IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 20942 - 20961
Published: Jan. 1, 2024
Over
recent
decades,
the
field
of
mobile
robot
path
planning
has
evolved
significantly,
driven
by
pursuit
enhanced
navigation
solutions.
The
need
to
determine
optimal
trajectories
within
complex
environments
led
exploration
diverse
methodologies.
This
paper
focuses
on
a
specific
subset:
Bio-inspired
Population-based
Optimization
(BPO)
BPO
methods
play
pivotal
role
in
generating
efficient
paths
for
planning.
Amidst
abundance
optimization
approaches
over
past
decade,
only
fraction
studies
have
effectively
integrated
these
into
strategies.
focus
is
years
2014-2023,
reviewing
techniques
applied
challenges.
Contributions
include
comprehensive
review
planning,
along
with
an
experimental
methodology
method
comparison
under
consistent
conditions.
encompasses
same
environment,
initial
conditions,
and
replicates.
A
multi-objective
function
incorporated
evaluate
methods.
delves
key
concepts,
mathematical
models,
algorithm
implementations
examined
techniques.
setup,
methodology,
benchmarking
performance
results
are
discussed.
In
conclusion,
this
reviews
algorithms
introduces
standardized
approach
algorithms,
providing
insights
their
strengths
challenges
Artificial Intelligence Review,
Journal Year:
2025,
Volume and Issue:
58(3)
Published: Jan. 6, 2025
The
advent
of
the
intelligent
information
era
has
witnessed
a
proliferation
complex
optimization
problems
across
various
disciplines.
Although
existing
meta-heuristic
algorithms
have
demonstrated
efficacy
in
many
scenarios,
they
still
struggle
with
certain
challenges
such
as
premature
convergence,
insufficient
exploration,
and
lack
robustness
high-dimensional,
nonconvex
search
spaces.
These
limitations
underscore
need
for
novel
techniques
that
can
better
balance
exploration
exploitation
while
maintaining
computational
efficiency.
In
response
to
this
need,
we
propose
Artificial
Lemming
Algorithm
(ALA),
bio-inspired
metaheuristic
mathematically
models
four
distinct
behaviors
lemmings
nature:
long-distance
migration,
digging
holes,
foraging,
evading
predators.
Specifically,
migration
burrow
are
dedicated
highly
exploring
domain,
whereas
foraging
predators
provide
during
process.
addition,
ALA
incorporates
an
energy-decreasing
mechanism
enables
dynamic
adjustments
between
exploitation,
thereby
enhancing
its
ability
evade
local
optima
converge
global
solutions
more
robustly.
To
thoroughly
verify
effectiveness
proposed
method,
is
compared
17
other
state-of-the-art
on
IEEE
CEC2017
benchmark
test
suite
CEC2022
suite.
experimental
results
indicate
reliable
comprehensive
performance
achieve
superior
solution
accuracy,
convergence
speed,
stability
most
cases.
For
29
10-,
30-,
50-,
100-dimensional
functions,
obtains
lowest
Friedman
average
ranking
values
among
all
competitor
methods,
which
1.7241,
2.1034,
2.7241,
2.9310,
respectively,
12
again
wins
optimal
2.1667.
Finally,
further
evaluate
applicability,
implemented
address
series
cases,
including
constrained
engineering
design,
photovoltaic
(PV)
model
parameter
identification,
fractional-order
proportional-differential-integral
(FOPID)
controller
gain
tuning.
Our
findings
highlight
competitive
edge
potential
real-world
applications.
source
code
publicly
available
at
https://github.com/StevenShaw98/Artificial-Lemming-Algorithm
.
Processes,
Journal Year:
2022,
Volume and Issue:
10(12), P. 2703 - 2703
Published: Dec. 14, 2022
Aquila
Optimizer
(AO)
and
Artificial
Rabbits
Optimization
(ARO)
are
two
recently
developed
meta-heuristic
optimization
algorithms.
Although
AO
has
powerful
exploration
capability,
it
still
suffers
from
poor
solution
accuracy
premature
convergence
when
addressing
some
complex
cases
due
to
the
insufficient
exploitation
phase.
In
contrast,
ARO
possesses
very
competitive
potential,
but
its
ability
needs
be
more
satisfactory.
To
ameliorate
above-mentioned
limitations
in
a
single
algorithm
achieve
better
overall
performance,
this
paper
proposes
novel
chaotic
opposition-based
learning-driven
hybrid
called
CHAOARO.
Firstly,
global
phase
of
is
combined
with
local
maintain
respective
valuable
search
capabilities.
Then,
an
adaptive
switching
mechanism
(ASM)
designed
balance
procedures.
Finally,
we
introduce
learning
(COBL)
strategy
avoid
fall
into
optima.
comprehensively
verify
effectiveness
superiority
proposed
work,
CHAOARO
compared
original
AO,
ARO,
several
state-of-the-art
algorithms
on
23
classical
benchmark
functions
IEEE
CEC2019
test
suite.
Systematic
comparisons
demonstrate
that
can
significantly
outperform
other
competitor
methods
terms
accuracy,
speed,
robustness.
Furthermore,
promising
prospect
real-world
applications
highlighted
by
resolving
five
industrial
engineering
design
problems
photovoltaic
(PV)
model
parameter
identification
problem.
Journal of Computational Design and Engineering,
Journal Year:
2022,
Volume and Issue:
10(1), P. 329 - 356
Published: Dec. 14, 2022
Abstract
The
African
vultures
optimization
algorithm
(AVOA)
is
a
recently
proposed
metaheuristic
inspired
by
the
vultures’
behaviors.
Though
basic
AVOA
performs
very
well
for
most
problems,
it
still
suffers
from
shortcomings
of
slow
convergence
rate
and
local
optimal
stagnation
when
solving
complex
tasks.
Therefore,
this
study
introduces
modified
version
named
enhanced
(EAVOA).
EAVOA
uses
three
different
techniques
namely
representative
vulture
selection
strategy,
rotating
flight
selecting
accumulation
mechanism,
respectively,
which
are
developed
based
on
AVOA.
strategy
strikes
good
balance
between
global
searches.
mechanism
utilized
to
improve
quality
solution.
performance
validated
23
classical
benchmark
functions
with
various
types
dimensions
compared
those
nine
other
state-of-the-art
methods
according
numerical
results
curves.
In
addition,
real-world
engineering
design
problems
adopted
evaluate
practical
applicability
EAVOA.
Furthermore,
has
been
applied
classify
multi-layer
perception
using
XOR
cancer
datasets.
experimental
clearly
show
that
superiority
over
methods.
