Materials Testing,
Год журнала:
2024,
Номер
66(10), С. 1539 - 1556
Опубликована: Авг. 26, 2024
Abstract
Build
orientation
in
additive
manufacturing
technology
is
a
pre-process
application
that
affects
many
parameters,
such
as
the
volume
of
support
structure,
part
quality,
build
time,
and
cost.
Determining
optimum
for
one
or
more
objectives
complex
parts
an
error-prone
puzzle.
This
study
evaluates
behavior
cuckoo
search
algorithm,
differential
evolution,
firefly
genetic
gray
wolf
optimizer,
Harris
hawks
optimization,
jaya
moth
flame
multi-verse
particle
swarm
A
Sine
cosine
salp
whale
optimization
algorithm
to
determine
component
be
manufactured
additively.
The
efficiency
these
algorithms
evaluated
on
problem
two
components
considering
undercut
area
height
objective
functions.
Thus,
feasibility
real-world
problems
revealed.
According
results
obtained
from
extensive
analysis,
best
alternative
minimizing
area,
its
robustness.
However,
required
time
solve
much
almost
twice
other
algorithms.
are
alternatives
height.
Materials Testing,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 11, 2025
Abstract
In
the
era
of
artificial
intelligence
(AI),
optimization
and
parametric
studies
engineering
structural
systems
have
become
feasible
tasks.
AI
ML
(machine
learning)
offer
advantages
over
classical
techniques,
which
often
face
challenges
such
as
slower
convergence,
difficulty
handling
multiobjective
functions,
high
computational
time.
Modern
techniques
may
not
effectively
address
all
critical
design
problems
despite
these
advancements.
Nature-inspired
algorithms
based
on
physical
phenomena
in
nature,
human
behavior,
swarm
intelligence,
evolutionary
principles
present
a
viable
alternative
for
multidisciplinary
challenges.
This
article
explores
various
using
newly
developed
modified
hiking
algorithm
(HOA).
The
is
inspired
by
hill
climbing
hiker
speed.
HOA
are
compared
with
those
several
famous
from
literature,
demonstrating
superior
results
terms
statistical
measures.
Materials Testing,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 14, 2025
Abstract
This
paper
introduces
an
improved
optimization
algorithm
based
on
migration
patterns
of
greylag
geese,
known
for
their
efficient
flying
formations.
The
Modified
Greylag
Goose
Optimization
Algorithm
(MGGOA)
is
modified
by
augmenting
the
levy
flight
mechanism
and
artificial
neural
network
(ANN)
strategies.
detailed,
presenting
mathematical
formulations
both
phases.
Subsequently,
applies
MGGOA
to
various
engineering
problems,
including
heat
exchanger
design,
car
side
impact
spring
design
optimization,
disc
clutch
brake
structural
automobile
component.
Statistical
comparisons
with
benchmark
algorithms
demonstrate
efficacy
in
finding
optimal
solutions
these
problems.
Energy & Environment,
Год журнала:
2024,
Номер
unknown
Опубликована: Апрель 3, 2024
This
research
examines
the
overall
energy
usage
in
residential
buildings,
focusing
on
architectural
characteristics.
The
study
utilizes
a
combination
of
CatBoost
method
and
meta-heuristic
algorithms
for
analysis.
main
approach
this
is
based
accuracy
defects
individual
models,
which
leads
to
employment
as
group
model.
Due
lack
enough
examinations
while
utilizing
method,
model
its
hyperparameters
are
optimized
using
various
methods,
including
Phasor
Particle
Swarm
Optimization
(PPSO),
Slime
Mould
Algorithm
(SMA),
Sparrow
Search
(SSA),
Ant
Lion
Optimizer
(ALO),
Artificial
Bee
Colony
(ABC),
Grey
Wolf
(GWO).
Eventually,
performance
all
models
compared
by
conduction
case
study,
diverse
statistical
examination
indexes
divided
dwelling
types
i.e.,
(1)
Standard
efficiency
upgraded
dwellings
(D1),
(2)
High
(D2),
(3)
Ultra
high
(D3).
results
show
that
hybrid
proposed
has
proper
ability
investigate
total
site
energy.
D1
according
test
dataset,
integrated
CatBoost-SMA
indicates
most
desired
predicting
But
D2
D3
referring
evaluation
emphasize
CatBoost-PPSO
shows
reliable
performance.
Materials Testing,
Год журнала:
2024,
Номер
66(10), С. 1539 - 1556
Опубликована: Авг. 26, 2024
Abstract
Build
orientation
in
additive
manufacturing
technology
is
a
pre-process
application
that
affects
many
parameters,
such
as
the
volume
of
support
structure,
part
quality,
build
time,
and
cost.
Determining
optimum
for
one
or
more
objectives
complex
parts
an
error-prone
puzzle.
This
study
evaluates
behavior
cuckoo
search
algorithm,
differential
evolution,
firefly
genetic
gray
wolf
optimizer,
Harris
hawks
optimization,
jaya
moth
flame
multi-verse
particle
swarm
A
Sine
cosine
salp
whale
optimization
algorithm
to
determine
component
be
manufactured
additively.
The
efficiency
these
algorithms
evaluated
on
problem
two
components
considering
undercut
area
height
objective
functions.
Thus,
feasibility
real-world
problems
revealed.
According
results
obtained
from
extensive
analysis,
best
alternative
minimizing
area,
its
robustness.
However,
required
time
solve
much
almost
twice
other
algorithms.
are
alternatives
height.
