Modelling and Simulation in Engineering,
Journal Year:
2025,
Volume and Issue:
2025(1)
Published: Jan. 1, 2025
The
synthesis
problem
of
the
number
array
elements,
element
spacing,
and
formation
is
widely
concerned
in
sparse
optimization.
local
optimum
still
an
urgent
to
be
solved
existing
optimization
algorithms.
A
algorithm
on
improved
sparrow
search
(ISSA)
proposed
this
paper.
Firstly,
a
probabilistic
following
strategy
optimize
(SSA),
it
can
improve
global
capability
algorithm.
Secondly,
adaptive
Cauchy–Gaussian
mutation
are
used
avoid
falling
into
situation,
more
high‐quality
areas
searched
extremum
escape
ability
convergence
performance
Finally,
peak
sidelobe
level
(PSLL)
as
fitness
function
adaptively
position
elements.
Experimental
simulations
show
that
approach
has
good
main
lobe
response
low
response.
In
planar
array,
decreases
by
−1.41
dB
compared
with
genetic
(GA)
0.69
lower
than
SSA.
linear
−1.09
differential
evolution
0.40
arrays
significantly
enhances
accuracy
robustness
antenna
error
estimation.
Processes,
Journal Year:
2023,
Volume and Issue:
11(2), P. 498 - 498
Published: Feb. 7, 2023
In
this
paper,
an
improved
gradient-based
optimizer
(IGBO)
is
proposed
with
the
target
of
improving
performance
and
accuracy
algorithm
for
solving
complex
optimization
engineering
problems.
The
IGBO
has
added
features
adjusting
best
solution
by
adding
inertia
weight,
fast
convergence
rate
modified
parameters,
as
well
avoiding
local
optima
using
a
novel
functional
operator
(G).
These
make
it
feasible
majority
nonlinear
problems
which
quite
hard
to
achieve
original
version
GBO.
effectiveness
scalability
are
evaluated
well-known
benchmark
functions.
Moreover,
statistically
analyzed
ANOVA
analysis,
Holm–Bonferroni
test.
addition,
was
assessed
real-world
results
functions
show
that
very
competitive,
superior
compared
its
competitors
in
finding
optimal
solutions
high
coverage.
studied
real
prove
superiority
difficult
indefinite
search
domains.
Entropy,
Journal Year:
2023,
Volume and Issue:
25(1), P. 178 - 178
Published: Jan. 16, 2023
Multi-level
thresholding
image
segmentation
divides
an
into
multiple
regions
of
interest
and
is
a
key
step
in
processing
analysis.
Aiming
toward
the
problems
low
accuracy
slow
convergence
speed
traditional
multi-level
threshold
methods,
this
paper,
we
present
based
on
improved
slime
mould
algorithm
(ISMA)
symmetric
cross-entropy
for
global
optimization
tasks.
First,
elite
opposition-based
learning
(EOBL)
was
used
to
improve
quality
diversity
initial
population
accelerate
speed.
The
adaptive
probability
adjust
selection
enhance
ability
jump
out
local
optimum.
historical
leader
strategy,
which
selects
optimal
information
as
position
update,
found
accuracy.
Subsequently,
14
benchmark
functions
were
evaluate
performance
ISMA,
comparing
it
with
other
well-known
algorithms
terms
accuracy,
speed,
significant
differences.
tested
method
proposed
paper
eight
grayscale
images
compared
criteria
algorithms.
experimental
metrics
include
average
fitness
(mean),
standard
deviation
(std),
peak
signal
noise
ratio
(PSNR),
structure
similarity
index
(SSIM),
feature
(FSIM),
utilized
segmentation.
results
demonstrated
that
superior
algorithms,
can
be
effectively
applied
task
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 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.
Journal of Computational Design and Engineering,
Journal Year:
2022,
Volume and Issue:
9(6), P. 2196 - 2234
Published: Sept. 13, 2022
Abstract
Salp
swarm
algorithm
(SSA)
is
a
well-established
population-based
optimizer
that
exhibits
strong
exploration
ability,
but
slow
convergence
and
poor
exploitation
capability.
In
this
paper,
an
endeavour
made
to
enhance
the
performance
of
basic
SSA.
The
new
upgraded
version
SSA
named
as
‘adaptive
strategy-based
(ABSSA)
algorithm’
proposed
in
paper.
First,
exploratory
scope
food
source
navigating
commands
are
enriched
using
inertia
weight
boosted
global
best-guided
mechanism.
Next,
novel
velocity
clamping
strategy
designed
efficiently
stabilize
balance
between
operations.
addition,
adaptive
conversion
parameter
tactic
modify
position
update
equation
effectively
intensify
local
competency
solution
accuracy.
effectiveness
ABSSA
verified
by
series
problems,
including
23
classical
benchmark
functions,
29
complex
optimization
problems
from
CEC
2017,
five
engineering
design
tasks.
experimental
results
show
developed
approach
performs
significantly
better
than
standard
other
competitors.
Moreover,
implemented
handle
path
planning
obstacle
avoidance
(PPOA)
tasks
autonomous
mobile
robots
compared
with
some
intelligent
approach-based
planners.
indicate
ABSSA-based
PPOA
method
reliable
algorithm.
Quality and Reliability Engineering International,
Journal Year:
2024,
Volume and Issue:
40(4), P. 1502 - 1525
Published: Jan. 23, 2024
Abstract
The
pivotal
problem
in
reliability
analysis
is
how
to
use
as
few
actual
assessments
possible
obtain
an
accurate
failure
probability.
Although
adaptive
Kriging
provides
a
viable
method
address
this
problem,
unsatisfied
surrogate
accuracy
and
modeling
samples
often
lead
unacceptable
computing
burden.
In
paper,
optimized
combining
efficient
sampling
(AOK‐ES)
proposed:
first,
enhance
the
approximation
ability,
high‐fidelity
model
(OKM)
established;
further,
ensure
quality
of
OKM
calculation,
improved
Latin
hypercube
importance
approach
are
developed
correspondingly.
Six
different
types
case
studies
demonstrate
superiority
proposed
AOK‐ES.
results
that
AOK‐ES
holds
potential
reduce
cost
while
ensuring
accuracy.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(6)
Published: May 9, 2024
Abstract
In
this
study,
the
Learning
Search
Algorithm
(LSA)
is
introduced
as
an
innovative
optimization
algorithm
that
draws
inspiration
from
swarm
intelligence
principles
and
mimics
social
learning
behavior
observed
in
humans.
The
LSA
optimizes
search
process
by
integrating
historical
experience
real-time
information,
enabling
it
to
effectively
navigate
complex
problem
spaces.
By
doing
so,
enhances
its
global
development
capability
provides
efficient
solutions
challenging
tasks.
Additionally,
improves
collective
capacity
incorporating
teaching
active
behaviors
within
population,
leading
improved
local
capabilities.
Furthermore,
a
dynamic
adaptive
control
factor
utilized
regulate
algorithm’s
exploration
abilities.
proposed
rigorously
evaluated
using
40
benchmark
test
functions
IEEE
CEC
2014
2020,
compared
against
nine
established
evolutionary
algorithms
well
11
recently
algorithms.
experimental
results
demonstrate
superiority
of
algorithm,
achieves
top
rank
Friedman
rank-sum
test,
highlighting
power
competitiveness.
Moreover,
successfully
applied
solve
six
real-world
engineering
problems
15
UCI
datasets
feature
selection
problems,
showcasing
significant
advantages
potential
for
practical
applications
problems.
