Knowledge-Based Systems,
Год журнала:
2024,
Номер
295, С. 111737 - 111737
Опубликована: Апрель 12, 2024
This
study
proposes
a
novel
artificial
protozoa
optimizer
(APO)
that
is
inspired
by
in
nature.
The
APO
mimics
the
survival
mechanisms
of
simulating
their
foraging,
dormancy,
and
reproductive
behaviors.
was
mathematically
modeled
implemented
to
perform
optimization
processes
metaheuristic
algorithms.
performance
verified
via
experimental
simulations
compared
with
32
state-of-the-art
Wilcoxon
signed-rank
test
performed
for
pairwise
comparisons
proposed
algorithms,
Friedman
used
multiple
comparisons.
First,
tested
using
12
functions
2022
IEEE
Congress
on
Evolutionary
Computation
benchmark.
Considering
practicality,
solve
five
popular
engineering
design
problems
continuous
space
constraints.
Moreover,
applied
multilevel
image
segmentation
task
discrete
experiments
confirmed
could
provide
highly
competitive
results
problems.
source
codes
Artificial
Protozoa
Optimizer
are
publicly
available
at
https://seyedalimirjalili.com/projects
https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.
Frontiers in Mechanical Engineering,
Год журнала:
2023,
Номер
8
Опубликована: Янв. 20, 2023
This
paper
introduces
a
new
metaheuristic
algorithm
named
the
Osprey
Optimization
Algorithm
(OOA),
which
imitates
behavior
of
osprey
in
nature.
The
fundamental
inspiration
OOA
is
strategy
ospreys
when
hunting
fish
from
seas.
In
this
strategy,
hunts
prey
after
detecting
its
position,
then
carries
it
to
suitable
position
eat
it.
proposed
approach
two
phases
exploration
and
exploitation
mathematically
modeled
based
on
simulation
natural
during
process.
performance
has
been
evaluated
optimization
twenty-nine
standard
benchmark
functions
CEC
2017
test
suite.
Furthermore,
compared
with
twelve
well-known
algorithms.
results
show
that
provided
superior
competitor
algorithms
by
maintaining
balance
between
exploitation.
addition,
implementation
twenty-two
real-world
constrained
problems
2011
suite
shows
high
capability
optimizing
applications.
Biomimetics,
Год журнала:
2023,
Номер
8(2), С. 149 - 149
Опубликована: Апрель 6, 2023
This
paper
presents
a
new
evolutionary-based
approach
called
Subtraction-Average-Based
Optimizer
(SABO)
for
solving
optimization
problems.
The
fundamental
inspiration
of
the
proposed
SABO
is
to
use
subtraction
average
searcher
agents
update
position
population
members
in
search
space.
different
steps
SABO's
implementation
are
described
and
then
mathematically
modeled
tasks.
performance
tested
fifty-two
standard
benchmark
functions,
consisting
unimodal,
high-dimensional
multimodal,
fixed-dimensional
multimodal
types,
CEC
2017
test
suite.
results
show
that
effectively
solves
problems
by
balancing
exploration
exploitation
process
problem-solving
compared
with
twelve
well-known
metaheuristic
algorithms.
analysis
simulation
shows
provides
superior
most
functions.
Furthermore,
it
much
more
competitive
outstanding
than
its
competitor
Additionally,
implemented
four
engineering
design
evaluate
handling
tasks
real-world
applications.
can
solve
applications
optimal
designs
Artificial Intelligence Review,
Год журнала:
2024,
Номер
57(4)
Опубликована: Март 23, 2024
Abstract
This
paper
innovatively
proposes
the
Black
Kite
Algorithm
(BKA),
a
meta-heuristic
optimization
algorithm
inspired
by
migratory
and
predatory
behavior
of
black
kite.
The
BKA
integrates
Cauchy
mutation
strategy
Leader
to
enhance
global
search
capability
convergence
speed
algorithm.
novel
combination
achieves
good
balance
between
exploring
solutions
utilizing
local
information.
Against
standard
test
function
sets
CEC-2022
CEC-2017,
as
well
other
complex
functions,
attained
best
performance
in
66.7,
72.4
77.8%
cases,
respectively.
effectiveness
is
validated
through
detailed
analysis
statistical
comparisons.
Moreover,
its
application
solving
five
practical
engineering
design
problems
demonstrates
potential
addressing
constrained
challenges
real
world
indicates
that
it
has
significant
competitive
strength
comparison
with
existing
techniques.
In
summary,
proven
value
advantages
variety
due
excellent
performance.
source
code
publicly
available
at
https://www.mathworks.com/matlabcentral/fileexchange/161401-black-winged-kite-algorithm-bka
.
