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.
Alexandria Engineering Journal,
Journal Year:
2023,
Volume and Issue:
82, P. 358 - 376
Published: Oct. 14, 2023
The
0–1
Knapsack
problem
is
a
non-deterministic
polynomial-time-hard
combinatorial
optimization
that
cannot
be
solved
in
reasonable
time
using
traditional
methods.
Therefore,
researchers
have
turned
to
metaheuristic
algorithms
for
their
ability
solve
several
problems
amount
of
time.
This
paper
adapts
the
Kepler
algorithm
eight
V-shaped
and
S-shaped
transfer
functions
create
binary
variant
called
BKOA
solving
problem.
Several
experiments
were
conducted
compare
efficacy
competing
optimizers
when
20
well-known
knapsack
instances
with
dimensions
ranging
from
4
75.
experimental
results
demonstrate
superiority
this
over
other
algorithms,
except
genetic
algorithm,
which
marginally
superior.
To
further
improve
it
combined
an
enhanced
improvement
strategy
new
hybrid
variant.
variant,
termed
HBKOA,
has
superior
exploration
exploitation
capabilities
make
better
than
all
performance
metrics
considered.
also
integrated
optimizers,
show
manta
ray
foraging
optimization,
equilibrium
optimizer
are
competitive
small
medium-dimensional
higher
dimensions.
This
paper
presents
a
new
binary
optimization
technique
for
solving
the
0–1
knapsack
problem.
algorithm
is
based
on
converting
continuous
search
space
of
recently
proposed
quadratic
interpolation
(QIO)
into
discrete
using
various
V-shaped
and
S-shaped
transfer
functions;
this
abbreviated
as
BQIO.
To
further
improve
its
performance,
it
effectively
integrated
with
uniform
crossover
operator
swap
to
explore
more
effectively.
improved
variant
called
BIQIO.
Both
BQIO
BIQIO
are
assessed
20
well-known
instances
compared
four
published
metaheuristic
algorithms
reveal
their
effectiveness.
The
comparison
among
three
performance
metrics:
mean
fitness
value,
Friedman
rank
computational
cost.
first
two
metrics
used
observe
accuracy
results,
while
last
metric
employed
show
efficiency
each
algorithm.
results
superiority
over
classical
rival
optimizers.
Journal of King Saud University - Computer and Information Sciences,
Journal Year:
2023,
Volume and Issue:
35(8), P. 101704 - 101704
Published: Aug. 12, 2023
The
irrelevant
and
repeated
features
in
high-dimensional
datasets
can
negatively
affect
the
final
performance
accuracy
of
classification-based
models.
Therefore,
feature
selection
(FS)
techniques
be
used
to
determine
most
optimal
relevant
features.
In
this
paper,
we
fuse
a
new
enhanced
model
from
Lemurs
Optimization
(LO)
algorithm,
called
Enhanced
(ELO).
We
combine
Opposition
Based
Learning
(OBL)
Local
Search
Algorithm
(LSA)
address
exploration
exploitation
challenges,
respectively.
Our
proposed
ELO
algorithm
incorporates
U-shaped
Sigmoid
transfer
functions
during
position
update
step,
leading
improved
convergence.
These
deployments
based
on
are
ELO-U
ELO-S
algorithms,
all
three
versions
our
optimization
algorithms
(ELO,
ELO-U,
ELO-S)
has
been
evaluated
using
21
UCI
different
fields
sizes.
Moreover,
their
results
also
compared
other
competitive
algorithms.
evaluation
process
included
several
measurements
such
as
fitness
value,
an
average
selected
features,
accuracy.
Experimental
demonstrate
that
achieves
best
91.03%.
Statistical
analysis
Friedman
Wilcoxon
tests
confirms
superiority
over
competitors.
Biomimetics,
Journal Year:
2023,
Volume and Issue:
8(3), P. 305 - 305
Published: July 11, 2023
The
reptile
search
algorithm
is
an
effective
optimization
method
based
on
the
natural
laws
of
biological
world.
By
restoring
and
simulating
hunting
process
reptiles,
good
results
can
be
achieved.
However,
due
to
limitations
laws,
it
easy
fall
into
local
optima
during
exploration
phase.
