Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 19, 2024
The
Whale
Optimization
Algorithm
(WOA)
is
regarded
as
a
classic
metaheuristic
algorithm,
yet
it
suffers
from
limited
population
diversity,
imbalance
between
exploitation
and
exploration,
low
solution
accuracy.
In
this
paper,
we
propose
the
Spiral-Enhanced
(SEWOA),
which
incorporates
nonlinear
time-varying
self-adaptive
perturbation
strategy
an
Archimedean
spiral
structure
into
original
WOA.
enhances
diversity
of
space,
aiding
algorithm
in
escaping
local
optima.
optimization
dynamic
improves
algorithm's
search
capability
effectiveness
proposed
validated
multiple
perspectives
using
CEC2014
test
functions,
CEC2017
23
benchmark
functions.
experimental
results
demonstrate
that
enhanced
significantly
balances
global
search,
Additionally,
SEWOA
exhibits
excellent
performance
solving
three
engineering
design
problems,
showcasing
its
value
wide
range
potential
applications.
Sensors,
Год журнала:
2023,
Номер
23(8), С. 3988 - 3988
Опубликована: Апрель 14, 2023
An
improved
whale
optimization
algorithm
is
proposed
to
solve
the
problems
of
original
in
indoor
robot
path
planning,
which
has
slow
convergence
speed,
poor
finding
ability,
low
efficiency,
and
easily
prone
falling
into
local
shortest
problem.
First,
an
logistic
chaotic
mapping
applied
enrich
initial
population
whales
improve
global
search
capability
algorithm.
Second,
a
nonlinear
factor
introduced,
equilibrium
parameter
A
changed
balance
capabilities
efficiency.
Finally,
fused
Corsi
variance
weighting
strategy
perturbs
location
quality.
The
logical
(ILWOA)
compared
with
WOA
four
other
algorithms
through
eight
test
functions
three
raster
map
environments
for
experiments.
results
show
that
ILWOA
better
merit-seeking
ability
function.
In
planning
experiments,
are
than
when
comparing
evaluation
criteria,
verifies
quality,
robustness
improved.
Neural Computing and Applications,
Год журнала:
2024,
Номер
36(27), С. 16873 - 16897
Опубликована: Июнь 2, 2024
Abstract
The
artificial
hummingbird
algorithm
(AHA)
has
been
applied
in
various
fields
of
science
and
provided
promising
solutions.
Although
the
demonstrated
merits
optimization
area,
it
suffers
from
local
optimum
stagnation
poor
exploration
search
space.
To
overcome
these
drawbacks,
this
study
redesigns
update
mechanism
original
AHA
with
natural
survivor
method
(NSM)
proposes
a
novel
metaheuristic
called
NSM-AHA.
strength
developed
is
that
performs
population
management
not
only
according
to
fitness
function
value
but
also
NSM
score
value.
adopted
strategy
contributes
NSM-AHA
exhibiting
powerful
avoidance
unique
ability.
ability
proposed
was
compared
21
state-of-the-art
algorithms
over
CEC
2017
2020
benchmark
functions
dimensions
30,
50,
100,
respectively.
Based
on
Friedman
test
results,
observed
ranked
1st
out
22
competitive
algorithms,
while
8th.
This
result
highlights
provides
remarkable
evolution
convergence
performance
algorithm.
Furthermore,
two
constrained
engineering
problems
including
single-diode
solar
cell
model
(SDSCM)
parameters
design
power
system
stabilizer
(PSS)
are
solved
better
results
other
9.86E
−
04
root
mean
square
error
for
SDSCM
1.43E
03
integral
time
PSS.
experimental
showed
optimizer
solving
global
problems.
Heliyon,
Год журнала:
2024,
Номер
10(14), С. e34496 - e34496
Опубликована: Июль 1, 2024
The
grey
wolf
optimizer
is
a
widely
used
parametric
optimization
algorithm.
It
affected
by
the
structure
and
rank
of
wolves
prone
to
falling
into
local
optimum.
In
this
study,
we
propose
for
fusion
cell-like
P
systems.
Cell-like
systems
can
parallelize
computation
communicate
from
cell
membrane
membrane,
which
help
jump
out
Design
new
convergence
factors
use
different
in
other
membranes
balance
overall
exploration
utilization
capabilities
At
same
time,
dynamic
weights
are
introduced
accelerate
speed
Experiments
performed
on
24
test
functions
verify
their
global
performance.
Meanwhile,
support
vector
machine
model
optimized
has
been
developed
tested
six
benchmark
datasets.
Finally,
optimizing
ability
constrained
problems
verified
three
real
engineering
design
problems.
Compared
with
algorithms,
obtains
higher
accuracy
faster
function,
at
it
find
better
parameter
set
stably
parameters,
addition
being
more
competitive
results
show
that
improves
searching
population,
optimum,
speed,
stability.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 19, 2024
The
Whale
Optimization
Algorithm
(WOA)
is
regarded
as
a
classic
metaheuristic
algorithm,
yet
it
suffers
from
limited
population
diversity,
imbalance
between
exploitation
and
exploration,
low
solution
accuracy.
In
this
paper,
we
propose
the
Spiral-Enhanced
(SEWOA),
which
incorporates
nonlinear
time-varying
self-adaptive
perturbation
strategy
an
Archimedean
spiral
structure
into
original
WOA.
enhances
diversity
of
space,
aiding
algorithm
in
escaping
local
optima.
optimization
dynamic
improves
algorithm's
search
capability
effectiveness
proposed
validated
multiple
perspectives
using
CEC2014
test
functions,
CEC2017
23
benchmark
functions.
experimental
results
demonstrate
that
enhanced
significantly
balances
global
search,
Additionally,
SEWOA
exhibits
excellent
performance
solving
three
engineering
design
problems,
showcasing
its
value
wide
range
potential
applications.
