Mathematics,
Год журнала:
2024,
Номер
12(20), С. 3221 - 3221
Опубликована: Окт. 14, 2024
The
Arithmetic
Optimization
Algorithm
(AOA)
is
a
novel
metaheuristic
inspired
by
mathematical
arithmetic
operators.
Due
to
its
simple
structure
and
flexible
parameter
adjustment,
the
AOA
has
been
applied
solve
various
engineering
problems.
However,
still
faces
challenges
such
as
poor
exploitation
ability
tendency
fall
into
local
optima,
especially
in
complex,
high-dimensional
In
this
paper,
we
propose
Hybrid
Improved
(HIAOA)
address
issues
of
susceptibility
optima
AOAs.
First,
grey
wolf
optimization
incorporated
AOAs,
where
group
hunting
behavior
GWO
allows
multiple
individuals
perform
searches
at
same
time,
enabling
solution
be
more
finely
tuned
avoiding
over-concentration
particular
region,
which
can
improve
capability
AOA.
Second,
end
each
run,
follower
mechanism
Cauchy
mutation
operation
Sparrow
Search
are
selected
with
probability
perturbed
enhance
escape
from
optimum.
overall
performance
improved
algorithm
assessed
selecting
23
benchmark
functions
using
Wilcoxon
rank-sum
test.
results
HIAOA
compared
other
intelligent
algorithms.
Furthermore,
also
three
design
problems
successfully,
demonstrating
competitiveness.
According
experimental
results,
better
test
than
comparator.
Materials Testing,
Год журнала:
2024,
Номер
66(9), С. 1439 - 1448
Опубликована: Май 24, 2024
Abstract
Optimization
techniques
play
a
pivotal
role
in
enhancing
the
performance
of
engineering
components
across
various
real-world
applications.
Traditional
optimization
methods
are
often
augmented
with
exploitation-boosting
due
to
their
inherent
limitations.
Recently,
nature-inspired
algorithms,
known
as
metaheuristics
(MHs),
have
emerged
efficient
tools
for
solving
complex
problems.
However,
these
algorithms
face
challenges
such
imbalance
between
exploration
and
exploitation
phases,
slow
convergence,
local
optima.
Modifications
incorporating
oppositional
techniques,
hybridization,
chaotic
maps,
levy
flights
been
introduced
address
issues.
This
article
explores
application
recently
developed
crayfish
algorithm
(COA),
assisted
by
artificial
neural
networks
(ANN),
design
optimization.
The
COA,
inspired
foraging
migration
behaviors,
incorporates
temperature-dependent
strategies
balance
phases.
Additionally,
ANN
augmentation
enhances
algorithm’s
accuracy.
COA
method
optimizes
components,
including
cantilever
beams,
hydrostatic
thrust
bearings,
three-bar
trusses,
diaphragm
springs,
vehicle
suspension
systems.
Results
demonstrate
effectiveness
achieving
superior
solutions
compared
other
emphasizing
its
potential
diverse
Archives of Computational Methods in Engineering,
Год журнала:
2024,
Номер
31(8), С. 4485 - 4519
Опубликована: Авг. 21, 2024
Abstract
The
greatest
and
fastest
advances
in
the
computing
world
today
require
researchers
to
develop
new
problem-solving
techniques
capable
of
providing
an
optimal
global
solution
considering
a
set
aspects
restrictions.
Due
superiority
metaheuristic
Algorithms
(MAs)
solving
different
classes
problems
promising
results,
MAs
need
be
studied.
Numerous
studies
algorithms
fields
exist,
but
this
study,
comprehensive
review
MAs,
its
nature,
types,
applications,
open
issues
are
introduced
detail.
Specifically,
we
introduce
metaheuristics'
advantages
over
other
techniques.
To
obtain
entire
view
about
classifications
based
on
(i.e.,
inspiration
source,
number
search
agents,
updating
mechanisms
followed
by
agents
their
positions,
primary
parameters
algorithms)
presented
detail,
along
with
optimization
including
both
structure
types.
application
area
occupies
lot
research,
so
most
widely
used
applications
presented.
Finally,
great
effort
research
is
directed
discuss
challenges
which
help
upcoming
know
future
directions
active
field.
Overall,
study
helps
existing
understand
basic
information
field
addition
directing
newcomers
areas
that
addressed
future.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 25, 2025
This
paper
addresses
issues
of
inadequate
accuracy
and
inconsistency
between
global
search
efficacy
local
development
capability
in
the
black-winged
kite
algorithm
for
practical
problem-solving
by
proposing
a
optimization
that
integrates
Osprey
Crossbar
enhancement
(DKCBKA).
Firstly,
adaptive
index
factor
fusion
Optimization
Algorithm
approach
are
incorporated
to
enhance
algorithm's
convergence
rate,
probability
distribution
is
updated
throughout
attack
stage.
Second,
stochastic
difference
variant
method
implemented
prevent
from
entering
optima.
Lastly,
longitudinal
transversal
crossover
technique
dynamically
alter
population's
individual
optimal
solutions.
Fifteen
benchmark
functions
chosen
test
effectiveness
enhanced
compare
efficiency
each
technique.
Simulation
experiments
performed
on
CEC2017
CEC2019
sets,
revealing
DKCBKA
surpasses
five
standard
swarm
intelligence
methods
six
improved
algorithms
regarding
solution
speed.
The
superiority
meeting
real
challenges
further
demonstrated
three
engineering
problems
DKCBKA,
with
capabilities
18.222%,
99.885%
0.561%
higher
than
BKA,
respectively.
Alexandria Engineering Journal,
Год журнала:
2024,
Номер
97, С. 267 - 282
Опубликована: Апрель 19, 2024
Nuclear
reactor
control
is
pivotal
for
the
safe
and
efficient
operation
of
nuclear
power
plants,
facilitating
regulation
reactivity.
This
study
introduces
an
optimized
fractional-order
proportional-integral-derivative
(FOPID)
controller
tailored
maintaining
reactivity
levels
in
particularly
during
load-following
operations.
The
adjusts
position
rod
to
regulate
output
effectively.
We
enhance
FOPID
controller's
performance
using
a
modification
Planet
Optimization
Algorithm
(POA-M),
leveraging
strengths
Arithmetic
(AOA)
improve
its
exploitation
capabilities.
evaluate
efficacy
POA-M-FOPID
against
traditional
techniques,
including
POA,
AOA,
Particle
Swarm
(PSO).
assess
Egyptian
Testing
Research
Reactor
(ETRR-2)
as
case
study.
Our
results
demonstrate
that
outperforms
alternative
algorithms
across
various
metrics,
exhibiting
superior
resilience
efficiency.
Notably,
utilization
yields
remarkable
improvements
performance,
achieving
significantly
reduced
settling
time
(25.27
sec)
maximum
overshoot
(0.67
%)
compared
conventional
controllers
incorporating
PSO
methods.
These
findings
underscore
effectiveness
enhancing
systems,
offering
potential
benefits
broader
industry
terms
safety,
stability,
operational
