The
Network
Slice
Selection
Function
(NSSF)
in
heterogeneous
technology
environments
is
a
complex
problem,
which
still
does
not
have
fully
acceptable
solution.Thus,
the
implementation
of
new
network
selection
strategies
represents
an
important
issue
development,
mainly
due
to
growing
demand
for
applications
and
scenarios
involving
5G
future
networks.This
work
then
presents
integrated
solution
NSSF
called
Decision-Aid
Framework
(NSSF
DAF),
consists
distributed
part
executed
on
user's
equipment
(e.g.smartphones,
Unmanned
Aerial
Vehicles,
IoT
brokers),
functioning
as
transparent
service,
another
at
Edge
operator
or
service
provider.It
requires
low
consumption
computing
resources
from
mobile
devices
offers
complete
independence
operator.For
this
purpose,
protocols
software
tools
are
used
classify
slices.This
employs
fourteen
multicriteria
methods
aid
decision-making:
ARAS,
COCOSO,
CODAS,
COPRAS,
EDAS,
MABAC,
MAIRCA,
MARCOS,
MOORA,
OCRA,
PROMETHEE
II,
SPOTIS,
TOPSIS
VIKOR.The
general
objective
verify
similarity
among
these
slice
classification
process,
considering
specific
scenario,
towards
framework.It
also
uses
machine
learning
through
K-means
clustering
algorithm,
adopting
hybrid
implement
operate
multi-domain
slicing
networks.Testbeds
were
conducted
validate
proposed
framework,
mapping
adequate
quality
requirements.The
results
indicate
real
possibility
offering
problem
that
can
be
implemented
Edge,
Core,
even
Radio
Base
Station
itself,
without
incremental
computational
cost
end
equipment,
allowing
experience.
Materials & Design,
Год журнала:
2024,
Номер
244, С. 113086 - 113086
Опубликована: Июнь 25, 2024
Additive
manufacturing
(AM)
has
undergone
significant
development
over
the
past
decades,
resulting
in
vast
amounts
of
data
that
carry
valuable
information.
Numerous
research
studies
have
been
conducted
to
extract
insights
from
AM
and
utilize
it
for
optimizing
various
aspects
such
as
process,
supply
chain,
real-time
monitoring.
Data
integration
into
proposed
digital
twin
frameworks
application
machine
learning
techniques
is
expected
play
pivotal
roles
advancing
future.
In
this
paper,
we
provide
an
overview
twin-assisted
AM.
On
one
hand,
discuss
domain
highlight
machine-learning
methods
utilized
field,
including
material
analysis,
design
optimization,
process
parameter
defect
detection
monitoring,
sustainability.
other
examine
status
current
technical
approach
offer
future
developments
perspectives
area.
This
review
paper
aims
present
convergence
big
data,
learning,
Although
there
are
numerous
papers
on
additive
others
twins
AM,
no
existing
considered
how
these
concepts
intrinsically
connected
interrelated.
Our
first
integrate
three
propose
a
cohesive
framework
they
can
work
together
improve
efficiency,
accuracy,
sustainability
processes.
By
exploring
latest
advancements
applications
within
domains,
our
objective
emphasize
potential
advantages
possibilities
associated
with
technologies
Knowledge-Based Systems,
Год журнала:
2024,
Номер
299, С. 111998 - 111998
Опубликована: Май 29, 2024
Constrained
multi-objective
optimization
problems
(CMOPs)
are
widespread
in
practical
applications
such
as
engineering
design,
resource
allocation,
and
scheduling
optimization.
It
is
high
challenging
for
CMOPs
to
balance
the
convergence
diversity
due
conflicting
objectives
complex
constraints.
Researchers
have
developed
a
variety
of
constrained
algorithms
(CMOAs)
find
set
optimal
solutions,
including
evolutionary
machine
learning-based
methods.
These
exhibit
distinct
advantages
solving
different
categories
CMOPs.
Recently,
(CMOEAs)
emerged
popular
approach,
with
several
literature
reviews
available.
However,
there
lack
comprehensive-view
survey
on
methods
CMOAs,
limiting
researchers
track
cutting-edge
investigations
this
research
direction.
Therefore,
paper
latest
handling
A
new
classification
method
proposed
divide
literature,
containing
classical
mathematical
methods,
learning
Subsequently,
it
modeling
context
applications.
Lastly,
gives
potential
directions
respect
This
able
provide
guidance
inspiration
scholars
studying
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 11, 2025
Abstract
The
Parrot
Optimizer
(PO)
has
recently
emerged
as
a
powerful
algorithm
for
single-objective
optimization,
known
its
strong
global
search
capabilities.
This
study
extends
PO
into
the
Multi-Objective
(MOPO),
tailored
multi-objective
optimization
(MOO)
problems.
MOPO
integrates
an
outward
archive
to
preserve
Pareto
optimal
solutions,
inspired
by
behavior
of
Pyrrhura
Molinae
parrots.
Its
performance
is
validated
on
Congress
Evolutionary
Computation
2020
(CEC’2020)
benchmark
suite.
Additionally,
extensive
testing
four
constrained
engineering
design
challenges
and
eight
popular
confined
unconstrained
test
cases
proves
MOPO’s
superiority.
Moreover,
real-world
helical
coil
springs
automotive
applications
conducted
depict
reliability
proposed
in
solving
practical
Comparative
analysis
was
performed
with
seven
published,
state-of-the-art
algorithms
chosen
their
proven
effectiveness
representation
current
research
landscape-Improved
Manta-Ray
Foraging
Optimization
(IMOMRFO),
Gorilla
Troops
(MOGTO),
Grey
Wolf
(MOGWO),
Whale
Algorithm
(MOWOA),
Slime
Mold
(MOSMA),
Particle
Swarm
(MOPSO),
Non-Dominated
Sorting
Genetic
II
(NSGA-II).
results
indicate
that
consistently
outperforms
these
across
several
key
metrics,
including
Set
Proximity
(PSP),
Inverted
Generational
Distance
Decision
Space
(IGDX),
Hypervolume
(HV),
(GD),
spacing,
maximum
spread,
confirming
potential
robust
method
addressing
complex
MOO
