Concurrency and Computation Practice and Experience,
Год журнала:
2025,
Номер
37(3)
Опубликована: Янв. 20, 2025
ABSTRACT
The
differential
evolution
algorithm,
as
a
simple
yet
effective
random
search
often
faces
challenges
in
terms
of
rapid
convergence
and
sharp
decline
population
diversity
during
the
evolutionary
process.
To
address
this
issue,
an
improved
namely
multi‐population
collaboration
(MPC‐DE)
is
introduced
article.
algorithm
proposes
mechanism
two‐stage
mutation
operator.
Through
mechanism,
individuals
involved
effectively
controlled,
enhancing
algorithm's
global
capability.
operator
efficiently
balances
requirements
exploration
exploitation
stages.
Additionally,
perturbation
to
enhance
ability
escape
local
optima
improve
stability.
By
conducting
comprehensive
comparisons
with
15
well‐known
optimization
algorithms
on
CEC2005
CEC2017
test
functions,
MPC‐DE
thoroughly
evaluated
solution
accuracy,
convergence,
stability,
scalability.
Furthermore,
validation
57
real‐world
engineering
problems
CEC2020
demonstrates
robustness
MPC‐DE.
Experimental
results
reveal
that,
compared
other
algorithms,
exhibits
superior
accuracy
both
constrained
unconstrained
problems.
These
research
findings
provide
strong
support
for
widespread
applicability
addressing
practical
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Фев. 29, 2024
Abstract
The
novelty
of
this
article
lies
in
introducing
a
novel
stochastic
technique
named
the
Hippopotamus
Optimization
(HO)
algorithm.
HO
is
conceived
by
drawing
inspiration
from
inherent
behaviors
observed
hippopotamuses,
showcasing
an
innovative
approach
metaheuristic
methodology.
conceptually
defined
using
trinary-phase
model
that
incorporates
their
position
updating
rivers
or
ponds,
defensive
strategies
against
predators,
and
evasion
methods,
which
are
mathematically
formulated.
It
attained
top
rank
115
out
161
benchmark
functions
finding
optimal
value,
encompassing
unimodal
high-dimensional
multimodal
functions,
fixed-dimensional
as
well
CEC
2019
test
suite
2014
dimensions
10,
30,
50,
100
Zigzag
Pattern
suggests
demonstrates
noteworthy
proficiency
both
exploitation
exploration.
Moreover,
it
effectively
balances
exploration
exploitation,
supporting
search
process.
In
light
results
addressing
four
distinct
engineering
design
challenges,
has
achieved
most
efficient
resolution
while
concurrently
upholding
adherence
to
designated
constraints.
performance
evaluation
algorithm
encompasses
various
aspects,
including
comparison
with
WOA,
GWO,
SSA,
PSO,
SCA,
FA,
GOA,
TLBO,
MFO,
IWO
recognized
extensively
researched
metaheuristics,
AOA
recently
developed
algorithms,
CMA-ES
high-performance
optimizers
acknowledged
for
success
IEEE
competition.
According
statistical
post
hoc
analysis,
determined
be
significantly
superior
investigated
algorithms.
source
codes
publicly
available
at
https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho
.
Artificial Intelligence Review,
Год журнала:
2024,
Номер
57(5)
Опубликована: Апрель 23, 2024
Abstract
This
study
introduces
a
novel
population-based
metaheuristic
algorithm
called
secretary
bird
optimization
(SBOA),
inspired
by
the
survival
behavior
of
birds
in
their
natural
environment.
Survival
for
involves
continuous
hunting
prey
and
evading
pursuit
from
predators.
information
is
crucial
proposing
new
that
utilizes
abilities
to
address
real-world
problems.
The
algorithm's
exploration
phase
simulates
snakes,
while
exploitation
models
escape
During
this
phase,
observe
environment
choose
most
suitable
way
reach
secure
refuge.
These
two
phases
are
iteratively
repeated,
subject
termination
criteria,
find
optimal
solution
problem.
To
validate
performance
SBOA,
experiments
were
conducted
assess
convergence
speed,
behavior,
other
relevant
aspects.
Furthermore,
we
compared
SBOA
with
15
advanced
algorithms
using
CEC-2017
CEC-2022
benchmark
suites.
All
test
results
consistently
demonstrated
outstanding
terms
quality,
stability.
Lastly,
was
employed
tackle
12
constrained
engineering
design
problems
perform
three-dimensional
path
planning
Unmanned
Aerial
Vehicles.
demonstrate
that,
contrasted
optimizers,
proposed
can
better
solutions
at
faster
pace,
showcasing
its
significant
potential
addressing
Artificial Intelligence Review,
Год журнала:
2024,
Номер
57(6)
Опубликована: Май 3, 2024
Abstract
Numerical
optimization,
Unmanned
Aerial
Vehicle
(UAV)
path
planning,
and
engineering
design
problems
are
fundamental
to
the
development
of
artificial
intelligence.
Traditional
methods
show
limitations
in
dealing
with
these
complex
nonlinear
models.
To
address
challenges,
swarm
intelligence
algorithm
is
introduced
as
a
metaheuristic
method
effectively
implemented.
However,
existing
technology
exhibits
drawbacks
such
slow
convergence
speed,
low
precision,
poor
robustness.
In
this
paper,
we
propose
novel
approach
called
Red-billed
Blue
Magpie
Optimizer
(RBMO),
inspired
by
cooperative
efficient
predation
behaviors
red-billed
blue
magpies.
The
mathematical
model
RBMO
was
established
simulating
searching,
chasing,
attacking
prey,
food
storage
magpie.
demonstrate
RBMO’s
performance,
first
conduct
qualitative
analyses
through
behavior
experiments.
Next,
numerical
optimization
capabilities
substantiated
using
CEC2014
(Dim
=
10,
30,
50,
100)
CEC2017
suites,
consistently
achieving
best
Friedman
mean
rank.
UAV
planning
applications
(two-dimensional
three
−
dimensional),
obtains
preferable
solutions,
demonstrating
its
effectiveness
solving
NP-hard
problems.
Additionally,
five
problems,
yields
minimum
cost,
showcasing
advantage
practical
problem-solving.
We
compare
our
experimental
results
categories
widely
recognized
algorithms:
(1)
advanced
variants,
(2)
recently
proposed
algorithms,
(3)
high-performance
optimizers,
including
CEC
winners.
Artificial Intelligence Review,
Год журнала:
2024,
Номер
57(5)
Опубликована: Апрель 24, 2024
Abstract
Crayfish
Optimization
Algorithm
(COA)
is
innovative
and
easy
to
implement,
but
the
crayfish
search
efficiency
decreases
in
later
stage
of
algorithm,
algorithm
fall
into
local
optimum.
To
solve
these
problems,
this
paper
proposes
an
modified
optimization
(MCOA).
Based
on
survival
habits
crayfish,
MCOA
environmental
renewal
mechanism
that
uses
water
quality
factors
guide
seek
a
better
environment.
In
addition,
integrating
learning
strategy
based
ghost
antagonism
enhances
its
ability
evade
optimality.
evaluate
performance
MCOA,
tests
were
performed
using
IEEE
CEC2020
benchmark
function
experiments
conducted
four
constraint
engineering
problems
feature
selection
problems.
For
constrained
improved
by
11.16%,
1.46%,
0.08%
0.24%,
respectively,
compared
with
COA.
average
fitness
value
accuracy
are
55.23%
10.85%,
respectively.
shows
solving
complex
spatial
practical
application
The
combination
environment
updating
significantly
improves
MCOA.
This
discovery
has
important
implications
for
development
field
optimization.
Graphical
