Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Март 20, 2024
Abstract
The
RIME
optimization
algorithm
(RIME)
represents
an
advanced
technique.
However,
it
suffers
from
issues
such
as
slow
convergence
speed
and
susceptibility
to
falling
into
local
optima.
In
response
these
shortcomings,
we
propose
a
multi-strategy
enhanced
version
known
the
improved
(MIRIME).
Firstly,
Tent
chaotic
map
is
utilized
initialize
population,
laying
groundwork
for
global
optimization.
Secondly,
introduce
adaptive
update
strategy
based
on
leadership
dynamic
centroid,
facilitating
swarm's
exploitation
in
more
favorable
direction.
To
address
problem
of
population
scarcity
later
iterations,
lens
imaging
opposition-based
learning
control
introduced
enhance
diversity
ensure
accuracy.
proposed
centroid
boundary
not
only
limits
search
boundaries
individuals
but
also
effectively
enhances
algorithm's
focus
efficiency.
Finally,
demonstrate
performance
MIRIME,
employ
30
CEC2017
test
functions
compare
with
11
popular
algorithms
across
different
dimensions,
verifying
its
effectiveness.
Additionally,
assess
method's
practical
feasibility,
apply
MIRIME
solve
three-dimensional
path
planning
unmanned
surface
vehicles.
Experimental
results
indicate
that
outperforms
other
competing
terms
solution
quality
stability,
highlighting
superior
application
potential.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Июнь 1, 2024
The
RIME
optimization
algorithm
(RIME)
represents
an
advanced
technique.
However,
it
suffers
from
issues
such
as
slow
convergence
speed
and
susceptibility
to
falling
into
local
optima.
In
response
these
shortcomings,
we
propose
a
multi-strategy
enhanced
version
known
the
improved
(MIRIME).
Firstly,
Tent
chaotic
map
is
utilized
initialize
population,
laying
groundwork
for
global
optimization.
Secondly,
introduce
adaptive
update
strategy
based
on
leadership
dynamic
centroid,
facilitating
swarm's
exploitation
in
more
favorable
direction.
To
address
problem
of
population
scarcity
later
iterations,
lens
imaging
opposition-based
learning
control
introduced
enhance
diversity
ensure
accuracy.
proposed
centroid
boundary
not
only
limits
search
boundaries
individuals
but
also
effectively
enhances
algorithm's
focus
efficiency.
Finally,
demonstrate
performance
MIRIME,
employ
CEC
2017
2022
test
suites
compare
with
11
popular
algorithms
across
different
dimensions,
verifying
its
effectiveness.
Additionally,
assess
method's
practical
feasibility,
apply
MIRIME
solve
three-dimensional
path
planning
unmanned
surface
vehicles.
Experimental
results
indicate
that
outperforms
other
competing
terms
solution
quality
stability,
highlighting
superior
application
potential.
Biomimetics,
Год журнала:
2024,
Номер
9(11), С. 701 - 701
Опубликована: Ноя. 15, 2024
Feature
selection
(FS)
constitutes
a
critical
stage
within
the
realms
of
machine
learning
and
data
mining,
with
objective
eliminating
irrelevant
features
while
guaranteeing
model
accuracy.
Nevertheless,
in
datasets
featuring
multitude
features,
choosing
optimal
feature
poses
significant
challenge.
This
study
presents
an
enhanced
Sand
Cat
Swarm
Optimization
algorithm
(MSCSO)
to
improve
process,
augmenting
algorithm's
global
search
capacity
convergence
rate
via
multiple
innovative
strategies.
Specifically,
this
devised
logistic
chaotic
mapping
lens
imaging
reverse
approaches
for
population
initialization
enhance
diversity;
balanced
exploration
local
development
capabilities
through
nonlinear
parameter
processing;
introduced
Weibull
flight
strategy
triangular
parade
optimize
individual
position
updates.
Additionally,
Gaussian-Cauchy
mutation
was
employed
ability
overcome
optima.
The
experimental
results
demonstrate
that
MSCSO
performs
well
on
65.2%
test
functions
CEC2005
benchmark
test;
15
UCI,
achieved
best
average
fitness
93.3%
fewest
selections
86.7%
attaining
accuracy
across
100%
datasets,
significantly
outperforming
other
comparative
algorithms.
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Март 20, 2024
Abstract
The
RIME
optimization
algorithm
(RIME)
represents
an
advanced
technique.
However,
it
suffers
from
issues
such
as
slow
convergence
speed
and
susceptibility
to
falling
into
local
optima.
In
response
these
shortcomings,
we
propose
a
multi-strategy
enhanced
version
known
the
improved
(MIRIME).
Firstly,
Tent
chaotic
map
is
utilized
initialize
population,
laying
groundwork
for
global
optimization.
Secondly,
introduce
adaptive
update
strategy
based
on
leadership
dynamic
centroid,
facilitating
swarm's
exploitation
in
more
favorable
direction.
To
address
problem
of
population
scarcity
later
iterations,
lens
imaging
opposition-based
learning
control
introduced
enhance
diversity
ensure
accuracy.
proposed
centroid
boundary
not
only
limits
search
boundaries
individuals
but
also
effectively
enhances
algorithm's
focus
efficiency.
Finally,
demonstrate
performance
MIRIME,
employ
30
CEC2017
test
functions
compare
with
11
popular
algorithms
across
different
dimensions,
verifying
its
effectiveness.
Additionally,
assess
method's
practical
feasibility,
apply
MIRIME
solve
three-dimensional
path
planning
unmanned
surface
vehicles.
Experimental
results
indicate
that
outperforms
other
competing
terms
solution
quality
stability,
highlighting
superior
application
potential.
