Heliyon,
Journal Year:
2024,
Volume and Issue:
10(16), P. e35595 - e35595
Published: Aug. 1, 2024
Providing
accurate
prediction
of
the
severity
traffic
collisions
is
vital
to
improve
efficiency
emergencies
and
reduce
casualties,
accordingly
improving
safety
reducing
congestion.
However,
issue
both
predictive
accuracy
model
interpretability
predicted
outcomes
has
remained
a
persistent
challenge.
We
propose
Random
Forest
optimized
by
Meta-heuristic
algorithm
framework
that
integrates
spatiotemporal
characteristics
crashes.
Through
analysis
motor
vehicle
crash
data
on
interstate
highways
within
United
States
in
2020,
we
compared
various
ensemble
models
single-classification
models.
The
results
show
(RF)
Crown
Porcupine
Optimizer
(CPO)
best
results,
accuracy,
recall,
f1
score,
precision
can
reach
more
than
90
%.
found
factors
such
as
Temperature
Weather
are
closely
related
Closely
indicators
were
analyzed
interpretatively
using
geographic
information
system
(GIS)
based
characteristic
importance
ranking
results.
enables
crashes
discovers
important
leading
with
an
explanation.
study
proposes
some
areas
consideration
should
be
given
adding
measures
nighttime
lighting
devices
fatigue
driving
alert
ensure
safe
driving.
It
offers
references
for
policymakers
address
management
urban
development
issues.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(5)
Published: April 23, 2024
Abstract
This
study
introduces
a
novel
population-based
metaheuristic
algorithm
called
secretary
bird
optimization
(SBOA),
inspired
by
the
survival
behavior
of
birds
in
their
natural
environment.
Survival
for
involves
continuous
hunting
prey
and
evading
pursuit
from
predators.
information
is
crucial
proposing
new
that
utilizes
abilities
to
address
real-world
problems.
The
algorithm's
exploration
phase
simulates
snakes,
while
exploitation
models
escape
During
this
phase,
observe
environment
choose
most
suitable
way
reach
secure
refuge.
These
two
phases
are
iteratively
repeated,
subject
termination
criteria,
find
optimal
solution
problem.
To
validate
performance
SBOA,
experiments
were
conducted
assess
convergence
speed,
behavior,
other
relevant
aspects.
Furthermore,
we
compared
SBOA
with
15
advanced
algorithms
using
CEC-2017
CEC-2022
benchmark
suites.
All
test
results
consistently
demonstrated
outstanding
terms
quality,
stability.
Lastly,
was
employed
tackle
12
constrained
engineering
design
problems
perform
three-dimensional
path
planning
Unmanned
Aerial
Vehicles.
demonstrate
that,
contrasted
optimizers,
proposed
can
better
solutions
at
faster
pace,
showcasing
its
significant
potential
addressing
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Dec. 30, 2024
The
study
suggests
a
better
multi-objective
optimization
method
called
2-Archive
Multi-Objective
Cuckoo
Search
(MOCS2arc).
It
is
then
used
to
improve
eight
classical
truss
structures
and
six
ZDT
test
functions.
aims
minimize
both
mass
compliance
simultaneously.
MOCS2arc
an
advanced
version
of
the
traditional
(MOCS)
algorithm,
enhanced
through
dual
archive
strategy
that
significantly
improves
solution
diversity
performance.
To
evaluate
effectiveness
MOCS2arc,
we
conducted
extensive
comparisons
with
several
established
algorithms:
MOSCA,
MODA,
MOWHO,
MOMFO,
MOMPA,
NSGA-II,
DEMO,
MOCS.
Such
comparison
has
been
made
various
performance
metrics
compare
benchmark
efficacy
proposed
algorithm.
These
comprehensively
assess
algorithms'
abilities
generate
diverse
optimal
solutions.
statistical
results
demonstrate
superior
evidenced
by
Additionally,
Friedman's
&
Wilcoxon's
corroborate
finding
consistently
delivers
compared
others.
show
highly
effective
improved
algorithm
for
structure
optimization,
offering
significant
promising
improvements
over
existing
methods.
This
article
introduces
a
novel
nature-inspired
metaheuristic
algorithm
called
Frilled
Lizard
Optimization
(FLO),
which
emulates
the
hunting
behavior
of
frilled
lizards
in
their
natural
habitat.
FLO
draws
in-spiration
from
sit-and-wait
strategy
observed
during
hunting.
The
underlying
theory
is
presented
and
mathematically
formulated
two
phases:
(i)
an
exploration
phase,
simulating
lizard's
attack
towards
prey,
(ii)
exploitation
retreat
to
top
tree
after
feeding.
To
assess
FLO's
efficacy
solving
optimization
problems,
algorithm's
performance
evaluated
across
fifty-two
standard
benchmark
functions,
encompassing
unimodal,
high-dimensional
multimodal,
fixed-dimensional
CEC
2017
test
suite.
Comparative
analyses
with
twelve
existing
algorithms
are
conducted.
simulation
results
reveal
that
FLO,
distinguished
by
its
adeptness
exploration,
exploitation,
balancing
them
search
process,
outperforms
competing
algorithms.
Additionally,
implemented
on
twenty-two
constrained
problems
2011
suite
four
engineering
design
demonstrating
effectiveness
addressing
real-world
applications.
