CAAI Transactions on Intelligence Technology,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 24, 2024
Abstract
The
Runge–Kutta
optimiser
(RUN)
algorithm,
renowned
for
its
powerful
optimisation
capabilities,
faces
challenges
in
dealing
with
increasing
complexity
real‐world
problems.
Specifically,
it
shows
deficiencies
terms
of
limited
local
exploration
capabilities
and
less
precise
solutions.
Therefore,
this
research
aims
to
integrate
the
topological
search
(TS)
mechanism
gradient
rule
(GSR)
into
framework
RUN,
introducing
an
enhanced
algorithm
called
TGRUN
improve
performance
original
algorithm.
TS
employs
a
circular
scheme
conduct
thorough
solution
regions
surrounding
each
solution,
enabling
careful
examination
valuable
areas
enhancing
algorithm’s
effectiveness
exploration.
To
prevent
from
becoming
trapped
optima,
GSR
also
integrates
descent
principles
direct
wider
investigation
global
space.
This
study
conducted
serious
experiments
on
IEEE
CEC2017
comprehensive
benchmark
function
assess
TGRUN.
Additionally,
evaluation
includes
engineering
design
feature
selection
problems
serving
as
additional
test
assessing
validation
outcomes
indicate
significant
improvement
accuracy
Mathematics,
Год журнала:
2024,
Номер
12(18), С. 2870 - 2870
Опубликована: Сен. 14, 2024
The
snow
ablation
optimizer
(SAO)
is
a
meta-heuristic
technique
used
to
seek
the
best
solution
for
sophisticated
problems.
In
response
defects
in
SAO
algorithm,
which
has
poor
search
efficiency
and
prone
getting
trapped
local
optima,
this
article
suggests
multi-strategy
improved
(MISAO)
optimizer.
It
employed
unmanned
aerial
vehicle
(UAV)
path
planning
issue.
To
begin
with,
tent
chaos
elite
reverse
learning
initialization
strategies
are
merged
extend
diversity
of
population;
secondly,
greedy
selection
method
deployed
retain
superior
alternative
solutions
upcoming
iteration;
then,
Harris
hawk
(HHO)
strategy
introduced
enhance
exploitation
capability,
prevents
trapping
partial
ideals;
finally,
red-tailed
(RTH)
adopted
perform
global
exploration,
which,
enhances
optimization
capability.
comprehensively
evaluate
MISAO’s
battery
digital
investigations
executed
using
23
test
functions,
results
comparative
analysis
show
that
suggested
algorithm
high
solving
accuracy
convergence
velocity.
Finally,
effectiveness
feasibility
MISAO
demonstrated
UAV
project.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Июль 8, 2024
Abstract
As
countries
attach
importance
to
environmental
protection,
clean
energy
has
become
a
hot
topic.
Among
them,
solar
energy,
as
one
of
the
efficient
and
easily
accessible
sources,
received
widespread
attention.
An
essential
component
in
converting
into
electricity
are
cells.
However,
major
optimization
difficulty
remains
precisely
effectively
calculating
parameters
photovoltaic
(PV)
models.
In
this
regard,
study
introduces
an
improved
rime
algorithm
(RIME),
namely
ERINMRIME,
which
integrates
Nelder-Mead
simplex
(NMs)
with
environment
random
interaction
(ERI)
strategy.
later
phases
ERI
strategy
serves
complementary
mechanism
for
augmenting
solution
space
exploration
ability
agent.
By
facilitating
external
interactions,
method
improves
algorithm’s
efficacy
conducting
global
search
by
keeping
it
from
becoming
stuck
local
optima.
Moreover,
incorporating
NMs,
ERINMRIME
enhances
its
do
searches,
leading
exploration.
To
evaluate
ERINMRIME's
performance
on
PV
models,
conducted
experiments
four
different
models:
single
diode
model
(SDM),
double
(DDM),
three-diode
(TDM),
module
model.
The
experimental
results
show
that
reduces
root
mean
square
error
SDM,
DDM,
TDM,
models
46.23%,
59.32%,
61.49%,
23.95%,
respectively,
compared
original
RIME.
Furthermore,
nine
classical
algorithms.
is
remarkable
competitor.
Ultimately,
evaluated
across
three
distinct
commercial
while
considering
varying
irradiation
temperature
conditions.
superior
existing
similar
algorithms
Therefore,
great
potential
identifying
recognizing
unknown
MethodsX,
Год журнала:
2024,
Номер
13, С. 102964 - 102964
Опубликована: Сен. 19, 2024
This
paper
presents
a
methodological
approach
to
solving
the
fuzzy
capacitated
logistic
distribution
center
problem,
with
focus
on
optimal
selection
of
centers
meet
demands
multiple
plants.
The
are
characterized
by
fixed
costs
and
capacities,
while
plant
modeled
using
triangular
membership
functions.
problem
is
mathematically
formulated
converting
into
crisp
values,
providing
structured
framework
for
addressing
uncertainty
in
planning.
To
support
future
research
facilitate
comparative
analysis,
20
benchmark
problems
were
generated,
filling
gap
existing
literature.
Three
distinct
artificial
bee
colony
algorithm
variants
hybridized
heuristic:
one
best
solution
per
iteration,
another
incorporating
chaotic
mapping
adaptive
procedures,
third
employing
convergence
diversity
archives.
An
experimental
design
based
Taguchi's
orthogonal
arrays
was
employed
optimizing
parameters,
ensuring
systematic
exploration
space.
developed
methods
offer
comprehensive
toolkit
complex,
uncertain
distribution,
code
provided
reproducibility.
Key
contributions
include:•Development
model
capacities
under
demands.•Generation
advance
domain.•Integration
heuristic
three
ABC
variants,
each
contributing
unique
insights.
