PeerJ Computer Science,
Год журнала:
2025,
Номер
11, С. e2722 - e2722
Опубликована: Фев. 28, 2025
The
Atom
Search
Optimization
(ASO)
algorithm
is
a
recent
advancement
in
metaheuristic
optimization
inspired
by
principles
of
molecular
dynamics.
It
mathematically
models
and
simulates
the
natural
behavior
atoms,
with
interactions
governed
forces
derived
from
Lennard-Jones
potential
constraint
based
on
bond-length
potentials.
Since
its
inception
2019,
it
has
been
successfully
applied
to
various
challenges
across
diverse
fields
technology
science.
Despite
notable
achievements
rapidly
growing
body
literature
ASO
domain,
comprehensive
study
evaluating
success
implementations
still
lacking.
To
address
this
gap,
article
provides
thorough
review
half
decade
advancements
research,
synthesizing
wide
range
studies
highlight
key
variants,
their
foundational
principles,
significant
achievements.
examines
applications,
including
single-
multi-objective
problems,
introduces
well-structured
taxonomy
guide
future
exploration
ASO-related
research.
reviewed
reveals
that
several
variants
algorithm,
modifications,
hybridizations,
implementations,
have
developed
tackle
complex
problems.
Moreover,
effectively
domains,
such
as
engineering,
healthcare
medical
Internet
Things
communication,
clustering
data
mining,
environmental
modeling,
security,
engineering
emerging
most
prevalent
application
area.
By
addressing
common
researchers
face
selecting
appropriate
algorithms
for
real-world
valuable
insights
into
practical
applications
offers
guidance
designing
tailored
specific
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 6, 2025
The
Snow
Goose
Algorithm
(SGA)
is
a
new
meta-heuristic
algorithm
proposed
in
2024,
which
has
been
proved
to
have
good
optimization
effect,
but
there
are
still
problems
that
easy
fall
into
local
optimal
and
premature
convergence.
In
order
further
improve
the
performance
of
algorithm,
this
paper
proposes
an
improved
(ISGA)
based
on
three
strategies
according
real
migration
habits
snow
geese:
(1)
Lead
goose
rotation
mechanism.
(2)
Honk-guiding
(3)
Outlier
boundary
strategy.
Through
above
strategies,
exploration
development
ability
original
comprehensively
enhanced,
convergence
accuracy
speed
improved.
paper,
two
standard
test
sets
IEEE
CEC2022
CEC2017
used
verify
excellent
algorithm.
practical
application
ISGA
tested
through
8
engineering
problems,
employed
enhance
effect
clustering
results
show
compared
with
comparison
faster
iteration
can
find
better
solutions,
shows
its
great
potential
solving
problems.
Journal of Field Robotics,
Год журнала:
2024,
Номер
41(6), С. 1843 - 1863
Опубликована: Апрель 29, 2024
Abstract
With
the
widespread
adoption
of
mobile
robots,
effective
path
planning
has
become
increasingly
critical.
Although
traditional
search
methods
have
been
extensively
utilized,
meta‐heuristic
algorithms
gained
popularity
owing
to
their
efficiency
and
problem‐specific
heuristics.
However,
challenges
remain
in
terms
premature
convergence
lack
solution
diversity.
To
address
these
issues,
this
paper
proposes
a
novel
artificial
potential
field
enhanced
improved
multiobjective
snake
optimization
algorithm
(APF‐IMOSO).
This
presents
four
key
enhancements
optimizer
significantly
improve
its
performance.
Additionally,
it
introduces
fitness
functions
focused
on
optimizing
length,
safety
(evaluated
via
method),
energy
consumption,
time
efficiency.
The
results
simulation
experiment
scenarios
including
static
dynamic
highlight
APF‐IMOSO's
advantages,
delivering
improvements
8.02%,
7.61%,
50.71%,
12.74%
safety,
efficiency,
time‐savings,
respectively,
over
original
algorithm.
Compared
with
other
advanced
meta‐heuristics,
APF‐IMOSO
also
excels
indexes.
Real
robot
experiments
show
an
average
length
error
1.19%
across
scenarios.
reveal
that
can
generate
multiple
viable
collision‐free
paths
complex
environments
under
various
constraints,
showcasing
for
use
within
realm
navigation.
