Biomimetics,
Год журнала:
2023,
Номер
8(4), С. 377 - 377
Опубликована: Авг. 18, 2023
With
the
rapid
development
of
geometric
modeling
industry
and
computer
technology,
design
shape
optimization
complex
curve
shapes
have
now
become
a
very
important
research
topic
in
CAGD.
In
this
paper,
Hybrid
Artificial
Hummingbird
Algorithm
(HAHA)
is
used
to
optimize
composite
shape-adjustable
generalized
cubic
Ball
(CSGC–Ball,
for
short)
curves.
Firstly,
algorithm
(AHA),
as
newly
proposed
meta-heuristic
algorithm,
has
advantages
simple
structure
easy
implementation
can
quickly
find
global
optimal
solution.
However,
there
are
still
limitations,
such
low
convergence
accuracy
tendency
fall
into
local
optimization.
Therefore,
paper
proposes
HAHA
based
on
original
AHA,
combined
with
elite
opposition-based
learning
strategy,
PSO,
Cauchy
mutation,
increase
population
diversity
avoid
falling
optimization,
thus
improve
rate
AHA.
Twenty-five
benchmark
test
functions
CEC
2022
suite
evaluate
overall
performance
HAHA,
experimental
results
statistically
analyzed
using
Friedman
Wilkerson
rank
sum
tests.
The
show
that,
compared
other
advanced
algorithms,
good
competitiveness
practicality.
Secondly,
order
better
realize
curves
engineering,
CSGC–Ball
parameters
constructed
SGC–Ball
basis
functions.
By
changing
parameters,
whole
or
be
adjusted
more
flexibly.
Finally,
make
ideal
shape,
curve-shape
model
established
minimum
energy
value,
solve
model.
Two
representative
numerical
examples
comprehensively
verify
effectiveness
superiority
solving
problems.
International Journal of Computational Intelligence Systems,
Год журнала:
2023,
Номер
16(1)
Опубликована: Июнь 16, 2023
Abstract
Meta-Heuristic
(MH)
algorithms
have
recently
proven
successful
in
a
broad
range
of
applications
because
their
strong
capabilities
picking
the
optimal
features
and
removing
redundant
irrelevant
features.
Artificial
Ecosystem-based
Optimization
(AEO)
shows
extraordinary
ability
exploration
stage
poor
exploitation
its
stochastic
nature.
Dwarf
Mongoose
Algorithm
(DMOA)
is
recent
MH
algorithm
showing
high
capability.
This
paper
proposes
AEO-DMOA
Feature
Selection
(FS)
by
integrating
AEO
DMOA
to
develop
an
efficient
FS
with
better
equilibrium
between
exploitation.
The
performance
investigated
on
seven
datasets
from
different
domains
collection
twenty-eight
global
optimization
functions,
eighteen
CEC2017,
ten
CEC2019
benchmark
functions.
Comparative
study
statistical
analysis
demonstrate
that
gives
competitive
results
statistically
significant
compared
other
popular
approaches.
function
also
indicate
enhanced
high-dimensional
search
space.
Biomimetics,
Год журнала:
2023,
Номер
8(2), С. 191 - 191
Опубликована: Май 4, 2023
Sand
cat
swarm
optimization
algorithm
(SCSO)
keeps
a
potent
and
straightforward
meta-heuristic
derived
from
the
distant
sense
of
hearing
sand
cats,
which
shows
excellent
performance
in
some
large-scale
problems.
However,
SCSO
still
has
several
disadvantages,
including
sluggish
convergence,
lower
convergence
precision,
tendency
to
be
trapped
topical
optimum.
To
escape
these
demerits,
an
adaptive
based
on
Cauchy
mutation
optimal
neighborhood
disturbance
strategy
(COSCSO)
are
provided
this
study.
First
foremost,
introduction
nonlinear
parameter
favor
scaling
up
global
search
helps
retrieve
optimum
colossal
space,
preventing
it
being
caught
Secondly,
operator
perturbs
step,
accelerating
speed
improving
efficiency.
Finally,
diversifies
population,
broadens
enhances
exploitation.
reveal
COSCSO,
was
compared
with
alternative
algorithms
CEC2017
CEC2020
competition
suites.
Furthermore,
COSCSO
is
further
deployed
solve
six
engineering
The
experimental
results
that
strongly
competitive
capable
practical
Alexandria Engineering Journal,
Год журнала:
2023,
Номер
81, С. 469 - 488
Опубликована: Сен. 22, 2023
There
are
many
tricky
optimization
problems
in
real
life,
and
metaheuristic
algorithms
the
most
effective
way
to
solve
at
a
lower
cost.
The
dung
beetle
algorithm
(DBO)
is
more
innovative
proposed
2022,
which
affected
by
action
of
beetles
such
as
ball
rolling,
foraging,
reproduction.
