Slime
Mould
Algorithm
(SMA)
is
a
new
swarm
intelligence
algorithm
inspired
by
the
oscillatory
behavior
of
slime
molds
during
foraging.
Numerous
researchers
have
widely
applied
SMA
and
its
variants
in
various
domains
proved
value
experiments
literatures.
In
this
paper
comprehensive
survey
on
introduced,
which
based
130
articles
visa
Google-scholar
between
2022
July,
2023.
Firstly,
theory
described.
Secondly
improved
are
provided
categorized
according
to
approach
that
they
with.
Finally,
it
also
discusses
main
applications
such
as
engineering
optimization,
energy
machine
learning,
network,
scheduling
image
segmentation
etc.
This
review
presents
some
research
suggestion
for
researcher
who
interested
algorithm.
Evolutionary Intelligence,
Journal Year:
2024,
Volume and Issue:
18(1)
Published: Dec. 5, 2024
Abstract
Nonlinear,
complex
optimization
problems
are
prevalent
in
many
scientific
and
engineering
fields.
Traditional
algorithms
often
struggle
with
these
due
to
their
high
dimensionality
intricate
nature,
making
them
time-consuming.
Many
researchers
have
proposed
new
metaheuristic
inspired
by
biological
behaviors
which
comparatively
show
higher
performance
accuracy
than
traditional
algorithms.
Nature-inspired
algorithms,
particularly
those
based
on
swarm
intelligence,
offer
adaptable
efficient
solutions
challenges.
In
recent
years,
intelligence
made
significant
advancements.
Classical
CEC
benchmark
suits
immersively
useful
for
studying
the
of
According
our
literature
survey,
we
identified
that
were
evaluated
accuracy.
Currently,
used
applications,
efficiency
computational
complexity
need
be
evaluated.
A
broad-level
study
popular
has
not
been
done
recently.
Therefore
this
comprehensively
evaluate
compare
21
bio-inspired
eight
non-separable
unimodal,
separable
five
multimodal,
seven
multimodal
functions,
two
2018
objective
functions.
We
structure
mathematical
model
selected
Then
categorized
into
six
different
behavioral
groups.
calculated
root
mean
square
error
between
expected
actual
values.
performed
an
RMSE
cross-validation
statistical
test
understand
how
accurately
algorithm
resolves
average
problem.
found
Artificial
Lizard
Search
Optimization
(ALSO)
is
most
prominent
efficiency.
Besides
that,
Cat
Swarm
(CSO),
Squirrel
Algorithm
(SSA),
Chimp
(CHOA-B)
also
considered
more
universal
The
(SSA)
ALSO’s
second-best
time
complexity.
Wasp
(WSO),
Bat-Inspired
(BA)
presented
lowest
Finally,
several
important
issues
research
directions
discussed.
International Journal of Computational Intelligence Systems,
Journal Year:
2024,
Volume and Issue:
17(1)
Published: Dec. 19, 2024
The
slime
mould
optimization
algorithm
(SMA)
is
one
of
the
well-established
algorithms
with
a
superior
performance
in
variety
real-life
problems.
SMA
has
certain
limitations
that
reduce
diversity
and
accuracy
solutions,
raising
risk
premature
convergence
an
inadequate
balance
between
its
exploitation
exploration
phases.
In
this
study,
novel
hybrid
multi-verse
(SMMVA)
proposed
to
improve
algorithm.
(MVO)
introduced
while
updating
variation
parameter
through
nonlinear
factor.
balances
ability
explore
exploit,
boosts
global
capability
improves
accuracy,
stability,
speed.
SMMVA
compared
16
recently-published
metaheuristic
on
23
standard
benchmark
functions,
CEC2017,
CEC2022
test
five
engineering
design
problems,
UCI
repository
datasets.
statistical
tests
such
as
Friedman's
test,
box
plot
comparison
Wilcoxon
rank
sum
are
employed
verify
SMMVA's
stability
superiority.
was
tested
total
64
achieving
overall
success
rate
68.75%
across
30
runs
other
counterparts.
results
for
feature
selection
problem
show
k-nearest
neighbour
(KNN)
classifier
obtained
more
informative
features
higher
values.
Thus,
proven
perform
excellent
solving
problems
better
solution
promising
prospect.
Slime
Mould
Algorithm
(SMA)
is
a
new
swarm
intelligence
algorithm
inspired
by
the
oscillatory
behavior
of
slime
molds
during
foraging.
Numerous
researchers
have
widely
applied
SMA
and
its
variants
in
various
domains
proved
value
experiments
literatures.
In
this
paper
comprehensive
survey
on
introduced,
which
based
130
articles
visa
Google-scholar
between
2022
July,
2023.
Firstly,
theory
described.
Secondly
improved
are
provided
categorized
according
to
approach
that
they
with.
Finally,
it
also
discusses
main
applications
such
as
engineering
optimization,
energy
machine
learning,
network,
scheduling
image
segmentation
etc.
This
review
presents
some
research
suggestion
for
researcher
who
interested
algorithm.
