As
the
installed
capacity
of
renewable
energy
sources
explosively
increases,
current
deterministic
reserve
standards
are
no
longer
suitable
for
high
proportion
integration
and
need
safe
stable
operation
power
grid.
It
is
urgent
to
improve
practical
level
ultra-short-term
operating
reserves.
This
article
proposes
an
assessment
method
requirements
system
based
on
QRXGboost-RSA,
which
combines
XGboost
model
with
quantile
theory
adopts
RSA
optimize
model,
assessing
future
periods
at
different
points.
Finally,
simulated
verification
conducted
a
dataset
from
province
in
Northwest
China,
results
indicate
that
proposed
can
effectively
assess
requirement.
The
Artificial
hummingbird
algorithm
which
is
given
by
Mirjalili
in
2022
a
swarm-based
meta-heuristic
technique.
This
technique
shows
better
results
than
many
classic
techniques
when
compared
and
tested
the
Wilcoxon
test
AHA
has
found
applications
different
real-life
problems
like
energy
sector.
In
this
work,
binary
version
of
code
for
various
optimization
algorithms
provided
researchers
serves
as
inspiration
developing
artificial
humming
to
solve
discrete
problems.
evaluated
on
benchmark
functions
are
with
original
at
dimensions.
The
study
presents
a
novel
framework
integrating
feature
selection
(FS)
and
machine
learning
(ML)
techniques
to
forecast
inland
national
energy
consumption
(EC)
in
the
United
Kingdom
across
all
sources.
This
innovative
strategically
combines
three
FS
approaches
with
five
interpretable
ML
models
using
Shapley
Additive
Explanations
(SHAP),
dual
goal
of
enhancing
accuracy
transparency
EC
predictions.
By
meticulously
selecting
most
pertinent
features
from
diverse
features—including
meteorological
conditions,
socioeconomic
parameters,
historical
patterns
different
primary
fuels—the
proposed
enhances
robustness
forecasting
model.
is
achieved
through
benchmarking
approaches:
ensemble
filter,
wrapper,
hybrid
filter-wrapper.
In
addition,
we
introduce
filter
FS,
synthesizing
outcomes
multiple
base
methods
make
well-informed
decisions
about
retention.
Experimental
results
underscore
efficacy
both
wrapper
filter-wrapper
models,
ensuring
process
remains
comprehensible
while
utilizing
manageable
number
(four
eight).
experimental
indicate
that
subsets
are
usually
selected
for
each
combined
approach
not
only
demonstrates
framework's
capability
provide
accurate
forecasts
but
also
establishes
it
as
valuable
tool
policymakers
analysts.
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 9, 2024
Abstract
Algorithms
serve
as
the
backbone
of
computer
science,
permeating
diverse
fields
with
their
indispensable
applications.
The
Knapsack
Problems
(KP),
an
optimization
puzzle,
revolves
around
judicious
selection
items
characterized
by
values
and
weights
to
maximize
utility
within
constraints
a
limited-capacity
container.
This
study
introduces
pioneering
mathematical
approach
inspired
nuanced
behaviors
natural
gazelles.
Delving
deep
into
intricate
hierarchical
social
dynamics
inherent
in
gazelle
behavior,
Binary
Mountain
Gazelle
Optimizer
(BinMGO)
emerges
standout.
Empowered
six
transfer
functions,
spanning
from
S-shaped
X-shaped
varieties,
BinMGO
is
finely
tuned
address
0–1
KP.
After
evaluating
variants,
most
effective
one
identified.
Acknowledging
limitations
posed
undergoes
additional
refinement,
resulting
developing
Enhanced
(EBinMGO),
employing
multiple
mutation
techniques
tailored
specifically
for
addressing
Thorough
experimentation
conducted
on
KP
datasets
highlights
EBinMGO's
superiority
over
renowned
swarm
intelligence
algorithms
such
Ali
Baba
Forty
Thieves
(AFT),
Prairie
Dog
Optimization
Algorithm
(PDO),
Pelican
(POA),
Snake
(SO).
consistent
proficiency
demonstrated
EBinMGO
delivering
superior
outcomes
across
all
experimental
results
positions
promising
solution
binary
challenges.
Furthermore,
this
provides
valuable
insights
mutation-based
algorithms,
offering
potential
avenues
complex
problems
nature's
intricacies.
IEEE Transactions on Cybernetics,
Journal Year:
2024,
Volume and Issue:
54(12), P. 7877 - 7890
Published: July 3, 2024
Hybridization
plays
a
prominent
role
in
bolstering
the
performance
of
optimization
algorithms
(OAs),
yet
designing
efficient
hybrid
OAs
tailored
to
intricate
problems
persists
as
formidable
task.
This
article
introduces
novel
top-down
methodology
for
automated
design
OAs,
treating
algorithm
meta-optimization
problem.
A
general
template
collaboration-based
is
developed,
integrating
multitude
hybridization
strategies
first
time.
Besides,
mathematical
model
built
formulate
problem
design.
To
address
challenge,
an
improved
multifactorial
evolutionary
proposed
automatically
metaheuristics
multitasking
environment
given
instances
with
diverse
features.
verify
effectiveness
methodology,
it
applied
CEC2017
benchmark
functions
and
binary
knapsack
Numerical
results
have
demonstrated
feasibility
both
continuous
combinatorial
benchmarks.
Algorithms,
Journal Year:
2024,
Volume and Issue:
17(11), P. 478 - 478
Published: Oct. 25, 2024
The
list-based
threshold
accepting
(LBTA)
algorithm
is
a
sophisticated
local
search
method
that
utilizes
list
to
streamline
the
parameter
tuning
process
in
traditional
(TA)
algorithm.
This
paper
proposes
an
enhanced
version
of
LBTA
specifically
tailored
for
solving
0–1
knapsack
problem
(0–1
KP).
To
maintain
dynamic
list,
feasible
updating
strategy
designed
accept
adaptive
modifications
during
process.
In
addition,
incorporates
improved
bit-flip
operator
generate
neighboring
solution
with
controlled
level
disturbance,
thereby
fostering
exploration
within
space.
Each
trial
produced
by
this
undergoes
repair
phase
using
hybrid
greedy
both
density-based
and
value-based
add
facilitate
optimization.
algorithm’s
performance
was
evaluated
against
several
state-of-the-art
metaheuristic
approaches
on
series
large-scale
instances.
simulation
results
demonstrate
outperforms
or
competitive
other
leading
metaheuristics
field.
As
the
installed
capacity
of
renewable
energy
sources
explosively
increases,
current
deterministic
reserve
standards
are
no
longer
suitable
for
high
proportion
integration
and
need
safe
stable
operation
power
grid.
It
is
urgent
to
improve
practical
level
ultra-short-term
operating
reserves.
This
article
proposes
an
assessment
method
requirements
system
based
on
QRXGboost-RSA,
which
combines
XGboost
model
with
quantile
theory
adopts
RSA
optimize
model,
assessing
future
periods
at
different
points.
Finally,
simulated
verification
conducted
a
dataset
from
province
in
Northwest
China,
results
indicate
that
proposed
can
effectively
assess
requirement.
