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.
PLoS ONE,
Journal Year:
2024,
Volume and Issue:
19(8), P. e0308474 - e0308474
Published: Aug. 19, 2024
This
research
article
presents
the
Multi-Objective
Hippopotamus
Optimizer
(MOHO),
a
unique
approach
that
excels
in
tackling
complex
structural
optimization
problems.
The
(HO)
is
novel
meta-heuristic
methodology
draws
inspiration
from
natural
behaviour
of
hippos.
HO
built
upon
trinary-phase
model
incorporates
mathematical
representations
crucial
aspects
Hippo's
behaviour,
including
their
movements
aquatic
environments,
defense
mechanisms
against
predators,
and
avoidance
strategies.
conceptual
framework
forms
basis
for
developing
multi-objective
(MO)
variant
MOHO,
which
was
applied
to
optimize
five
well-known
truss
structures.
Balancing
safety
precautions
size
constraints
concerning
stresses
on
individual
sections
constituent
parts,
these
problems
also
involved
competing
objectives,
such
as
reducing
weight
structure
maximum
nodal
displacement.
findings
six
popular
methods
were
used
compare
results.
Four
industry-standard
performance
measures
this
comparison
qualitative
examination
finest
Pareto-front
plots
generated
by
each
algorithm.
average
values
obtained
Friedman
rank
test
analysis
unequivocally
showed
MOHO
outperformed
other
resolving
significant
quickly.
In
addition
finding
preserving
more
Pareto-optimal
sets,
recommended
algorithm
produced
excellent
convergence
variance
objective
decision
fields.
demonstrated
its
potential
navigating
objectives
through
diversity
analysis.
Additionally,
swarm
effectively
visualize
MOHO's
solution
distribution
across
iterations,
highlighting
superior
behaviour.
Consequently,
exhibits
promise
valuable
method
issues.
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(5), P. e26665 - e26665
Published: March 1, 2024
This
research
introduces
the
Multi-Objective
Liver
Cancer
Algorithm
(MOLCA),
a
novel
approach
inspired
by
growth
and
proliferation
patterns
of
liver
tumors.
MOLCA
emulates
evolutionary
tendencies
tumors,
leveraging
their
expansion
dynamics
as
model
for
solving
multi-objective
optimization
problems
in
engineering
design.
The
algorithm
uniquely
combines
genetic
operators
with
Random
Opposition-Based
Learning
(ROBL)
strategy,
optimizing
both
local
global
search
capabilities.
Further
enhancement
is
achieved
through
integration
elitist
non-dominated
sorting
(NDS),
information
feedback
mechanism
(IFM)
Crowding
Distance
(CD)
selection
method,
which
collectively
aim
to
efficiently
identify
Pareto
optimal
front.
performance
rigorously
assessed
using
comprehensive
set
standard
test
benchmarks,
including
ZDT,
DTLZ
various
Constraint
(CONSTR,
TNK,
SRN,
BNH,
OSY
KITA)
real-world
design
like
Brushless
DC
wheel
motor,
Safety
isolating
transformer,
Helical
spring,
Two-bar
truss
Welded
beam.
Its
efficacy
benchmarked
against
prominent
algorithms
such
grey
wolf
optimizer
(NSGWO),
multiobjective
multi-verse
(MOMVO),
(NSGA-II),
decomposition-based
(MOEA/D)
marine
predator
(MOMPA).
Quantitative
analysis
conducted
GD,
IGD,
SP,
SD,
HV
RT
metrics
represent
convergence
distribution,
while
qualitative
aspects
are
presented
graphical
representations
fronts.
source
code
available
at:
https://github.com/kanak02/MOLCA.
Engineering Optimization,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 30
Published: Feb. 12, 2024
The
main
aim
of
this
article
is
to
use
a
new
metaheuristic
algorithm
for
the
optimum
design
truss
structures.
artificial
rabbits
optimization
(ARO)
used,
in
which
rabbits'
natural
survival
strategies,
such
as
detour
foraging
and
random
hiding,
are
considered
developing
search
loop
algorithm.
An
improved
version,
I-ARO,
also
proposed,
using
diagonal
linear
uniform
(DLU)
initialization
process
instead
conventional
enhance
overall
performance
convergence
behaviour
ARO
For
numerical
investigations,
five
well-known
benchmark
structures
with
10,
37,
52,
72
120
bars
determined,
considering
frequencies
constraints.
I-ARO
shown
be
capable
providing
better
results
than
standard
other
approaches
literature.
This
demonstrates
capability
DLU
method
enhancing
International Journal of Computational Intelligence Systems,
Journal Year:
2025,
Volume and Issue:
18(1)
Published: Jan. 8, 2025
Abstract
In
the
current
landscape,
there
is
a
rapid
increase
in
creation
of
new
algorithms
designed
for
specialized
problem
scenarios.
The
performance
these
unfamiliar
or
practical
settings
often
remains
untested.
This
paper
presents
development,
multi-objective
Runge–Kutta
optimizer
(MORKO),
which
built
upon
principles
elitist
non-dominated
sorting
and
crowding
distance.
goal
to
achieve
superior
efficiency,
diversity,
robustness
solutions.
MORKO
effectiveness
further
enhanced
by
incorporating
various
strategies
that
maintain
balance
between
diversity
execution
efficiency.
approach
not
only
directs
search
toward
optimal
regions
but
also
ensures
process
does
become
stagnant.
efficiency
compared
against
renowned
like
marine
predicator
algorithm
(MOMPA),
gradient-based
(MOGBO),
evolutionary
based
on
decomposition
(MOEA/D),
genetic
(NSGA-II)
several
test
benchmarks
such
as
ZDT,
DTLZ,
constraint
(CONSTR,
TNK,
SRN,
BNH,
OSY
KITA)
real-world
engineering
design
(brushless
DC
wheel
motor,
safety
isolating
transformer,
helical
spring,
two-bar
truss,
welded
beam,
disk
brake,
tool
spindle
cantilever
beam)
problems.
We
used
unique,
non-overlapping
metrics
this
comparison
suggested
fresh
correlation
analysis
technique
exploration.
outcomes
were
rigorously
tested
confirmed
using
non-parametric
statistical
evaluations.
proves
excel
deriving
comprehensive
varied
solutions
many
tests
challenges,
owing
its
multifaceted
features.
Looking
ahead,
has
potential
applications
complex
management
tasks.
Mathematical Biosciences & Engineering,
Journal Year:
2024,
Volume and Issue:
21(2), P. 2856 - 2878
Published: Jan. 1, 2024
<abstract>
<p>Three-dimensional
path
planning
refers
to
determining
an
optimal
in
a
three-dimensional
space
with
obstacles,
so
that
the
is
as
close
target
location
possible,
while
meeting
some
other
constraints,
including
distance,
altitude,
threat
area,
flight
time,
energy
consumption,
and
on.
Although
bald
eagle
search
algorithm
has
characteristics
of
simplicity,
few
control
parameters,
strong
global
capabilities,
it
not
yet
been
applied
complex
problems.
In
order
broaden
application
scenarios
scope
solve
problem
space,
we
present
study
where
five
geographical
environments
are
simulated
represent
real-life
unmanned
aerial
vehicles
flying
scenarios.
These
maps
effectively
test
algorithm's
ability
handle
various
terrains,
extreme
environments.
The
experimental
results
have
verified
excellent
performance
BES
algorithm,
which
can
quickly,
stably,
problems,
making
highly
competitive
this
field.</p>
</abstract>
