This
paper
presents
two
simple
yet
powerful
optimization
algorithms
named
Best-Mean-Random
(BMR)
and
Best-Worst-Randam
(BWR)
to
handle
both
constrained
unconstrained
problems.
These
are
free
of
metaphors
algorithm-specific
parameters.
The
BMR
algorithm
is
based
on
the
best,
mean,
random
solutions
population
generated
for
solving
a
given
problem;
BWR
worst,
solutions.
performances
proposed
investigated
12
engineering
problems
results
compared
with
very
recent
(in
some
cases
more
than
30
algorithms).
Furthermore,
computational
experiments
conducted
standard
benchmark
including
5
recently
developed
having
distinct
characteristics.
proved
better
competitiveness
superiority
algorithms.
research
community
may
gain
an
advantage
by
adapting
these
solve
various
real-life
across
scientific
disciplines.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 2, 2025
Electric
furnaces
play
an
important
role
in
many
industrial
processes
where
precise
temperature
control
is
essential
to
ensure
production
efficiency
and
product
quality.
Traditional
proportional-integral-derivative
(PID)
controllers
their
modified
versions
are
commonly
used
maintain
stability
by
reacting
quickly
deviations.
In
this
study,
the
real
PID
plus
second-order
derivative
(RPIDD2)
controller
introduced
for
first
time
applications,
which
a
novel
alternative
that
has
not
yet
been
investigated
literature.
To
optimal
performance,
parameters
of
RPIDD2
optimized
using
metaheuristic
algorithms,
including
flood
optimization
algorithm
(FLA),
reptile
search
(RSA),
particle
swarm
(PSO)
differential
evolution
(DE).
A
new
approach
proposed
combines
quadratic
interpolation
(QIO)
with
controller,
taking
advantage
fast
convergence,
low
computational
cost
high
accuracy
QIO.
Comparative
analyses
between
QIO-RPIDD2,
FLA-RPIDD2,
RSA-RPIDD2,
PSO-RPIDD2
DE-RPIDD2
performed
evaluating
performance
metrics
such
as
transient
frequency
response.
The
results
show
QIO-RPIDD2
achieves
superior
adapts
different
reference
temperatures
performs
excellently
on
key
indicators.
These
make
promising
solution
contribute
more
efficient
adaptive
techniques.
Biomimetics,
Год журнала:
2025,
Номер
10(1), С. 31 - 31
Опубликована: Янв. 6, 2025
In
recent
years,
unmanned
aerial
vehicle
(UAV)
technology
has
advanced
significantly,
enabling
its
widespread
use
in
critical
applications
such
as
surveillance,
search
and
rescue,
environmental
monitoring.
However,
planning
reliable,
safe,
economical
paths
for
UAVs
real-world
environments
remains
a
significant
challenge.
this
paper,
we
propose
multi-strategy
improved
red-tailed
hawk
(IRTH)
algorithm
UAV
path
real
environments.
First,
enhance
the
quality
of
initial
population
by
using
stochastic
reverse
learning
strategy
based
on
Bernoulli
mapping.
Then,
is
further
through
dynamic
position
update
optimization
mean
fusion,
which
enhances
exploration
capabilities
helps
it
explore
promising
solution
spaces
more
effectively.
Additionally,
proposed
an
method
frontier
updates
trust
domain,
better
balances
exploitation.
To
evaluate
effectiveness
algorithm,
compare
with
11
other
algorithms
IEEE
CEC2017
test
set
perform
statistical
analysis
to
assess
differences.
The
experimental
results
demonstrate
that
IRTH
yields
competitive
performance.
Finally,
validate
applicability
scenarios,
apply
path-planning
problem
practical
environments,
achieving
successfully
performing
UAVs.
Aerospace,
Год журнала:
2025,
Номер
12(2), С. 101 - 101
Опубликована: Янв. 30, 2025
This
study
proposes
a
design
procedure
for
the
multi-objective
aeroelastic
optimization
of
tow-steered
composite
wing
structure
that
operates
at
transonic
speed.
The
aerodynamic
influence
coefficient
matrix
is
generated
using
doublet
lattice
method,
with
steady-state
component
further
refined
through
high-fidelity
computational
fluid
dynamics
(CFD)
analysis
to
enhance
accuracy
in
conditions.
Finite
element
(FEA)
used
perform
structural
analysis.
A
problem
formulated
structure,
where
objective
functions
are
designed
mass
and
critical
speed,
constraints
include
limits.
comparative
eight
state-of-the-art
algorithms
conducted
evaluate
their
performance
solving
this
problem.
Among
them,
Multi-Objective
Multi-Verse
Optimization
(MOMVO)
algorithm
stands
out,
demonstrating
superior
achieving
best
results
task.
PeerJ Computer Science,
Год журнала:
2025,
Номер
11, С. e2671 - e2671
Опубликована: Фев. 17, 2025
The
termite
life
cycle
optimizer
algorithm
(TLCO)
is
a
new
bionic
meta-heuristic
that
emulates
the
natural
behavior
of
termites
in
their
habitat.
This
work
presents
an
improved
TLCO
(ITLCO)
to
increase
speed
and
accuracy
convergence.
A
novel
strategy
for
worker
generation
established
enhance
communication
between
individuals
population
population.
would
prevent
original
from
effectively
balancing
convergence
diversity
reduce
risk
reaching
local
optimum.
soldier
proposed,
which
incorporates
step
factor
adheres
principles
evolution
further
algorithm's
speed.
Furthermore,
replacement
update
mechanism
executed
when
individual
lower
quality
than
individual.
ensures
balance
findings
CEC2013,
CEC2019,
CEC2020
test
sets
indicate
ITLCO
exhibits
notable
benefits
regarding
speed,
accuracy,
stability
comparison
with
basic
four
most
exceptional
algorithms
thus
far.