Therefore,
A
based
on
quasi-oppositional
learning
Q-learning
(QOLDBO).
First,
quantum
state
update
idea
cleverly
integrated
into
increase
randomness
generated
population.
And
best
behavior
pattern
selected
adding
rolling
stage
improve
search
effect.
In
addition,
variable
spiral
local
domain
method
make
up
for
shortage
developing
only
around
neighborhood
optimum.
For
optimal
solution
each
iteration,
dimensional
adaptive
Gaussian
variation
retained.
Experimental
performance
tests
show
that
QOLDBO
performs
well
both
benchmark
test
functions
CEC
2017.
Simultaneously,
validity
verified
several
classical
practical
application
engineering
problems.
Alexandria Engineering Journal,
Год журнала:
2023,
Номер
73, С. 543 - 577
Опубликована: Май 11, 2023
Archimedes
Optimization
Algorithm
(AOA)
is
a
new
physics-based
optimizer
that
simulates
principles.
AOA
has
been
used
in
variety
of
real-world
applications
because
potential
properties
such
as
limited
number
control
parameters,
adaptability,
and
changing
the
set
solutions
to
prevent
being
trapped
local
optima.
Despite
wide
acceptance
AOA,
it
some
drawbacks,
assumption
individuals
modify
their
locations
depending
on
altered
densities,
volumes,
accelerations.
This
causes
various
shortcomings
stagnation
into
optimal
regions,
low
diversity
population,
weakness
exploitation
phase,
slow
convergence
curve.
Thus,
specific
region
conventional
may
be
examined
achieve
balance
between
exploration
capabilities
AOA.
The
bird
Swarm
(BSA)
an
efficient
strategy
strong
ability
search
process.
In
this
study,
hybrid
called
AOA-BSA
proposed
overcome
limitations
by
replacing
its
phase
with
BSA
one.
Moreover,
transition
operator
have
high
exploitation.
To
test
examine
performance,
first
experimental
series,
29
unconstrained
functions
from
CEC2017
whereas
series
second
experiments
use
seven
constrained
engineering
problems
AOA-BSA's
handling
issues.
performance
suggested
algorithm
compared
10
optimizers.
These
are
original
algorithms
8
other
algorithms.
experiment's
results
show
effectiveness
optimizing
suite.
AOABSA
outperforms
metaheuristic
across
16
functions.
statically
validated
using
Wilcoxon
Rank
sum.
shows
superior
capability.
due
added
power
integration
not
only
seen
faster
achieved
AOABSA,
but
also
found
For
further
validation
extensive
statistical
analysis
performed
during
process
recording
ratios
problems,
achieves
competitive
curve
reaches
lowest
values
problem.
It
minimum
standard
deviation
which
indicates
robustness
solving
these
problems.
Also,
obtained
counterparts
regarding
problem
variables
behavior
best
values.
Soft Computing,
Год журнала:
2023,
Номер
27(19), С. 13951 - 13989
Опубликована: Июнь 6, 2023
Abstract
A
population-based
optimizer
called
beluga
whale
optimization
(BWO)
depicts
behavioral
patterns
of
water
aerobics,
foraging,
and
diving
whales.
BWO
runs
effectively,
nevertheless
it
retains
numerous
deficiencies
that
has
to
be
strengthened.
Premature
convergence
a
disparity
between
exploitation
exploration
are
some
these
challenges.
Furthermore,
the
absence
transfer
parameter
in
typical
when
moving
from
phase
direct
impact
on
algorithm’s
performance.
This
work
proposes
novel
modified
(mBWO)
incorporates
an
elite
evolution
strategy,
randomization
control
factor,
transition
factor
exploitation.
The
strategy
preserves
top
candidates
for
subsequent
generation
so
helps
generate
effective
solutions
with
meaningful
differences
them
prevent
settling
into
local
maxima.
random
mutation
improves
search
offers
more
crucial
ability
prevents
stagnation
optimum.
mBWO
controlling
algorithm
away
optima
region
during
BWO.
Gaussian
(GM)
acts
initial
position
vector
produce
new
location.
Because
this,
majority
altered
operators
scattered
close
original
position,
which
is
comparable
carrying
out
small
region.
method
can
now
depart
optimal
zone
because
this
modification,
also
increases
optimizer’s
precision
traverses
space
using
placements,
lead
zone.
Transition
(TF)
used
make
transitions
agents
gradually
concerning
amount
time
required.
undergoes
comparison
10
additional
optimizers
29
CEC2017
functions.
Eight
engineering
problems
addressed
by
mBWO,
involving
design
welded
beams,
three-bar
trusses,
tension/compression
springs,
speed
reducers,
best
industrial
refrigeration
systems,
pressure
vessel
challenges,
cantilever
beam
designs,
multi-product
batch
plants.
In
both
constrained
unconstrained
settings,
results
preformed
superior
those
other
methods.