Inspired
by
different
fields
organisms
with
varying
flight
heights,
this
paper
proposes
a
considering
heights.
In
phase,
introducing
altitude
abilities
two
animals,
northern
goshawk
African
vulture,
enables
reptiles
have
better
horizons,
improve
their
global
ability,
reduce
probability
falling
A
novel
dynamic
factor
(DF)
proposed
in
exploitation
phase
algorithm’s
convergence
speed
accuracy.
To
verify
effectiveness
algorithm,
test
were
compared
ten
state-of-the-art
(SOTA)
algorithms
thirty-three
famous
functions.
experimental
show
that
has
performance.
addition,
SOTA
applied
three
micromachine
practical
engineering
problems,
problem-solving
ability.
International Journal of Computational Intelligence Systems,
Journal Year:
2024,
Volume and Issue:
17(1)
Published: April 22, 2024
Abstract
Binary
optimization
problems
belong
to
the
NP-hard
class
because
their
solutions
are
hard
find
in
a
known
time.
The
traditional
techniques
could
not
be
applied
tackle
those
computational
cost
required
by
them
increases
exponentially
with
increasing
dimensions
of
problems.
Therefore,
over
last
few
years,
researchers
have
paid
attention
metaheuristic
algorithms
for
tackling
an
acceptable
But
unfortunately,
still
suffer
from
being
able
avert
local
minima,
lack
population
diversity,
and
low
convergence
speed.
As
result,
this
paper
presents
new
binary
technique
based
on
integrating
equilibrium
optimizer
(EO)
search
operator,
which
effectively
integrates
single
crossover,
uniform
mutation
flipping
swapping
operator
improve
its
exploration
exploitation
operators.
In
more
general
sense,
is
two
folds:
first
fold
borrows
single-point
crossover
accelerate
speed,
addition
avoiding
falling
into
minima
using
strategy;
second
applying
different
operators
best-so-far
solution
hope
finding
better
solution:
flip
bit
selected
randomly
given
solution,
swap
unique
positions
solution.
This
variant
called
hybrid
(BHEO)
three
common
problems:
0–1
knapsack,
feature
selection,
Merkle–Hellman
knapsack
cryptosystem
(MHKC)
investigate
effectiveness.
experimental
findings
BHEO
compared
classical
algorithm
six
other
well-established
evolutionary
swarm-based
algorithms.
From
findings,
it
concluded
that
strong
alternative
Quantatively,
reach
average
fitness
0.090737884
section
problem
difference
optimal
profits
some
used
Knapsack
2.482.
Journal of King Saud University - Computer and Information Sciences,
Journal Year:
2024,
Volume and Issue:
36(6), P. 102093 - 102093
Published: June 13, 2024
This
paper
examines
the
performance
of
three
binary
metaheuristic
algorithms
when
applied
to
two
distinct
knapsack
problems
(0–1
(KP01)
and
multidimensional
(MKP)).
These
are
based
on
classical
mantis
search
algorithm
(MSA),
quadratic
interpolation
optimization
(QIO)
method,
well-known
differential
evolution
(DE).
Because
these
were
designed
for
continuous
problems,
they
could
not
be
used
directly
solve
problems.
As
a
result,
V-shaped
S-shaped
transfer
functions
propose
variants
algorithms,
such
as
(BDE),
(BQIO),
(BMSA).
evaluated
using
various
high-dimensional
KP01
examples
compared
several
techniques
determine
their
efficacy.
To
enhance
those
combined
with
repair
operator
2
(RO2)
offer
better
hybrid
variants,
namely
HMSA,
HQIO,
HDE.
Those
medium-
large-scale
MKP
instances,
well
other
demonstrate
effectiveness.
comparison
is
conducted
metrics:
average
fitness
value,
Friedman
mean
rank,
computational
cost.
The
experimental
findings
that
HQIO
strong
alternative
solving
MKP.
In
addition,
proposed
Merkle-Hellman
Knapsack
Cryptosystem
resource
allocation
problem
in
adaptive
multimedia
systems
(AMS)
illustrate
effectiveness
optimize
real
applications.
handling
knapsack-based
