
Experimental
statistical
results
confirm
that
demonstrates
significant
enhancements
stands
strong
competitor
among
existing
algorithms.
The
source
code
publicly
available
at
https://github.com/RuiZhong961230/QVEGE.
