Journal of Marine Science and Engineering,
Journal Year:
2024,
Volume and Issue:
12(7), P. 1167 - 1167
Published: July 11, 2024
The
path
planning
problem
is
an
important
issue
in
maritime
search
and
rescue.
This
paper
models
the
as
a
dynamic
vehicle
routing
problem.
It
first
designs
generator
that
transforms
existing
benchmark
sets
for
static
into
scenarios.
Subsequently,
it
proposes
effective
Dynamic
Ant
Colony
Optimization
(DACO)
algorithm,
whose
novelty
lies
dynamically
adjusts
pheromone
matrix
to
efficiently
handle
customers’
changes.
Moreover,
DACO
incorporates
simulated
annealing
increase
population
diversity
employs
local
operator
dedicated
route
modification
continuous
performance
maximization
of
route.
experimental
results
demonstrated
proposed
outperformed
approaches
generating
better
routes
across
various
sets.
Specifically,
achieved
significant
improvements
cost,
serviced
customer
quantity,
adherence
time
window
requirements.
These
highlight
superiority
problem,
providing
solution
similar
problems.
Artificial Intelligence Review,
Journal Year:
2025,
Volume and Issue:
58(3)
Published: Jan. 6, 2025
Abstract
Numerical
optimization
and
point
cloud
registration
are
critical
research
topics
in
the
field
of
artificial
intelligence.
The
differential
evolution
algorithm
is
an
effective
approach
to
address
these
problems,
LSHADE-SPACMA,
winning
CEC2017,
a
competitive
variant.
However,
LSHADE-SPACMA’s
local
exploitation
capability
can
sometimes
be
insufficient
when
handling
challenges.
Therefore,
this
work,
we
propose
modified
version
LSHADE-SPACMA
(mLSHADE-SPACMA)
for
numerical
registration.
Compared
original
approach,
work
presents
three
main
innovations.
First,
present
precise
elimination
generation
mechanism
enhance
algorithm’s
ability.
Second,
introduce
mutation
strategy
based
on
semi-parametric
adaptive
rank-based
selective
pressure,
which
improves
evolutionary
direction.
Third,
elite-based
external
archiving
mechanism,
ensures
diversity
population
accelerate
convergence
progress.
Additionally,
utilize
CEC2014
(Dim
=
10,
30,
50,
100)
CEC2017
test
suites
experiments,
comparing
our
against:
(1)
10
recent
CEC
winner
algorithms,
including
LSHADE,
EBOwithCMAR,
jSO,
LSHADE-cnEpSin,
HSES,
LSHADE-RSP,
ELSHADE-SPACMA,
EA4eig,
L-SRTDE,
LSHADE-SPACMA;
(2)
4
advanced
variants:
APSM-jSO,
LensOBLDE,
ACD-DE,
MIDE.
results
Wilcoxon
signed-rank
Friedman
mean
rank
demonstrate
that
mLSHADE-SPACMA
not
only
outperforms
but
also
surpasses
other
high-performance
optimizers,
except
it
inferior
L-SRTDE
CEC2017.
Finally,
25
cases
from
Fast
Global
Registration
dataset
applied
simulation
analysis
potential
developed
technique
solving
practical
problems.
code
available
at
https://github.com/ShengweiFu?tab=repositories
https://ww2.mathworks.cn/matlabcentral/fileexchange/my-file-exchange
