PeerJ Computer Science,
Journal Year:
2024,
Volume and Issue:
10, P. e2102 - e2102
Published: July 22, 2024
Constrained
many-objective
optimization
problems
(CMaOPs)
have
gradually
emerged
in
various
areas
and
are
significant
for
this
field.
These
often
involve
intricate
Pareto
frontiers
(PFs)
that
both
refined
uneven,
thereby
making
their
resolution
difficult
challenging.
Traditional
algorithms
tend
to
over
prioritize
convergence,
leading
premature
convergence
of
the
decision
variables,
which
greatly
reduces
possibility
finding
constrained
(CPFs).
This
results
poor
overall
performance.
To
tackle
challenge,
our
solution
involves
a
novel
dual-population
evolutionary
algorithm
based
on
reference
point
angle
easing
strategy
(dCMaOEA-RAE).
It
relies
relaxed
selection
utilizing
points
angles
facilitate
cooperation
between
dual
populations
by
retaining
solutions
may
currently
perform
poorly
but
contribute
positively
process.
We
able
guide
population
move
optimal
feasible
region
timely
manner
order
obtain
series
superior
can
be
obtained.
Our
proposed
algorithm’s
competitiveness
across
all
three
evaluation
indicators
was
demonstrated
through
experimental
conducted
77
test
problems.
Comparisons
with
ten
other
cutting-edge
further
validated
its
efficacy.
Materials,
Journal Year:
2024,
Volume and Issue:
17(14), P. 3521 - 3521
Published: July 16, 2024
This
paper
provides
a
comprehensive
review
of
recent
advancements
in
computational
methods
for
modeling,
simulation,
and
optimization
complex
systems
materials
engineering,
mechanical
energy
systems.
We
identified
key
trends
highlighted
the
integration
artificial
intelligence
(AI)
with
traditional
methods.
Some
cited
works
were
previously
published
within
topic:
"Computational
Methods:
Modeling,
Simulations,
Optimization
Complex
Systems";
thus,
this
article
compiles
latest
reports
from
field.
The
work
presents
various
contemporary
applications
advanced
algorithms,
including
AI
It
also
introduces
proposals
novel
strategies
production
domain.
is
essential
to
optimize
properties
used
energy.
Our
findings
demonstrate
significant
improvements
accuracy
efficiency,
offering
valuable
insights
researchers
practitioners.
contributes
field
by
synthesizing
state-of-the-art
developments
suggesting
directions
future
research,
underscoring
critical
role
these
advancing
engineering
technological
solutions.
Knowledge-Based Systems,
Journal Year:
2024,
Volume and Issue:
299, P. 111998 - 111998
Published: May 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
Engineering Optimization,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 31
Published: May 30, 2024
Constrained
Many-objective
Optimization
Problems
(CMaOPs)
are
challenging
in
handling
objectives
and
constraints
simultaneously.
Here,
a
novel
Evolutionary
Algorithm
(CMaOEA)
based
on
Multi-population,
Knowledge
transfer
Improved
environmental
selection
called
CMaMKI
is
proposed
to
handle
CMaOPs.
The
framework
evolves
task
population
solve
the
original
CMaOP
another
helper
problem
derived
from
one.
To
assist
solving
CMaOP,
knowledge
expression
strategy
designed
share
useful
information
with
population.
Meanwhile,
balance
convergence,
diversity
feasibility,
an
enhanced
devised
by
combining
ε-constrained
technique,
θ-dominance
subregional
density
evaluation.
algorithm
evaluated
contrasted
six
state-of-the-art
algorithms
set
of
benchmark
experimental
results
demonstrate
superiority
competitiveness
method.
