IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 31589 - 31604
Published: Jan. 1, 2024
Confronted
with
the
challenges
posed
by
climate
change
and
ongoing
energy
transition,
solar
is
one
of
important
new
sources,
tower
power
plant
has
become
an
innovative
solution
to
promote
clean
development.
The
optimization
heliostat
field
layout
constitutes
a
crucial
aspect
in
enhancing
operational
efficiency
concentrated
plant.
Currently,
garnered
widespread
attention.
In
this
paper,
we
propose
swarm
algorithm
niching
elite
competition
called
NECSO
solve
large-scale
optimization.
First,
aiming
increase
diversity
heterogeneity
within
population,
employ
random
grouping
strategy
partition
population
into
distinct
sub-swarms.
Then,
design
mechanism
harmonize
performance
exploration.
carried
out
any
sub-swarm
enhance
explorability
particles.
occurs
between
elites
which
select
from
each
improve
convergence
Additionally,
develop
mathematical
model
for
layout.
This
employs
currently
advanced
computational
methods,
facilitating
prompt
precise
calculation
optical
To
evaluate
NECSO,
15
practical
cases
varying
scales.
And
then,
conduct
comparative
experiments
eight
mainstream
excellent
algorithms.
results
indicate
that
exhibits
competitive
solving
optimization,
particularly
cases.
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
