A multi-scale analysis method with multi-feature selection for house prices forecasting
Applied Soft Computing,
Год журнала:
2025,
Номер
unknown, С. 112779 - 112779
Опубликована: Янв. 1, 2025
Язык: Английский
An improved grey wolf optimization algorithm based on scale-free network topology
Heliyon,
Год журнала:
2024,
Номер
10(16), С. e35958 - e35958
Опубликована: Авг. 1, 2024
Язык: Английский
A new enhanced grey wolf optimizer to improve geospatially subsurface analyses
Modeling Earth Systems and Environment,
Год журнала:
2025,
Номер
11(2)
Опубликована: Янв. 31, 2025
Язык: Английский
Knowledge-guided classification and regression surrogates co-assisted multi-objective soft subspace clustering algorithm
Applied Intelligence,
Год журнала:
2025,
Номер
55(6)
Опубликована: Фев. 15, 2025
Язык: Английский
A Pareto Front searching algorithm based on reinforcement learning for constrained multiobjective optimization
Information Sciences,
Год журнала:
2025,
Номер
unknown, С. 121985 - 121985
Опубликована: Фев. 1, 2025
Язык: Английский
A novel multi-objective dung beetle optimizer for Multi-UAV cooperative path planning
Heliyon,
Год журнала:
2024,
Номер
10(17), С. e37286 - e37286
Опубликована: Сен. 1, 2024
Path
planning
for
multiple
unmanned
aerial
vehicles
(UAVs)
is
crucial
in
collaborative
operations
and
commonly
regarded
as
a
complicated,
multi-objective
optimization
problem.
However,
traditional
approaches
have
difficulty
balancing
convergence
diversity,
well
effectively
handling
constraints.
In
this
study,
directional
evolutionary
non-dominated
sorting
dung
beetle
optimizer
with
adaptive
stochastic
ranking
(DENSDBO-ASR)
developed
to
address
these
issues
multi-UAV
path
planning.
Two
objectives
are
initially
formulated:
the
first
one
represents
total
cost
of
length
altitude,
while
second
threat
time.
Additionally,
an
improved
introduced,
which
integrates
strategy
including
mutation
crossover,
thereby
accelerating
enhancing
global
search
capability.
Furthermore,
mechanism
proposed
successfully
handle
different
constraints
by
dynamically
adjusting
comparison
probability.
The
effectiveness
superiority
DENSDBO-ASR
demonstrated
constrained
problem
functions
(CF)
test,
Wilcoxon
rank
sum
Friedman
test.
Finally,
three
sets
simulated
tests
carried
out,
each
numbers
UAVs.
most
challenging
scenario,
identifies
feasible
paths
average
values
two
objective
low
637.26
0.
comparative
results
demonstrate
that
outperforms
other
five
algorithms
terms
accuracy
population
making
it
exceptional
approach
challenges.
Язык: Английский
An improved reinforcement learning-based differential evolution algorithm for combined economic and emission dispatch problems
Engineering Applications of Artificial Intelligence,
Год журнала:
2024,
Номер
140, С. 109709 - 109709
Опубликована: Ноя. 29, 2024
Язык: Английский
Fractional order swarming intelligence for multi-objective load dispatch with photovoltaic integration
Engineering Applications of Artificial Intelligence,
Год журнала:
2024,
Номер
137, С. 109073 - 109073
Опубликована: Авг. 8, 2024
Язык: Английский
Semi-supervised prediction method for time series based on Monte Carlo and time fusion feature attention
Applied Soft Computing,
Год журнала:
2024,
Номер
unknown, С. 112283 - 112283
Опубликована: Сен. 1, 2024
Язык: Английский
Research on Optimized Allocation of University English Hybrid Teaching Resources under Cloud Computing Environment
Applied Mathematics and Nonlinear Sciences,
Год журнала:
2024,
Номер
9(1)
Опубликована: Янв. 1, 2024
Abstract
Driven
by
the
informationization
of
education,
a
large
number
educational
resources
have
been
developed,
which
usually
exist
in
form
data,
and
situation
“data
explosion”
has
challenged
storage
retrieval
capabilities
hybrid
teaching
resource
platform
universities.
In
this
paper,
we
construct
for
university
English
cloud
computing
environment
introduce
improved
bat
algorithm
using
dynamic
inertia
weights
Gaussian
perturbation
terms
into
to
optimize
process
allocation.
The
experimental
results
benchmark
performance
test
show
that
no
abnormalities,
such
as
program
execution
failure
processing
files,
indicating
stability
is
good.
analysis
its
application
effect
shows
indicators
allocation
are
optimized
after
experiment,
variability
among
college
classes
decreases.
learning
effectiveness
students
assisted
significantly
better
than
control
(P=0.001<0.05).
This
paper
lays
foundation
improving
informatization
provides
reference
basis
students’
effectiveness.
Язык: Английский