Enhancing slime mould algorithm for engineering optimization: leveraging covariance matrix adaptation and best position management
Journal of Computational Design and Engineering,
Год журнала:
2024,
Номер
11(4), С. 151 - 183
Опубликована: Июнь 12, 2024
Abstract
The
slime
mould
algorithm
(SMA),
as
an
emerging
and
promising
swarm
intelligence
algorithm,
has
been
studied
in
various
fields.
However,
SMA
suffers
from
issues
such
easily
getting
trapped
local
optima
slow
convergence,
which
pose
challenges
when
applied
to
practical
problems.
Therefore,
this
study
proposes
improved
SMA,
named
HESMA,
by
incorporating
the
covariance
matrix
adaptation
evolution
strategy
(CMA-ES)
storing
best
position
of
each
individual
(SBP).
On
one
hand,
CMA-ES
enhances
algorithm’s
exploration
capability,
addressing
issue
being
unable
explore
vicinity
optimal
solution.
other
SBP
convergence
speed
prevents
it
diverging
inferior
solutions.
Finally,
validate
effectiveness
our
proposed
conducted
experiments
on
30
IEEE
CEC
2017
benchmark
functions
compared
HESMA
with
12
conventional
metaheuristic
algorithms.
results
demonstrated
that
indeed
achieved
improvements
over
SMA.
Furthermore,
highlight
performance
further,
13
advanced
algorithms,
showed
outperformed
these
algorithms
significantly.
Next,
five
engineering
optimization
problems,
experimental
revealed
exhibited
significant
advantages
solving
real-world
These
findings
further
support
practicality
complex
design
challenges.
Язык: Английский
IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection
iScience,
Год журнала:
2024,
Номер
27(8), С. 110561 - 110561
Опубликована: Июль 22, 2024
Rime
optimization
algorithm
(RIME)
encounters
issues
such
as
an
imbalance
between
exploitation
and
exploration,
susceptibility
to
local
optima,
low
convergence
accuracy
when
handling
problems.
This
paper
introduces
a
variant
of
RIME
called
IRIME
address
these
drawbacks.
integrates
the
soft
besiege
(SB)
composite
mutation
strategy
(CMS)
restart
(RS).
To
comprehensively
validate
IRIME's
performance,
IEEE
CEC
2017
benchmark
tests
were
conducted,
comparing
it
against
many
advanced
algorithms.
The
results
indicate
that
performance
is
best.
In
addition,
applying
in
four
engineering
problems
reflects
solving
practical
Finally,
proposes
binary
version,
bIRIME,
can
be
applied
feature
selection
bIRIMR
performs
well
on
12
low-dimensional
datasets
24
high-dimensional
datasets.
It
outperforms
other
algorithms
terms
number
subsets
classification
accuracy.
conclusion,
bIRIME
has
great
potential
selection.
Язык: Английский
Application of Lévy and sine cosine algorithm hunger game search in machine learning model parameter optimization and acute appendicitis prediction
Expert Systems with Applications,
Год журнала:
2025,
Номер
unknown, С. 126413 - 126413
Опубликована: Янв. 1, 2025
Язык: Английский
Game-Theory-Based Multi-Objective Optimization for Enhancing Environmental and Social Life Cycle Assessment in Steel–Concrete Composite Bridges
Mathematics,
Год журнала:
2025,
Номер
13(2), С. 273 - 273
Опубликована: Янв. 16, 2025
The
design
of
bridges
must
balance
sustainability
and
construction
simplicity.
A
game-theory-based
optimization
method
was
applied
in
this
research
to
find
a
sustainable
steel–concrete
composite
bridge
design.
evaluated
through
cost
environmental
social
impact
using
the
Life
Cycle
Assessment
method.
process
considered
four
criteria
simultaneously,
discrete
version
SCA
algorithm
transfer
function
for
discretization.
preferred
solutions
were
selected
Minkowski
distances
approach.
Results
showed
decrease
slab
reinforcement
an
increase
amount
steel
cross-section,
leading
only
8.2‰
compared
similar
studies.
Regarding
geometry
obtained
considers
cells
upper
lower
parts
webs
improve
bending
resistance.
proposed
allows
simultaneous
multiple
provides
yet
simple
solution.
Язык: Английский
Quadruple Strategy-Driven Hiking Optimization Algorithm for Low and High-Dimensional Feature Selection and Real-World Skin Cancer Classification
Knowledge-Based Systems,
Год журнала:
2025,
Номер
unknown, С. 113286 - 113286
Опубликована: Март 1, 2025
Язык: Английский
An advanced RIME Optimizer with Random Reselection and Powell Mechanism for Engineering Design
Journal of Computational Design and Engineering,
Год журнала:
2024,
Номер
11(6), С. 139 - 179
Опубликована: Окт. 18, 2024
Abstract
RIME
is
a
recently
introduced
optimization
algorithm
that
draws
inspiration
from
natural
phenomena.
However,
has
certain
limitations.
For
example,
it
prone
to
falling
into
Local
Optima,
thus
failing
find
the
Global
and
problem
of
slow
convergence.
To
solve
these
problems,
this
paper
introduces
an
improved
(PCRIME),
which
combines
random
reselection
strategy
Powell
mechanism.
The
enhances
population
diversity
helps
escape
while
mechanism
improve
convergence
accuracy
optimal
solution.
verify
superior
performance
PCRIME,
we
conducted
series
experiments
at
CEC
2017
2022,
including
qualitative
analysis,
ablation
studies,
parameter
sensitivity
comparison
with
various
advanced
algorithms.
We
used
Wilcoxon
signed-rank
test
Friedman
confirm
advantage
PCRIME
over
its
peers.
experimental
data
show
ability
robustness.
Finally,
applies
five
real
engineering
problems
proposes
feasible
solutions
comprehensive
index
definitions
for
prove
stability
proposed
algorithm.
results
can
not
only
effectively
practical
but
also
excellent
stability,
making
Язык: Английский